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5 Things Security Pros Need To Know About Machine Learning

Experts share best practices for data integrity, pattern recognition and computing power to help enterprises get the most out of machine learning-based technology for cybersecurity.
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#5 Machine Learning Can Provide Pre-Execution Protection Against Malware

Companies employ a variety of techniques to protect their IT infrastructures from malware, such as traditional signature-based detection, sandboxing and now Machine Learning. Machine Learning is one of the techniques used by CrowdStrike and other security vendors to provide pre-execution protection against malware, notes CrowdStrikes Sven Krasser.

For example, a CrowdStrike tool can determine the presence of malicious code without relying on signatures, detecting both known and unknown, or zero-day malware. With machine learning you can create an engine or algorithm that understands what makes something zero day or malicious. As opposed to signatures, machine learning allows analysts to look at all the data coming in on an incident and quickly make decisions immediately upon detection, Krasser says.

Another growing concern for companies are advanced persistent threats (APT) wherein an unauthorized person gains access to a network and stays there undetected for a long period. The intention is to steal data rather than to cause damage to the network or organization.

In dealing with APTs, you need to realize you are up against humans. They only need to get on one machine on the companys network and then move laterally onto others, Krasser says. The risk is in the persistence of the threat, so just detecting the threat is not enough.

Companies must constantly monitor for these types of attacks, so techniques that transcend machine learning need to be applied, although ML can still be part of the defense against APT attacks. CrowdStrike, for example, employs an indicators of attack detection approach for endpoint systems, which focuses on detecting the intent of what an attacker is trying to accomplish, regardless of the malware or exploit used in an attack.

For instance, an attacker might deploy a spear phishing attack, persuading a victim to click on a link or open a document that will infect the machine. Once compromised, the attacker will silently execute another process, hide in memory or on disk and maintain persistence across reboots of the system. The next step is to contact with a command and control site, where the attacker informs his handlers that he awaits further instructions.

IOAs are concerned with the execution of these steps, the intent of the adversary and the outcomes he is trying to achieve. They are not focused on the specific tools the attacker uses to accomplish his objectives. By monitoring these execution points, gathering the indicators and consuming them via a Stateful Execution Inspection engine, analysts can determine how an actor successfully gains access to the network and infer intent.

Protecting an organizations IT infrastructure requires looking at the broader picture beyond just machine learning. That is why enterprises need a cocktail of solutions, Krasser says.

Image Source: CrowdStrike

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User Rank: Author
12/15/2016 | 3:02:27 PM
Insightful, thank you for sharing.
User Rank: Apprentice
12/14/2016 | 10:11:16 AM
Machine Learning - Useful points
First of all, a big thanks for the article. The informations (5 security pros) mentioned in this article very useful. As AI and ML is going to rule future world, we need to consider these security pros.
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