Credit to Author: Ghanshyam More| Date: Wed, 03 Jul 2019 07:18:00 +0000
Ransomware authors keep experimenting with the development of payload in various dimensions. In the timeline of ransomware implementations, we have seen its evolution from a simple screen locker to multi-component model for file encryption, from novice approach to a sophisticated one. The Ransomware as a Tool has evolved in wild…
Read moreCredit to Author: Pankaj Pachlore| Date: Mon, 17 Jun 2019 13:32:01 +0000
After a busy day at office or business, you settle down on the sofa at home with coffee and you want to catch your favourite show on your phone or TV. While streaming you want to make a payment for your bills that are due. You connect to your Wi-Fi…
Read moreCredit to Author: Eric Avena| Date: Thu, 25 Jul 2019 16:30:55 +0000
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.
The post New machine learning model sifts through the good to unearth the bad in evasive malware appeared first on Microsoft Security.
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