The Dutch police have utilized a deepfakes video to appeal for information on the 2003 murder of a young boy. According to authorities in Rotterdam, it is a “world-first” inquiry using artificially altered video.
What are deepfakes?
A deepfake is a form of artificial intelligence that may be used to generate realistic images, and audio and video hoaxes. The generator and the discriminator are two AI algorithms that are used to create a fake video. The generator, which creates phony multimedia content, asks the discriminator to determine whether the material is genuine or artificial. Deepfakes are made using generative neural network technologies such as autoencoders and generative adversarial networks (GANs) to adjust or create visual and audio content.
Neural networks were previously excellent at classifying existing material (for example, understanding speech or recognizing faces) but not at generating new material. GANs gave neural networks the capacity to not just detect things, but also to create.
The first stage in building a GAN is to choose the intended output and design a training set for the generator. The discriminator may then be fed video clips after the generator has generated an acceptable quantity of output.
Deepfakes are already used for malevolent ends such as the illicit manufacture of sexual material without permission, fraud, and the creation of deceptive content designed to influence public opinion and democratic processes.
However, in Rotterdam, officials have proved that the technology can be used for good. Dutch police developed a deepfake video of 13-year-old Sedar Soares in an attempt to solve his murder after he was murdered in 2003 as a young footballer who was throwing snowballs with his friends at a subway station’s parking lot.
Soares is seen picking up a football in front of the camera and going through a guard of honor on the field that includes his family, friends, and former teachers in this video.
“Somebody must know who murdered my darling brother. That’s why he has been brought back to life for this film,” says someone in the video before Soares pitches his ball.
“Do you know more? Then speak,” his relatives and friends urge. The video then provides the police with contact information. It is hoped that the moving film and a reminder of Soares’ appearance at the time will encourage people to recall him, leading to his case being solved.
Detective Daan Annegarn, a member of the National Investigation Communications Team, stated:
“We know better and better how cold cases can be solved. Science shows that it works to hit witnesses and the perpetrator in the heart—with a personal call to share information. What better way to do that than to let Sedar and his family do the talking? We had to cross a threshold. It is not nothing to ask relatives: ‘Can I bring your loved one to life in a deepfake video? We are convinced that it contributes to the detection, but have not done it before.‘ The family has to fully support it.”
So far, it appears to have worked. The Dutch police claim to have already received several tips, but they must determine whether they are genuine. In the meantime, anyone who has any information is urged to share it with the authorities.
“The deployment of deepfake is not just a lucky shot. We are convinced that it can touch hearts in the criminal environment—that witnesses and perhaps the perpetrator can come forward,” Annegarn explained.
The way we live our lives has an impact on our work. Long lists of typical chores may turn your staff into marketing automatons, reducing their creativity and performance. Fortunately, there are some tools to help. Digital marketing’s first step is to automate and personalize your communications. Multi-channel marketing is a difficult job that requires good campaign management and intelligent automation. Let’s take a closer look at some of the best ones.
Importance of marketing automation tools
How do you figure out that you’ll need marketing automation tools? It’s critical to consider a few things before buying marketing automation software to make sure it’s beneficial:
Is there a marketing team employed in your company? If you answer “no,” your staff would undoubtedly benefit from the use of automation solutions. This is especially true for small firms.
Do you already use a marketing channel? If that’s the case, these automation tools might assist you in obtaining superior results. They allow you to optimize your campaigns quickly and effectively.
Do you have a defined marketing strategy? Marketers may use marketing automation systems to analyze the results of their current approach. Then, you can continue improving and enhancing your campaigns with the usage of facts.
Do you have a budget for the marketing automation tool? You may save money in the long run by utilizing such software.
Almost every organization in all industries may benefit from marketing automation solutions.
You may use many tools to optimize numerous fundamental operations when you increase your productivity. You’ll be much more successful with marketing automation when you use it:
Cross-channel marketing campaigns
Marketing automation use cases
We are in the age of hyperautomation, and marketing, of course, is affected by this. Don’t worry; we’ve already explained what hyperautomation is and how it works. Anyway, the arrival of this new era has opened up several use cases for marketing automation.
The following are some of the most frequent applications of marketing automation:
What are the top marketing automation tools in 2022?
Analytics capabilities are a component of marketing automation solutions, allowing marketers to analyze a campaign’s effectiveness across segments and platforms. These features track the influence of campaigns on marketing department KPIs and campaign ROI, and revenue growth.
Marketing automation solutions frequently connect with CRM software, social media management systems, CMS tools, and account-based orchestration platforms. If you’re unsure of the distinctions, you might want to look at the CRM vs marketing automation comparison. Let’s get started on our best marketing automation tools list if we are ready.
HubSpot Marketing Hub
In terms of features, one of the most useful tools on this list is HubSpot‘s marketing automation platform. It’s also one of the most popular among customers.
Your marketing tools and data are on one simple, powerful platform with Marketing Hub. You’ll save time and have all the information to deliver a tailored experience that attracts and converts the right people in large numbers. Because Marketing Hub is integrated with the HubSpot CRM system, marketers may keep data at the heart of their activities. Marketers can build on this using the full CRM platform to demonstrate ROI and validate expenditures.
Depending on the features and scale, pricing ranges from $50 to $3,200+ per month.
Marketo is one of the most popular marketing automation suites on the market. Adobe acquired Marketo and integrated it into its corporate marketing cloud in recent years.
The Adobe connection gives you a strong business marketing automation and optimization suite if you utilize Adobe Analytics for data measurement and Adobe Target for experimentation and personalization.
The product is meant for larger business customers; it’s a little far above the price range for most small business owners.
Marketo is a reasonable alternative for businesses with 100+ employees who don’t want to spend over $2000 on an automated marketing software but desire a dependable program with viable automation capabilities.
Contact Marketo for pricing information.
Eloqua, which is now part of the Oracle software suite, distinguishes itself by integrating with nearly every other piece of your marketing arsenal. Eloqua has more than 700 integrations, allowing it to make every aspect of your marketing campaign more personalized and efficient. It’s also one of the few all-in-one solutions that specifically serve business-to-business marketers and firms.
Contact Oracle for pricing information.
Customer.io‘s email automation software is designed for subscription-based enterprises and focuses on engaging consumers throughout the customer lifecycle. It has a lot of integrations, allowing you to filter data from across your technology stack and transform it into automated segments and emails that get results.
Customer.io has a monthly fee of $150 to $1,000+ depending on the features and support required.
