There are many ways to detect malware traffic. Finding malware traffic accurately and quickly from massive network traffic is one of the keys to defending against cyber attacks. As defenses evolve, threat actors adopt more advanced attack techniques. People have to take various methods to defend against cyber attacks. Even some critical infrastructures have been affected. These cyber attacks have had a tremendous negative impact on public life and brought significant economic losses. In recent years, cyber attacks have become more and more frequent, and national-level threats have often appeared in the public eye. Additionally, M3F is well interpretable and is the network-level representation of the malware’s behavior. It does not mark normal traffic as malicious traffic. The experimental results show that M3F has 99.41% precision and 99.54% recall, both outperforming the baseline methods. We use a large amount of malicious traffic to verify M3F. Armed with M3F, we can locate malware traffic in massive network traffic. Meanwhile, the approximate matching technique is used to deal with the evolution of malware, which can improve the recall rate. M3Fs reflect the dynamics of malware communication traffic. For a malware family, we learn a first-order homogeneous Markov chain using its state sequences as its traffic fingerprint (aka M3F). For each session in a session sequence, we represent it with a state with three features so that a session sequence can be transformed into a state sequence. We associate multiple sessions of different protocols together to form several session sequences. To address these problems, we propose M3F, a Multi-session and Multi-protocol based Malware traffic Fingerprinting that uses multiple related sessions with different protocols as the smallest classification unit. Detecting these sessions alone without contextual information is prone to false negatives and false positives. Usually, malware creates more than one session while executing malicious functions. Traditional detection methods, whether signature-based or machine learning-based, use a packet or a session as the smallest detection unit. Accurately and quickly finding malware traffic from massive network traffic is one of the keys to defending against network attacks. In recent years, cyber attacks have become increasingly frequent, which has had a tremendous negative impact on public life and social order.
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