One Article Review
| Source |  CSO |
|---|---|
| Identifiant | 1989308 |
| Date de publication | 2020-10-21 06:53:00 (vue: 2020-10-21 14:05:47) |
| Titre | BrandPost: Strange Behavior: The Case for Machine Learning in Cybersecurity |
| Texte | Many people are skeptical about machine learning claims, and rightfully so. You can't just sprinkle machine learning pixie dust on a product and make it better. You need to first understand the use case. The strongest case for machine learning in cybersecurity is detecting unusual behavior that represents attack activity.Once attackers have breached your defenses, there are many ways for them to evade detection from traditional security tools-not to mention that no one has the time to set up complex SIEM alerts for every possibility. Attackers will mask themselves by using legitimate credentials, delete or modify logs, encrypt or obfuscate their communications, or use sanctioned IT administration tools to move laterally while escaping notice. But one thing that they won't be able to do is escape from an always-on machine learning system that's looking for suspicious behavior on the network. |
| Envoyé | Oui |
| Condensat | able about activity administration alerts always are article attack attackers behavior behavior: better brandpost: breached but can case claims click communications complex credentials cybersecurity defenses delete detecting detection dust encrypt escape escaping evade every first from full has have here just laterally learning legitimate logs looking machine make many mask mention modify move need network not notice obfuscate once one people pixie please possibility product read represents rightfully sanctioned security set siem skeptical sprinkle strange strongest suspicious system that them themselves thing time tools traditional understand unusual use using ways will won your |
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