论文标题
在各种困境中：在多元化的逃避比赛中赢得国防挑战
Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification
论文作者
论文摘要
基于机器学习的恶意软件检测系统在敌对的环境中运行。因此，对手还将针对学习系统，并使用逃避攻击绕过恶意软件的检测。在本文中，我们概述了基于学习的系统Peberus，该系统获得了Microsoft逃避竞赛辩护人挑战的第一名，从而抵制了独立攻击者的各种攻击。我们的系统结合了多种不同的防御力：我们解决语义差距，使用各种分类模型，并采用状态防御。这场比赛为我们提供了在现实情况下检查逃避攻击的独特机会。它还强调，通过彻底分析攻击表面并实施对抗性学习的概念，可以通过彻底分析攻击来加强现有的机器学习方法。我们的辩护将来可以作为增强安全学习研究的额外基线。
Machine learning-based systems for malware detection operate in a hostile environment. Consequently, adversaries will also target the learning system and use evasion attacks to bypass the detection of malware. In this paper, we outline our learning-based system PEberus that got the first place in the defender challenge of the Microsoft Evasion Competition, resisting a variety of attacks from independent attackers. Our system combines multiple, diverse defenses: we address the semantic gap, use various classification models, and apply a stateful defense. This competition gives us the unique opportunity to examine evasion attacks under a realistic scenario. It also highlights that existing machine learning methods can be hardened against attacks by thoroughly analyzing the attack surface and implementing concepts from adversarial learning. Our defense can serve as an additional baseline in the future to strengthen the research on secure learning.