Constant Contact is a simple marketing tool that allows you to design beautiful and highly converting email communications for your subscribers. It also has tools to assist you in growing your email list.
It’s simple to produce and send personalized emails using the drag-and-drop email builder. You may also utilize their pre-made email templates to write and send emails quickly.
You can trigger emails based on user activity and actions on your website with Constant Contact. You may segment your audience to send the appropriate message at the right time. It also keeps track of non-responsive subscribers and automatically re-delivers messages if necessary.
The cost per month ranges from $20 to $335, depending on the number of features and list size.
Salesforce Account Engagement (Pardot)
Pardot was released in the automation market in 2006 as an optional component of SalesForce. This cloud-based marketing automation software is ideal for large companies since it might take some time to get used to this marketing automation platform.
Pardot provides several essential features to help you improve your marketing efforts, including email automation, CRM integration, lead scoring, lead nurturing, and detailed ROI reporting. It also keeps track of how individuals use your website and delivers strong analytics that can assist you in connecting with them.
Based on the degree of automation and analytics, it costs between $1,250 and $4,000 per month.
The category-defining customer experience automation platform (CXA) from ‘ActiveCampaign‘ empowers companies to interact with their customers meaningfully. The tool gives small and large corporations alike access to 500+ pre-built automations that link email marketing, marketing automation, and CRM for deeper segmentation and customization across social media, email, and more.
850+ integrations, including Microsoft, Shopify, Square, Facebook, and Salesforce.
Depending on the features and number of contacts, it costs anywhere from $9 to $400 or more per month.
Omnisend is an eCommerce marketing automation platform that aims to help you manage your whole customer journey. With pre-built templates designed with online merchants in mind, you can get started swiftly and efficiently with pre-built templates.
You can use Omnisend to connect several channels into the same automation process: email, SMS, Facebook Messenger, push notifications, etc. Omnisend also offers a user-friendly WYSIWYG visual builder and templates that make building workflows, forms, landing pages, popups, and emails simple and quick. If you are specifically searching for workflow automation, we have already explained what is workflow automation best software.
The free plan includes basic email marketing services. Automation costs from $16 per month to bespoke Enterprise pricing, depending on the features and number of contacts involved.
Keap (formerly known as Infusionsoft) is a marketing automation software promoting itself as a small business and enterprise solution. The software’s CRM and automation connections have made it well-known for its marketing campaign management.
Keap has integrated sales and marketing, giving you a comprehensive solution to automate your operations. While it’s beneficial for small companies, it may not be suitable for bigger organizations.
Keap also includes several other features that aid in managing your accounting firm. The system is also quite easy to use, which will make it much easier for you to handle all of your current tasks and new responsibilities, such as invoicing and payments. Furthermore, the automation builder is quite versatile, allowing you to design automation processes that may save you time. Overall, Keap is an excellent choice for SMEs, although it does have a steep learning curve.
The 14-day free trial of Keap allows you to check it out. After that, monthly plans begin at $79 and provide access to basic automation, CRM, and other features. However, if you want more advanced marketing and sales automation, you’ll need the Pro version at $149 per month.
Buffer, the social media management software, is a solution that allows you to plan, collaborate, and publish amazing content while also providing detailed metrics so you may thrive on social networks.
The platform is best known for its suite of products for publishing, engagement, analytics, and team collaboration. It enables teams to work more productively and generate more meaningful social media interaction and results.
Overall, Buffer is a great tool that caters to small businesses and helps them grow their brands while genuinely engaging on social media platforms.
The free version of Buffer is good for beginners since it comes with a free plan that lasts forever. Paid plans start at $15 per month and come with more features and social media platforms.
Autopilot is one of the most visually attractive marketing automation solutions available.
They describe themselves as the simplest marketing automation platform to use, and they provide email marketing, messaging, and automation services. Their visual editor is simple to understand and has a sense of humor about it.
Naturally, their platform is typically utilized for more complex communication and targeting, but you may even make an automated responder out of a time sequence.
Rejoiner is a marketing automation software that is managed. Unlike other systems, in which you must pay a subscription and figure out how to use the tool to expand your business, Rejoiner relieves you of this burden.
The more time you spend on the platform, the simpler it will be to find information and insights. The ease of use is also attributed to their intuitive design, which is particularly well-suited to e-commerce and SaaS firms, where modest enhancements may lead to substantial increases in revenue.
But it’s their à la carte services, such as marketing automation strategy, copywriting, design, and delivery testing that set them apart from other marketing automation solutions.
Rejoiner was one of our research’s top-performing marketing automation tools, with one of the highest customer satisfaction ratings.
Rejoiner has an exorbitant price tag, as you might expect. A typical customer of theirs spends $4,000 to $6,000 per month on their platform and services.
FreshMarketer is a free marketing automation solution powered by industry-leading software provider Freshworks. This platform offers a variety of solutions to help organizations streamline and accelerate their marketing processes, including end-to-end marketing capabilities such as email builder, event tracking, custom reports, and marketplace integrations.
Freshmarketer also has several useful tools to help you improve your marketing efforts. The solution offers behavioral targeting, behavioral segmentation, and customized campaign capabilities that you may employ to target a larger demographic. Freshmarketer allows you to develop your landing pages, accelerate lead creation time, and boost conversion rates.
Freshmarketer’s automation tools allow you to effortlessly manage your CRM procedures across the whole lifecycle, from acquisition to retention of clients, with Freshmarketer’s automation tools. The platform also includes an AI chatbot that may create automated responses at any moment, allowing you to engage with your clients in unique ways.
Best marketing automation tools by size
Different-sized firms with the same problem should have varied answers, and these are the solutions:
Best marketing automation tools for small business
ActiveCampaign is the best marketing automation software for small companies in most situations. This is especially true if you need an all-in-one sales and marketing platform, including a CRM.
However, if you already have a CRM, we suggest adding Autopilot or Rejoiner if money permits since their automation journey builder are unrivaled.
Lastly, if you own an e-commerce firm, Omnisend will demolish both preceding choices.
Best marketing automation tools for medium-sized businesses
ActiveCampaign, Autopilot, or HubSpot are small enterprises’ most recommended marketing automation tools.
HubSpot is a significant investment, so we suggest it’s best suited for agencies or B2B service organizations that can offset the expense by gaining one or two extra clients each year.
If you’re an e-commerce firm, Omnisend (or Rejoiner if money permits) are far superior alternatives to the three above.
Best marketing automation tools for enterprise businesses
For bigger businesses, providing that deliverability, security, support, and training is essential.
