Ward off morphing malware and other attacks with machine learning

  • 3 March 2014
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It's getting tougher to ward off blended threats and intrusions as system attacks become more sophisticated. Fight fire with fire and pit compute cycles against compute cycles to achieve the upper hand.

Blended threats, silent intrusions, zero day attacks and morphing malware are common problems for enterprise IT managers. Attacks and compromises are escalating and the technology behind these attacks is become more sophisticated. Nevertheless, those in charge of enterprise IT security are still responsible for mitigating any and all attacks before damage is done, regardless of the circumstances.

Yet, fighting the latest threats has become an almost impossible chore, simply because attackers have turned to the power of the CPU and are creating learning algorithms that can leverage large amounts of data to uncover zero day vulnerabilities. In other words, the signature-based technologies that are in use will be hard pressed to fight the threats of the future.

That situation creates a conundrum for most IT security managers. However, there are some who are thinking ahead of the curve and are leveraging the power of the CPU to fight threats actively and in real time. Those individuals are unlocking the power of machine learning to combat the threats created by attackers leveraging powerful algorithms.

Machine learning as a methodology

Machine learning for computer security has quickly become an established methodology for protecting systems from the threats of today and tomorrow. Case in point is the founding of the Machine Learning and Computer Security Research Institute (MLSEC.ORG), which offers open source algorithms that can be used to detect anomalies and be used against source code to uncover potential vulnerabilities. What’s more, MLSEC is backed by the Computer Security Group at the Institute of Computer Science at the University of Göttingen, in Göttingen, Germany. The institute also offers backgrounders, whitepapers, research and other publications on machine learning algorithms for computer security.

While MLSEC proves to be mostly academic in nature, the institute does offer a viable starting point for IT administrators looking to better grasp the ideologies around machine learning. However, the biggest value comes from the vendors who adopt the ideologies presented and build products for deployment that leverage machine learning technologies. Regrettably, those vendors seem far and few between, amounting only to a few that have implemented some form of artificial intelligence to combat threats.
 
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