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CSPI...The Applied Use of Machine Learning Across the Entire Threat Surface

August 13, 2021

In this first article in a three-part series, we look at machine learning and artificial intelligence and show how it can help modern cybersecurity solutions such as ARIA ADR detect and stop cyberattacks. Stay tuned for future articles that highlight specific machine learning and AI use cases.

Cybersecurity solutions that relied on traditional search and static detection processes may have worked for some threats. Yet they are largely ineffective at finding zero-day threats such as malware, ransomware, and sophisticated intrusion methods.

Yet even when threats or actual breaches are identified, most approaches tend to take hours, even days, weeks, months, or even years—far too long to prevent damage from being done. This challenge is made worse by the fact that it takes human analysts to comb through the security alerts and other “noise” to investigate possible threats to determine what may be real. Unfortunately, this is true even for companies with a full complement of specialized cybersecurity teams, systems, and other resources.

For proof, just consider the recent SolarWinds and Microsoft Exchange attacks. Each of these are examples of highly sophisticated zero-day malware attacks that successfully took advantage of vulnerabilities at the perimeter, device, or application-defense level. In other cases, ransomware has also been weaponized and often spreads undetected in an organization such as the recent high-profile case of Colonial Pipeline.

As cyberattackers continue to become more sophisticated and determined to stay one step ahead of the good guys, it’s clear new innovations are needed. It’s reached the point where cybercrime is even an illicit business and source of revenue for hackers, especially when it comes to ransomware. Today, machine learning (ML) and artificial intelligence (AI) are helping leading solutions, such as ARIA ADR provide much better managed threat identification, containment, and remediation.

Machine Learning in Cybersecurity

Machine learning is the perfect application for cybersecurity because it starts by baselining normal device, network, or application behavior and then uses that to establish and determine abnormal behaviors.

Our ARIA ADR solution uses machine learning to take existing threat models and their behavior data and then feeds it into pattern-based detection models to detect the following types of behaviors (and much more)—all without the need for human analysts or users:

Network scans
Unauthorized communication attempts
Unauthorized connections
Abnormal/malicious credential use
Brute force login attempts
Unusual data movement
Data exfiltrations
With this advanced insight, ARIA ADR is able to automatically, and in real time find the attacks that other tools cannot.

The Advantage of AI in Cybersecurity

Artificial Intelligence plays an important role in cybersecurity, too. For example, consider the case of the overwhelming volume of threat alerts cybersecurity teams receive each day--in most cases, more than 5,000 per day. In this case, AI can feed these through powerful threat models to assign severity profiles so that busy security teams can quickly investigate alerts that may present a higher risk than others that are just “noise.” This helps drastically reduce the number of alerts that must be dealt with each day.

Use of artificial intelligence in cybersecurity tools like our ARIA ADR solution really becomes a win-win: Not only do they help find real threats, but they do it much faster than past methods. For example, where human teams may have once required days (or even longer), these AI capabilities can complete the analysis in just a matter of seconds.
ARIA ADR: A modern, ML and AI-based cybersecurity solution

ARIA Cybersecurity Solutions designed the ARIA ADR solution to find, verify, and stop all types of cyber attacks— automatically and in real time. ARIA ADR uses machine learning and AI to detect threats and attacks by their unique, tell-tale behaviors. This approach works because the attackers can’t hide. With over 70 threat behavioral models built in, it covers all types of modern threats and attacks.

Also, since it does not rely on signatures or SIEM-based static rule detection methods, ARIA ADR can detect never-before-seen threats like zero-day attacks and fileless ransomware. ARIA ADR also learns and finds anomalous threat or attack behavior using machine learning to distinguish abnormal from normal device, application, and/or user behaviors.

Additionally, ARIA ADR uses powerful AI security capabilities to correlate the relevant threat indicators to identify, verify the type of threat, identify its targets and assign an overall severity score to prioritize each threat. ARIA ADR’s AI capability helps find real threats and potential attacks from all the background noise and even intentional obfuscation techniques.
https://blog.ariacybersecurity.com/blog/the-applied-use-of-ai-machine-learning-in-cybersecurity