Unfortunately, most other business marketing automation systems (e.g., Marketo, Pardot, Eloqua) that satisfy these requirements are simply uninteresting, and their solutions are complex and difficult to use.
One of the minor exceptions is ActiveCampaign, which offers all of the standards, but vital capabilities – such as dedicated IPs and SLAs and SSO and account management – that you would d anticipate from an enterprise client.
The 3 best marketing automation tools free
22% of the most effective marketing campaigns will be automated as we move beyond the pandemic. And businesses are scaling and optimizing to make use of this development. So much so that, according to experts, the global marketing automation software market is expected to reach $15,018.5 billion by 2030.
Remember that free marketing automation services exist if you’re concerned about how much these solutions might cost. Some provide a free, no-obligation trial, while others (like HubSpot) provide a completely free package with all bells and whistles.
These are three of the best free marketing automation tools:
Bonus: Google marketing automation tools
You’ve probably heard of Google, an Internet search engine that’s become increasingly popular. 3.5 billion searches for information are conducted every day on the Internet, right?
Google is much more than a search engine, though. So much more, in fact.
In reality, several Google business tools beyond its search engine can be quite helpful if you’re a marketer. These are some of the best ones:
Last time, we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices. DataRobot assists the modeler in this process by providing tools that are aimed at accelerating and automating critical steps of the model development process—from flagging potential data quality issues to trying out multiple model architectures, these tools not only conform to the expectations laid out by SR 11-7, but also give the modeler a wider tool kit in adopting sophisticated algorithms in the enterprise setting.
In this post, we will dive deeper into how members from both the first and second line of defense within a financial institution can adapt their model validation strategies in the context of modern ML methods. Further, we will discuss how DataRobot is able to help streamline this process, by providing various diagnostic tools aimed at thoroughly evaluating a model’s performance prior to placing it into production.
Validating Machine Learning Models
If we have already built out a model for a business application, how do we ensure that it is working to our expectations? What are some steps that the modeler/validator must take to evaluate the model and ensure that it is a strong fit for its design objectives?
Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses. Effective validation helps ensure that models are sound. It also identifies potential limitations and assumptions, and assesses their possible impact.
SR 11-7 further goes to detail the components of an effective validation, which includes:
Evaluation of conceptual soundness
While SR 11-7 is prescriptive in its guidance, one challenge that validators face today is adapting the guidelines to modern ML methods that have proliferated in the past few years. When the FRB’s guidance was first introduced in 2011, modelers often employed traditional regression-based models for their business needs. These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input. Furthermore, due to their relative simplicity in model structure, these models were very straightforward to interpret. However, with the widespread adoption of modern ML techniques, including gradient-boosted decision trees (GBDTs) and deep learning algorithms, many traditional validation techniques become difficult or impossible to apply. These newer approaches often have the benefit of higher performance compared to regression-based approaches, but come at the cost of added model complexity. To deploy these models into production with confidence, modelers and validators need to adopt new techniques to ensure the validity of the model.
Conceptual Soundness of the Model
Evaluating ML models for their conceptual soundness requires the validator to assess the quality of the model design and ensure it is fit for its business objective. Not only does this include reviewing the assumptions in selecting the input features and data, it also requires analyzing the model’s behavior over a variety of input values. This may be accomplished through a wide variety of tests, to develop a deeper introspection into how the model behaves.
Model explainability is a critical component of understanding a model’s behavior over a spectrum of input values. Traditional statistical models like linear and logistic regression made this process relatively straightforward, as the modeler was able to leverage their domain expertise and directly encode factors relevant to the target they were trying to predict. In the model-fitting procedure, the modeler is then able to measure the impact of each factor against the outcome. In contrast, many modern ML methods may combine data inputs in non-linear ways to produce outputs, making model explainability more difficult, yet necessary prior to productionization. In this context, how does the validator ensure that the data inputs and model behavior matches their expectations?
One approach is to assess the importance of the input variables in the model, and evaluate its impact on the outcome being predicted. Examining these global feature importances enables the validator to understand the top data inputs and ensure that they fit with their domain expertise. Within DataRobot, each model created in the model leaderboard contains a feature impact visualization, which makes use of a mathematical technique called permutation importance to measure variable importance. Permutation importance is model agnostic, making it perfect for modern ML approaches, and it works by measuring the impact of shuffling the values of an input variable against the performance of the model. The more important a variable is, the more negatively the model performance will be impacted by randomizing its values.
As a concrete example, a modeler may be tasked with constructing a probability of default (PD) model. After building the model, the validator in the second line of defense may inspect the feature impact plot shown in Figure 1 below, to examine the most influential variables the model leveraged. As per the output, the two most influential variables were the grade of the loan assigned and the annual income of the applicant. Given the context of the problem, the validator may approve the model construction, as these inputs are context-appropriate.
In addition to examining feature importances, another step a validator may take to review the conceptual soundness of a model is to perform a sensitivity analysis. To directly quote SR 11-7:
Where appropriate to the model, banks should employ sensitivity analysis in model development and validation to check the impact of small changes in input and parameter values on model outputs to make sure they fall within an expected range.
By examining the relationship the model learns between its inputs and outputs, the validator is able to confirm that the model is fit for its design objectives and that the model will yield reasonable outputs across a range of input values. Within DataRobot, the validator may look at the feature effects plot as shown in Figure 2 below, which makes use of a technique called partial dependence to highlight how the outcome of the model changes as a function of the input variable. Drawing from the probability of default model discussed earlier, we can see in the figure that the likelihood of an applicant defaulting on a loan decreases with an increase in their salary. This should make intuitive sense, as individuals with more financial reserves would pose the institution with a lower credit risk compared to those with less.
Lastly, in contrast with the above approaches, a validator may make use of ‘local’ feature explanations to understand the additive contributions of each input variable against the model output. Within DataRobot, the validator may accomplish this by configuring the modeling project to make use of SHAP to produce these prediction explanations. This methodology assists in evaluating the conceptual soundness of a model by ensuring that the model adheres to domain-specific rules when making predictions, especially for modern ML approaches. Furthermore, it can foster trust between model consumers, as they are able to understand the factors driving a particular model outcome.
Outcomes Analysis is a core component of the model validation process, whereby the model’s outputs are compared against actual outcomes observed. These comparisons enable the modeler and validator alike to evaluate the model’s performance, and assess it against the business objectives for which it was created. In the context of machine learning models, many different statistical tests and metrics may be used to quantify the performance of a model, but as quoted by SR 11-7, is wholly dependent upon the model’s technique and intended use:
The precise nature of the comparison depends on the objectives of a model, and might include an assessment of the accuracy of estimates or forecasts, an evaluation of rank-ordering ability, or other appropriate tests.
