监控系统状态+计数自定义指令序列可以帮助检测恶意软件吗?

2019 IEEE 28th Asian Test Symposium (ATS) Pub Date : 2019-12-01 DOI:10.1109/ATS47505.2019.00007

Aditya Rohan, K. Basu, R. Karri

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引用次数: 5

摘要

基于签名和行为的反病毒系统(AVS)传统上用于检测恶意软件。然而，这些AVS无法捕获变形和多态恶意软件，这些恶意软件可以在每一代或每一个实例中自我重构。我们介绍了两种基于系统状态+指令序列的机器学习(ML)方法，它们使用硬件调试数据来检测此类具有挑战性的恶意软件。我们对数百个英特尔恶意软件样本的实验表明，这些技术单独或联合检测恶意软件的准确率≥99.5%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Can Monitoring System State + Counting Custom Instruction Sequences Aid Malware Detection?

Signature and behavior-based anti-virus systems (AVS) are traditionally used to detect Malware. However, these AVS fail to catch metamorphic and polymorphic Malware-which can reconstruct themselves every generation or every instance. We introduce two Machine learning (ML) approaches on system state + instruction sequences – which use hardware debug data – to detect such challenging Malware. Our experiments on hundreds of Intel Malware samples show that the techniques either alone or jointly detect Malware with ≥ 99.5% accuracy.

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2019 IEEE 28th Asian Test Symposium (ATS)

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