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A Machine Learning Model to Detect Malware Variants
For a piece of malware to be able to do its intended malicious activity, it has to be able to sneak inside a machine’s system without being flagged by cybersecurity defenses. It camouflages and packages itself to look like a benign piece of code and, when it has cleared past security filters, unleashes its payload. When malware is difficult to discover — and has limited samples for analysis — we propose a machine learning model that uses adversarial autoencoder and semantic hashing to find what bad actors try to hide. We, along with researchers from the Federation University Australia, discussed this model in our study titled “Generative Malware Outbreak Detection.” Seeing the Stealthy: Obfuscated Malware. Malware authors know that malware is only as good as its ability to remain undetected for it to compromise a device or network. Hence, they use different tools and techniques to keep attacks under the radar. And malware authors have been hard at work at making malware even harder to detect, using various techniques such as sandbox evasion, anti-disassembly, anti-debugging, antivirus evasion, and metamorphism or polymorphism.
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