Out of the box, DataRobot provides a variety of different model performance metrics based on the model architecture used, and further empowers the modeler to do their own analysis by making available all model-related data through its API. For example, in the context of a supervisedbinary classification problem, DataRobot automatically calculates the model’s F1, Precision, and Recall score—performance metrics that capture the model’s ability to accurately identify classes of interest. Furthermore, through its interactive interface, the modeler is able to do multiple what-if analyses to see the impact of changing the prediction threshold on the corresponding model precision and recall. In the context of financial services, these metrics would be especially useful in evaluating the institution’s Anti-Money-Laundering (AML) models, where the model performance can be measured by the number of false positives it generates.
In addition to the model metrics discussed above for classification, DataRobot similarly provides fit metrics for regression models, and helps the modeler visualize the spread of model errors.
While model metrics help to quantify the model’s performance, it is by no means the only way of evaluating the overall quality of the model. To this end, a validator may also make use of a lift chart to see if the model they are reviewing is well calibrated for its objectives. For example, drawing upon the probability of default model discussed earlier in this post, a lift chart would be useful in determining if the model is able to discern between those applicants that pose the highest and least amount of credit risk for the financial institution. In the figure shown below, the predictions made by the model are compared against observed outcomes and rank ordered in increasing deciles based on the predicted value outputted by the model. It is clear in this case that the model is relatively well calibrated, as the actual outcomes observed align themselves closely with the predicted values. In other words, when the model predicts that an applicant is of high risk, we have correspondingly observed a higher rate of defaults (Bin 10 below), whereas we observe a much lower rate of defaults when the model predicts an applicant is at low risk (Bin 1). If, however, we had constructed a model that had a flat blue line for all the ordered deciles, it would have not been fit for its business objective, as the model had no means of discerning those applicants that are of high risk of defaulting versus those that weren’t.
Model validation is a critical component of the model risk management process, in which the proposed model is thoroughly tested to ensure that its design is fit for its objectives. In the context of modern machine learning methods, traditional validation approaches have to be adapted to make certain that the model is both conceptually sound and that its outcomes satisfy the necessary business requirements.
In this post, we covered how DataRobot empowers the modeler and validator to gain a deeper understanding into model behavior by means of global and local feature importances, as well as providing feature effects plots to illustrate the direct relationship between model inputs and outputs. Because these techniques are model agnostic, they may be readily applied to sophisticated techniques employed today, without sacrificing on model explainability. In addition, by providing a host of model performance metrics and lift charts, the validator can be rest assured that the model is able to handle a wide range of data inputs appropriately and satisfy the business requirements for which it was created.
In the next post, we will continue our discussion on model validation by focusing on model monitoring.
About the author
Customer-Facing Data Scientist at DataRobot
Harsh Patel is a Customer-Facing Data Scientist at DataRobot. He leverages the DataRobot platform to drive the adoption of AI and Machine Learning at major enterprises in the United States, with a specific focus within the Financial Services Industry. Prior to DataRobot, Harsh worked in a variety of data-centric roles in both startups and major enterprises, where he had the opportunity to build many data products leveraging machine learning. Harsh studied Physics and Engineering at Cornell University, and in his spare time enjoys traveling and exploring the parks in NYC.
Today we explore the best cyber security monitoring tools in 2022 and why they are important. Finding good cyber security software for business sometimes can be tricky. The term “cyber security threat monitoring” refers to the process of detecting cyber threats and data breaches. IT infrastructure monitoring is an important element of cyber risk management since it allows businesses to detect and respond to cyberattacks before they cause harm and disruption.
Importance of cyber security monitoring tools
The old network perimeter is fading away as the contemporary workplace becomes more cloud-focused and digitalized. Cyber attacks are evolving to take advantage of new weaknesses that appear regularly.
On the other hand, preventive security measures can detect only known signature-based dangers. Cyber security threat monitoring is required to spot more sophisticated threats that go around these safeguards.
Organizations that want to prevent data breaches should implement continuous cyber security monitoring tools. These are some of the benefits:
Find a wider variety of hazards.
Reduce the time it takes to react to attacks
Meet legal and industry standards.
How does cyber security threat monitoring work?
At the network and endpoint levels, cyber security monitoring is possible. But, what are they?
Network security monitoring
Security monitoring systems collect and evaluate security logs from a variety of sources.
Endpoint security monitoring
Endpoint security solutions give cyber-security teams the ability to spot a threat earlier in the kill chain by giving them insight into the host level.
Types of cyber tools
Analysts use a wide range of tools in their work, which may be divided into a few categories:
Network security monitoring tools
Web vulnerability scanning tools
Network intrusion detection
Managed detection services
The topic is the first one we’ll look at. Security monitoring tools are used to analyze network data and discover network threats. Argus, Nagios, Pof, Splunk, and OSSEC are just a few examples of such tools.. So, let’s have a closer look at cyber security monitoring tools.
Best network security monitoring tools in 2022
While it’s fairly simple to secure a system that is 99% secure, achieving 100% security in a diverse network of interconnected components with so many moving parts as our current technology-driven world is impossible. Cybersecurity includes protecting networks from unauthorized access and attacks, defending systems from assaults launched via endpoints, encrypting network communications, and other activities. As a result, detecting vulnerabilities within the IT environment and fixing them before cyber attackers take advantage of them is one of the most effective methods to attain maximum security.
Organizations should be familiar with the many cybersecurity solutions and their various classifications to achieve this. Our list of the best cyber security monitoring tools is listed below.
It is designed for both real-time investigation and data mining. Splunk is a network monitoring software that is both quick and versatile.
Splunk has a user-friendly program with a common design. Splunk’s powerful search capabilities make application monitoring a breeze. Splunk is a paid software, but free versions are available if you don’t want to invest in it.
The free version has certain limitations. This is a great tool to add to the list for those with a budget to work with it. Independent contractors are particular about the premium tools they invest in it. Splunk is well worth the money, and any information security professional with a large enough client base should purchase it.
P0f remains popular even though there have been no updates for nearly a decade. Because it was almost flawless when it was initially released, P0f has barely altered in over a decade. Streamlined and efficient, P0f generates zero additional traffic. It can be used to determine the operating system of any host with which it communicates. Many of these programs generate probes, name lookups, varied queries, and so on. P0f is light and quick to run. It’s a must-have for seasoned users but not the easiest tool to master in a team of novices.
