5/3/2023 0 Comments Byte defense anti malware![]() ![]() As the malware has many symmetric features, the saved training model can detect malicious code with symmetric features. Facing these challenges, this paper proposes a malware detection approach based on convolutional neural network and memory forensics. ![]() For malicious processes in memory, signature-based detection methods are becoming increasingly ineffective. This type of attack is well concealed, and it is difficult to find the malicious code in the static files. ![]() In particular, fileless malware injects malicious code into the physical memory directly without leaving attack traces on disk files. As cyber attacks grow more complex and sophisticated, new types of malware become more dangerous and challenging to detect. ![]()
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