使用机器学习检测加密的恶意网络流量

Michael J. De Lucia, Chase Cotton
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引用次数: 22

摘要

加密网络流量的激增需要一种创新的机器学习流量分析方法,该方法不依赖于模式匹配或数据包的有效载荷内容来检测恶意/可疑通信。互联网流量加密日益成为一种典型的最佳实践,这使得网络数据包内容分析的收益递减。现在,大多数互联网流量都使用被称为传输层安全(TLS)的加密协议进行保护。恶意软件的作者也遵循这一趋势,使用TLS来隐藏恶意的网络通信。我们提出了一种使用支持向量机(SVM)和卷积神经网络(CNN)替代的恶意通信检测机制。两种方法均取得了令人满意的结果和较低的假阳性率(FPR)。然而,SVM方法在所有评价指标上都优于CNN方法。最后,我们提出了未来的工作,将传输层大小和方向作为特征进行实验,并通过使用带有长短期记忆(LSTM)增强的CNN的原始数据包流量来检测恶意流量,从而实现特征工程的自动化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Encrypted Malicious Network Traffic using Machine Learning
The proliferation of encrypted network traffic necessitates an innovative machine learning traffic analysis approach which does not rely on pattern matching or the payload content of the packets to detect malicious / suspicious communications. Encryption of Internet traffic has increasingly become a typical best practice, making network packet content analysis yield diminishing returns. A majority of internet traffic is now protected using the cryptographic protocol known as Transport Layer Security (TLS). Malware authors have also followed this trend with the use of TLS to hide malicious network communications. We propose a malicious communication detection mechanism using a Support Vector Machine (SVM) and an alternative with a Convolutional Neural Network (CNN). Both methods achieve respectable results and a low False Positive Rate (FPR). However, the SVM method outperforms the CNN method in all evaluation metrics presented. Lastly, we propose future work to experiment with transport layer size and direction as features and automate feature engineering by using raw packet traffic with a CNN augmented with a Long Short-term Memory (LSTM) for detection of malicious traffic.
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