Nagios enables security experts to monitor networks and linked hosts and systems in real-time. The tool generates notifications when a security issue is detected in a network. Users can choose how they want to be notified, however. Network services such as SMTP, NNTP, ICMP, POP3, HTTP, and many others can all be monitored by Nagios.
Acronis Cyber Protect Cloud
The Acronis Cyber Protect Cloud is a one-of-a-kind combination of backup and anti-malware protection and endpoint management tools. This harmony reduces the hassle, allowing service providers to safeguard clients better while lowering expenses.
WebTitan from TitanHQ is a DNS-based web content filter that blocks malware, phishing attempts, and ransomware and provides internet hotspot management for businesses, educational institutions, and public wifi networks.
These are the some of its features:
Blocking badware, c2 callbacks, and phishing websites
Predictive intelligence that automates threat protection by detecting attacks before they happen.
Automated reporting delivered to your inbox.
SolarWinds Security Event Manager
SolarWinds Security Event Manager is a network and host intrusion detection software. It actively monitors, responds, and reports on security dangers in real-time. It offers advanced log search capabilities that are highly indexed. It’s a cloud-based scalable solution with full functionality for 14-day free trial. The cost of the product ranges starts from $4500.
These are the some of its features:
The data will be kept up to date.
It has features for Security Information and Event Manager.
It offers features of Log correlation and Log event archive.
It has a wide range of integrated reporting capabilities.
Syxsense Secure is a cloud-based endpoint protection solution that combines Security Scanning, Patch Management, and Remediation into one console, allowing IT and security personnel to detect breaches using one endpoint security solution. It costs $960 per year for ten devices.
These are the some of its features:
Scan for Vulnerabilities: With information from the security scanner, you can prevent cyber-attacks by checking for authorization problems, security implementation, and antivirus status.
Patch Everything: Automatically deploy OS and third-party patches and Windows 10 Feature Updates, with support for all major operating systems.
Quarantine Devices: Before a computer infection spreads, the cybersecurity software on our network actively monitors all internal traffic to identify and prevent further infections.
Are they expensive? Don’t worry, we prepared free cyber security tools list too.
Best free cyber security tools in 2022
We gathered some of the most used free cyber security monitoring tools in 2022.
OSSEC is a free, open-source cybersecurity solution for detecting intrusions in a network. It allows you to monitor the security events of a system in real-time, and it can provide real-time analytics to users. Users may set it up to monitor all potential unauthorized access or entry sources, including files, processes, logs, rootkits, and registries. OSSEC is quite useful since it may be used on numerous operating systems. Windows (including Vista), Linux (including Ubuntu), and Mac OS X (including Snow Leopard) are just a few examples.
The Argus software is an open-source cybersecurity tool that is commonly used to analyze network traffic. Audit Record Generation and Utilization System is the name of Argus. It’s meant for performing a thorough examination of data transmitted via a network. It has strong analytical tools for analyzing huge quantities of traffic and provides deep, timely reporting.
With the aid of Onion, you can monitor and manage security in your organization. It has enterprise security monitoring and log management tools like ElastiCASH, Logstash, Kibana, Snort, Suricata, Zeek, OSSEC, Wazuh, Sguil, Squirt, NetworkMiner, and others to safeguard your company against cyber attacks.
It’s an all-in-one open source security solution that allows users to spot threats and monitor their systems with miscellaneous tools.
It’s also possible to use Nmap, Network Mapper, for penetration testing and security auditing. It uses NSE scripts to find vulnerabilities, misconfigurations, and security concerns in-network services.
Nmap performs a security audit by first scanning the network and ports before starting, then using the scripts to identify any known security issues. The program collects raw data and subsequently determines a host type, operating system (OS), and all of the hosts in the network.
Network administrators may use Nmap to examine network inventories, service upgrade plans, and outage monitoring.
The open-source security software is available for Linux, Windows, and Mac OS X. It’s specifically made to scan huge networks, but it can also be used to scan single computers.
Best cyber security software companies in 2022
As the demand for secure network protection increases every day, so has the market for cybersecurity technology and the number of options available.
We offer our suggestions for the world’s leading cybersecurity technology providers to help you find your way through this expanding market.
As the demand for secure network protection increases every day, so has the market for cybersecurity technology and the number of options available.
So, we gathered some of the world’s leading cybersecurity technology providers to help you find your way through this expanding market.
Here are the some of the best cyber security software companies in 2022:
Palo Alto Networks
We’ve looked at the top cyber security monitoring tools and recognized their importance. Is there anything else you’d want to add more solutions for it? Please note in the comments and stay safe.
Cyber forensics can be described as the science of crime scene investigation for data devices and services. It is a well-defined and strictly regulated (by law) applied science that collects data as proof of an unlawful activity that involves electronic devices and services, using established investigation standards to capture the culprit by presenting the evidence to the court or the board of directors.
What is cyber forensics?
Cyber forensics, sometimes known as computer forensics, conducts a methodical inquiry and keeps a traceable chain of evidence to identify what occurred on a computing device and who was responsible for it.
Cyber forensics has grown in popularity over the last two decades because computer and portable media devices, such as smartphones, have been increasingly utilized in criminal behavior. As a result, these gadgets are frequently packed with critical evidence, including usernames, phone logs, location data, text messages, emails, images, and recordings. Cyber forensics experts can recover deleted logs such as files, calls, and messages; get audio records of phone conversations, and identify detailed system user actions to present them in a court of law or internal investigations.
The terms digital forensics and cyber forensics are sometimes used interchangeably with computer forensics.
The cyber forensics process simplified
The first stage of digital forensics is collecting digital data in a way that retains its integrity. Next, investigators evaluate the data or system to see if it was altered, how it was modified, and who made the changes. Computer forensics isn’t always used in the context of a crime. The forensic process is also utilized in data recovery procedures to recover data from a malfunctioning server, destroyed drive, reformatted OS, or other cause of system failure.
Why is cyber forensics important?
Integrating technology and forensics allows for more efficient investigations and precise findings. Cyber forensics is a specialized field that aids in collecting critical digital evidence to trace criminals.
Electronic equipment collects a large amount of data that the average person would overlook. For example, smart homes generate data over every word we say; cars know when we hit the brakes. These are very valuable for cyber forensics to present tangible proofs. Many people’s innocence is proven with cyber forensics nowadays.
