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Machine learning and neural networks have become increasingly popularsolutions for encrypted malware traffic detection. They mine and learn complextraffic patterns, enabling detection by fitting boundaries between malwaretraffic and benign traffic. Compared with signature-based methods, they havehigher scalability and flexibility. However, affected by the frequent variantsand updates of malware, current methods suffer from a high false positive rateand do not work well for unknown malware traffic detection. It remains acritical task to achieve effective malware traffic detection. In this paper, weintroduce CBSeq to address the above problems. CBSeq is a method thatconstructs a stable traffic representation, behavior sequence, to characterizeattacking intent and achieve malware traffic detection. We novelly propose thechannels with similar behavior as the detection object and extract side-channelcontent to construct behavior sequence. Unlike benign activities, the behaviorsequences of malware and its variant's traffic exhibit solid internalcorrelations. Moreover, we design the MSFormer, a powerful Transformer-basedmulti-sequence fusion classifier. It captures the internal similarity ofbehavior sequence, thereby distinguishing malware traffic from benign traffic.Our evaluations demonstrate that CBSeq performs effectively in various knownmalware traffic detection and exhibits superior performance in unknown malwaretraffic detection, outperforming state-of-the-art methods.

CBSeq: A Channel-level Behavior Sequence For Encrypted Malware Traffic Detection