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AI quickly cooks malware that AV software can't spot


DEF CON Machine-learning tools can create custom malware that defeats antivirus software. In a keynote demonstration at the DEF CON hacking convention Hyrum Anderson, technical director of data science at security shop Endgame, showed off research that his company had done in adapting Elon Musk's OpenAI framework to the task of creating malware that security engines can't spot. The system basically learns how to tweak malicious binaries so that they can slip past antivirus tools and continue to work once unpacked and executed. Changing small sequences of bytes can fool AV engines, even ones that are also powered by artificial intelligence, he said. Anderson cited research by Google and others to show how changing just a few pixels in an image can cause classification software to mistake a bus for an ostrich.

6 ways hackers will use machine learning to launch attacks


Defined as the "ability for (computers) to learn without being explicitly programmed," machine learning is huge news for the information security industry. It's a technology that potentially can help security analysts with everything from malware and log analysis to possibly identifying and closing vulnerabilities earlier. Perhaps too, it could improve endpoint security, automate repetitive tasks, and even reduce the likelihood of attacks resulting in data exfiltration.

On labeling Android malware signatures using minhashing and further classification with Structural Equation Models Machine Learning

Multi-scanner Antivirus systems provide insightful information on the nature of a suspect application; however there is often a lack of consensus and consistency between different Anti-Virus engines. In this article, we analyze more than 250 thousand malware signatures generated by 61 different Anti-Virus engines after analyzing 82 thousand different Android malware applications. We identify 41 different malware classes grouped into three major categories, namely Adware, Harmful Threats and Unknown or Generic signatures. We further investigate the relationships between such 41 classes using community detection algorithms from graph theory to identify similarities between them; and we finally propose a Structure Equation Model to identify which Anti-Virus engines are more powerful at detecting each macro-category. As an application, we show how such models can help in identifying whether Unknown malware applications are more likely to be of Harmful or Adware type.

MaMi malware targets Mac OS X DNS settings


A researcher has discovered a strain of malware in the wild which targets Mac OS X users.

3 questions to ask about machine learning in cybersecurity


The following is a guest article from Dr. Sven Krasser, chief scientist at CrowdStrike. Without a doubt, machine learning is one of the hottest topics in cybersecurity at the moment, and most vendors boast their newest machine learning additions as the panacea that liberates you from all security woes. Machine learning allows security products to do vastly better in various areas. However, it is best understood as a set of techniques that dramatically optimize detection techniques. It does not allow sidestepping inherent limitations e.g.