Cyber forensics is used to solve digital and real-world issues like theft, murder, etc. Businesses profit from cyber forensics by tracking system breaches and identifying the attackers.
How do businesses utilize cyber forensics?
Businesses employ cyber forensics to investigate a system or network breach, which might be used to identify and prosecute cyber attackers. In the event of a system or network failure caused by natural or other disasters, businesses utilize digital forensic specialists and procedures to assist them with data recovery.
How does cyber forensics work?
The first stage of cyber forensics is determining what the evidence is, where it’s being kept, and how it is stored. The next step is to keep the data secure so that no one else can tamper with it.
After collecting the data, the next step is to evaluate it. After obtaining them back, a specialist recuperates the erased files and checks for evidence of a criminal’s attempt to erase secret files. This procedure might require many stages before concluding.
After this, data is collected, and a record is generated. This record contains all of the recovered and available information, which aids in reconstructing the crime scene and reviewing it. The last step involves analyzing the data presented before a court or committee to solve cases.
Cyber forensics techniques
A forensics investigator makes a copy of a compromised device and examines it using a variety of approaches and unique forensic tools. For instance, they look for copies of deleted, encrypted, or damaged files in hidden directories and unallocated disk space. In preparation for legal proceedings that include discovery, depositions, or genuine litigation, any evidence discovered on the digital copy is meticulously recorded in a finding report and verified with the actual device.
A cyber forensics investigation might employ a variety of methods and specialist expertise. One of them is reverse steganography. Steganography is the covert embedding of information within any form of a digital file, communication, or data stream. When analyzing the data hashing in a file to try and find reverse steganography, computer forensics specialists can undo it. Suppose a cybercriminal hides critical information within an image or other digital file. In that case, it may appear the same before and after to the uneducated eye, but the underlying hash or string of data will prove otherwise.
Cyber forensics has been used as evidence by law enforcement agencies and in criminal and civil law since the 1980s
Stochastic forensics is a computer science technique that extracts and analyzes data without using digital artifacts. Digital processes result in unintended changes to data, which are known as artifacts. Clues related to a digital crime, such as modifications to file attributes during data theft, are included in the term artifact. Stochastic forensics is frequently used in data breach investigations to determine the perpetrator’s identity when it’s believed that the intruder is an insider.
The cross-drive analysis technique combines and cross-references data discovered on multiple computer drives to look for, evaluate, and archive data relevant to an inquiry. Events that arouse suspicion are compared with information from other drives to find similarities and provide context. Anomaly detection is another name for this process.
The live analysis approach examines a computer while operating using system tools on the machine. The examination looks at volatile data, usually kept in cache or RAM. To maintain the integrity of a chain of evidence, many instruments for extracting volatile data need the computer to be sent to a forensics lab.
When a file is deleted from a computer system, its information remains in certain areas of the machine’s memory. The deleted file recovery technique involves searching for fragments of files that were partially erased in one location but still leave traces elsewhere on the system. This is also known as file carving or data carving.
Types of cyber forensics
Cyber forensics investigates IT infrastructures, devices, and software to find the clues and evidence it seeks. Using network forensics, investigators monitor and evaluate the criminal’s network traffic. Network intrusion detection systems and other automated tools are used here. In email forensics, experts examine the criminal’s emails and recover deleted email threads, allowing them to extract critical information regarding the case.
Hacking-related offenses are the focus of malware forensics. The malware is examined by a forensic expert, in this case, looking for trojans to figure out who was behind it. Memory forensics is the practice of analyzing data stored in memory (such as cache, RAM, and so on) and extracting information from it.
Mobile forensics is typically focused on mobile devices. This branch examines and analyzes the data from mobile data devices, such as smartphones, tablets, and GPS units. The data recovered from hard drives and cloud platforms by disk forensics are examined and analyzed in detail. Disk forensics extracts data from storage media by searching changed, active, or deleted files.
Cyber forensics prevents trade secret theft
Cyber forensics has been used as evidence by law enforcement agencies and in criminal and civil law since the 1980s. But lately, it has resolved some notable trade secret theft cases.
Apple’s autonomous car division announced the retirement of a software engineer named Xiaolang Zhang, who said he would be returning to China to look after his ailing mother. He informed his superiors he intended to work for an electronic automobile manufacturer in China, which aroused curiosity. According to an FBI statement, Apple’s security staff reviewed Zhang’s activity on the company network and discovered he had taken trade secrets from local company databases to which he had access in the weeks before his resignation. In 2018, he was indicted by the FBI.
In another case, cyber forensics proved a man’s innocence. Anthony Scott Levandowski, formerly an executive of both Uber and Google, was indicted in 2019 with 33 counts of trade secret theft. From 2009 to 2016, he worked for Google’s self-driving car project, where he downloaded thousands of files from a password-protected corporate server. Otto is a self-driving truck startup started by him after he left Google. Uber bought the company In 2016.
Levandowski was arrested in late 2017 and charged with theft of trade secrets as part of the FBI’s widening investigation into Uber. He was indicted by a federal grand jury on October 28, 2018, for one count of trade secrets theft and one count of conspiracy to commit fraud. Levandowski was sentenced to 18 months in prison and $851,499 in fines and restitutionHowever, after a cyber forensics investigation, Levandowski was proven innocent and received a presidential pardon in 2021.
Another famous case that cyber forensics solved was investigating a death, not a trade secret theft. Metadata and medical data from Michael Jackson’s doctor’s iPhone showed that Conrad Murray had given lethal dosages of drugs to the King of Pop, who died in 2009.
Climate change and natural disasters are a concern for both the public sector and commercial organizations. The scale and costs of weather disasters in the U.S. is substantial and growing. From 2018 to 2020, the U.S. experienced 50 independent weather and climate disasters that cost over $1 billion each. In the past three decades, the National Oceanic and Atmospheric Administration (NOAA) estimates that climate and weather disasters have cost the U.S. over $1.875 trillion.
The DataRobot team has proven experience supporting weather and climate applications like identifying clean drinking water, fighting forest fires, and enabling renewable energy companies. The DataRobot AI Cloud Platform can also help identify infrastructure and buildings at risk of damage from natural disasters. In 2017, Hurricane Harvey struck the U.S. Gulf Coast and caused approximately $125 billion in damage. In this blog post, the DataRobot team will demonstrate the potential of the DataRobot AI Cloud Platform to aid in both proactive and reactive disaster response using the wide range of features available on the platform.
