New machine learning model sifts through the good to unearth the bad in evasive malware

Most machine learning models are trained on a mix of malicious and clean features. Attackers routinely try to throw these models off balance by stuffing clean features into malware. Monotonic models are resistant against adversarial attacks because they are trained differently: they only look for malicious features. The magic is this: Attackers can’t evade a monotonic model by adding clean features. To evade a monotonic model, an attacker would have to remove malicious features.
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AV-Comparatives: Trend Micro Antivirus for Mac Provides 100% Malware Protection for Mac Users

Despite popular opinion otherwise, the days have long since passed when Mac users can venture forth on the Internet without having to worry about viruses or ransomware, phishing attacks or dangerous URLs. Though the number of attacks on the Mac are fewer than those on Windows machines (because there are fewer Macs, of course, making…
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A Look at Scan4You and the Counter Antivirus Service Landscape

The concept of antivirus (AV) scanning within IT security is simple and effective. These programs, which have become part and parcel of typical infrastructure and data protection strategies, scan enterprise networks for known malware signatures and other processes associated with suspicious hacker activity. If and when these signatures or processes are detected, the antivirus program…
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