DataRobot enables the user to easily combine multiple datasets into a single training dataset for AI modeling. DataRobot also processes nearly every type of data, such as satellite imagery of buildings using DataRobot’s Visual AI, the latitude and longitude of buildings using DataRobot’s Location AI, tweets with geotagged locations using DataRobot’s Text AI, and a variety of other details such as the home price, whether it was previously flooded, when it was built, and elevation. DataRobot combines these datasets and data types into one training dataset used to build models for predicting whether a building will be damaged in the hurricane. In this example, the training dataset only includes information that was known before Hurricane Harvey hit the Gulf Coast to provide proactive predictions about which structures were most vulnerable.
Quickly and Easily Build Models
DataRobot’s AutoML rapidly builds and compares hundreds of models using customized model blueprints. Using either the code-centric DataRobot Core or no-code Graphical User Interface (GUI), both data scientists and non-data scientists such as risk analysts, government experts, or first responders can build, compare, explain, and deploy their own models. In less than a day, DataRobot produced a damage-prediction model that correctly predicted damaged properties 87% of the time and performed especially well at predicting which 30% of homes were most at-risk of damage from Hurricane Harvey. DataRobot’s Explainable AI features like Feature Impact inform the user that the satellite imagery is the most important factor in determining damaged homes for the top-performing model.
Other Disaster Applications for DataRobot
With DataRobot, professionals and organizations impacted by natural disasters can solve an array of difficult predictive analytics questions and rapidly gain value from their data. Some additional DataRobot applications include the following:
Predicting fraudulent insurance claims
Predicting infrastructure resiliency
Predicting electrical grid demand
Predicting demand requirements for critical supplies
Predicting staffing requirements for emergency responders
Predicting outages in communications systems
Predicting most at-risk communities
Contact a member of the DataRobot team to learn more and see how your organization can become AI-driven.
According to the experts, blockchain gaming will be the first genuine use case for blockchain, revolutionizing the sector and making games more immersive. Game platforms have integrated blockchain technology, allowing gamers to collect and trade digital assets that generate a reliable income for game developers while also offering value to players. Blockchain gaming usage and investments grew in the first quarter of 2022, accounting for 52% of all blockchain activity. We’ve already put them up for you if you’re interested in blockchain use cases in 2022.
What is blockchain gaming?
A blockchain game (also known as an NFT game or a crypto game) is a video game that includes features that utilize cryptography-based blockchain technologies. Blockchain features are most often utilized in these games to provide cryptocurrency or non-fungible tokens (NFTs) that players may buy, sell, and trade, earning the game maker a cut from each transaction as a form of revenue. In some instances, blockchain games have also been known as play-to-earn games, where players have earned enough money to cover their living expenses. People who want to get into cryptocurrency investing or have considered it, but don’t know where to start, may benefit from reading the best blockchain books in 2022.
Is blockchain useful to the gaming industry?
In the game sector, blockchain has several advantages. Blockchain might protect data in existing procedures, such as recording winners and losers or the names of those who bet on games. It may also be used to construct decentralized gaming systems where no single entity can manage the gaming system. The gaming industry’s dependency on centralized third-party servers may be cracked by blockchain technology. Read our article if you wonder how blockchain is solving some of the gaming industry’s trickiest problems.
First blockchain-based game: CryptoKitties
The first blockchain-based game was CryptoKitties, released by Axiom Zen in November 2017 for personal computers. Players buy NFTs with Ethereum cryptocurrency and collect virtual pets that they can breed to produce new NFTs with combined characteristics. When one virtual pet sold for more than $100,000 in December 2017, the game created headlines.
CryptoKitties also highlighted Ethereum’s scalability issues when it created a major jam on the network shortly after its debut. Around 30% of all Ethereum transactions were for the game and delays in players’ transactions. Axiom Zen was concerned that, following their release of the game for mobile, particularly given China’s influx of users, Ethereum would have further problems.
How did blockchain gaming become popular?
Blockchain games have been around since 2017, yet they’ve only gained the attention of the video game industry in 2021 when several AAA publishers expressed interest in exploring their potential, and gamers, developers, and companies from the gaming sector criticized them.
According to the recent DappRadar x BGA Games study, blockchain-based game playing increased 2,000 percent from Q1 of 2021, accounting for 52% of all blockchain activity. These are some key takeaways from the report:
In March, blockchain games attracted 1.22 million unique active wallets (UAW), with 22,000 belonging to Axie Infinity despite the $615 million Ronin Bridge hack.
The growth of Ethereum sidechains has aided the increase in popularity of play-to-earn non-fungible token (NFT) games, with sites like Crazy Defense Heroes, Pegaxy, Arc8, and Aavegotchi driving a 219 percent rise in Polygon’s gaming activity since the start of 2022.
Users have flocked to smaller, more stable coins like BSC and Ronin while avoiding risks on the likes of EOS and Tron. Since the end of last year, activity on BSC and Ronin has decreased as users seek to mitigate risk on more volatile chains.
Q1 of 2022 was another good quarter for investors, with $2.5 billion invested across the sector, up 150% from last year’s Q1. Animoca Brands attained significant new funding as it became one of the leading Web 3 brands at a valuation of $5 billion.
With an average of over 650,000 daily UAW in March, Blockchain gaming activity is driven by Splinterlands, Alien Worlds, and Crazy Defense Heroes.
What is Blockchain Gaming Alliance (BGA)?
The Blockchain Game Alliance is a nonprofit organization dedicated to raising awareness of blockchain in the gaming industry.
Their primary objective is to raise awareness about blockchain technologies and urge their adoption by pointing out how they may help establish new ways of developing, publishing, playing, and building strong communities around games.
The BGA also offers a public place for individuals and businesses to share information, collaborate, develop common standards, establish best practices, and network.
Best blockchain games to make money in 2022
Blockchain games are typically made with non-fungible tokens and some gameplay elements. The majority of the characters, cards, creatures, or items used in blockchain games are NFTs because they are unique on the blockchain, belong to a single individual at a time, and can be transferred both within and outside the game. Land and assets created on parcels are NFTs in games with land or other finite-supply dynamics.
Best blockchain games list
Top crypto games (Android)
Ragnarok Labyrinth NFT
Chromatic Souls: AFK Raid
Promising new blockchain games
Desert Farm Game
Blockchain games are frequently referred to as crypto games or play-to-earn games owing to their link with cryptocurrency. A crypto wallet is required for all blockchain games. Most video games have a straight play-to-earn element, allowing gamers to convert in-game effort, activity, or items directly into cryptocurrencies.
Best blockchain gaming coins to buy
The blockchain gaming industry has recently exploded. It’s red hot in 2022, with the number of blockchain games increasing by over 60% last year. Blockchain gaming businesses have raised more than $476 million throughout the first half of 2021, during which interest in play-to-earn cryptocurrency games was intense. Do you want to join the bull run? Here are some of the best blockchain gaming coins according to Coinmarketcap:
The Sandbox (SAND)
Axie Infinity (AXS)
Are gaming crypto coins a good investment?
There are numerous reasons to invest in crypto gaming coins for investors who aren’t sure if they should buy them.
Here are some of the benefits of investing in crypto gaming as a worthwhile venture:
Growth of the Metaverse
In the metaverse, cryptocurrencies have a significant impact. Take Decentraland (MANA), for example. Investors may profit while the metaverse is still in its infancy. The potential of blockchain-based gaming platforms to power virtual reality has never been higher than it is now. Experts believe DEFI, crypto, and blockchain will transform with the metaverse.
Due to the growing popularity of blockchain technology in the gaming industry, crypto gaming platforms are attracting a lot of institutional money. Portion, an NFT auction house and marketplace, JP Morgan, Samsung, and PriceWaterhouseCooper (PWC) are some prominent firms that have invested in Decentraland real estate.
Institutional investors are diving headfirst into the growing decentralized economy, thanks to the hype around NFTs and gaming cryptos.
Players may use the platform’s native cryptocurrencies to purchase in-game items. The blockchain network is powered by each network token, which can be used for transactions and other associated activities.
On a gaming platform, crypto gaming coins are used for various things. For example, the AXS token, which powers the Axie Infinity network, is utilized to vote on network policies. Holders of AXS can also vote on how treasury dollars should be spent.
The community is more involved and democratic as a result of this. Aside from receiving money while playing, token holders have a voice in the project’s future direction, an important advantage.
Biggest blockchain gaming companies
Blockchain games are at all-time highs and gaining in popularity as the year of NFTs draws to a close. So, who is behind them? Here are some of the best blockchain gaming companies:
Why should you try blockchain games?
There is nothing unusual about gamers wanting to monetize their gaming skills and time spent in front of a screen. Blockchain gaming gives developers a lot of possibilities. You may also profit in transparent and fair virtual economies, have real ownership of game assets, and be a stakeholder by contributing to a community-driven ecosystem (DAO) and having your voice heard on game-related choices.
The popularity of decentralized gaming is skyrocketing as more cryptocurrencies join the market, the greater incentive for more players to participate in fairer and more transparent virtual experiences. This was previously impossible in traditional centralized gaming.
What are your thoughts on blockchain games? Is blockchain gaming a hype or the future of gaming? Please leave a comment below.
On any given day, 500,000 passengers and pedestrians, 150,000 privately owned vehicles, and approximately $7.6 billion worth of imported goods cross U.S. borders. Delays at the crossing points along the border are a recurring problem. A limited number of agents, officers, and government professionals conduct operations across more than 300 ports of entry every day, which can experience unexpected surges or declines in traffic volume. Wait times to enter the U.S. from Mexico can exceed 10 hours and cost upwards of $7 billion in economic activity annually.
DataRobot’s AI Cloud Platform can enable effective and secure border transportation by predicting activity at crossing points to support better decisions about staffing levels. This use case can reduce wait times to spur economic trade, as well as ensure enough personnel are on hand to screen for illegal goods and criminal activity. For instance, every day Customs and Border Protection (CBP) arrests an average of 25 wanted criminals at ports of entry and seizes over 4,700 pounds of drugs. Having more agents in the right spot for more effective inspections can increase those seizures and help keep America more safe. AI-enabled staffing can also improve efficiency by predicting periods where activity will be low and allow CBP to reduce staffing to minimal levels without impacting risk.
Department of Transportation Data
The U.S. Department of Transportation (USDOT) Bureau of Transportation Statistics (BTS) provide publicly-available monthly summary statistics for both the U.S.-Canada and U.S.-Mexico borders at the port-of-entry level. The database contains entry data from Mexico to the U.S. for 26 years dating back to 1996. It includes pedestrian, bus, personal vehicle, rail container, train, and truck data. For this example, DataRobot is only predicting truck crossings.
An example of the truck data is shown to the left. This image displays the total truck crossings per port of entry in January 2021. In this example, DataRobot used all 26 years of data to predict unexpected increases or decreases in truck crossings at a specific port of entry for the next month.
DataRobot Time Series Modeling
DataRobot’s Automated Time Series Modeling rapidly builds forecasting models to scale across an organization’s needs. Time series modeling is different from other types of machine learning and requires specialized data handling, preprocessing, and modeling capabilities. Using DataRobot’s built-in automation and no-code user interface, users can easily access the full-spectrum of time-based machine learning techniques. DataRobot automatically identifies the ports of entries as different series in the dataset and treats them independently. DataRobot also automatically handles complicated time series requirements like date and time partitioning while generating explainable predictions and visualizations, which increases model explainability and builds trust with users.
Predicting Border Surges
In this example, the DataRobot team used truck data from the USDOT dataset to forecast the next month’s total truck crossings at each port of entry using the DataRobot AI Cloud Platform. With this information, leaders could modify staffing levels, alter lane openings and closures, and plan major repairs around surges or shortfalls in expected volume, thereby decreasing wait times and increasing trade throughput.
An indicator variable was created in the dataset to account for COVID-19 (known as a “regime change” in data science). For more accurate predictions, truck traffic could be aggregated at a more precise level such as hourly or daily. DataRobot model performance could also be improved by training on organizational-specific data such as border-specific events and historic staffing levels at ports of entry.
A six-month feature derivation window generated the best results for forecasting the truck volumes of the next month. DataRobot enables quick and easy iterations of various backtest configurations to rapidly find the best performing model parameters. DataRobot also took the nine original input features and generated 135 new features during automated Feature Discovery to increase the model performance. Using these new features, DataRobot automatically built 63 models for comparison.
DataRobot quickly produced a multi-series time series forecasting model capable of predicting surges of truck traffic at each port of entry across the southwest border. Performance of the model dropped immediately around the beginning of COVID-19, then rapidly regained accuracy. DataRobot Time Series modeling can be applied to numerous use cases across homeland security organizations including staffing, demand forecasting, supply chain management, predictive maintenance, anomaly detection, and more. Contact a member of the DataRobot team today to see how your organization can become AI-driven.