Evidence obtaining method and system aiming at deep malicious code detection backdoor attack
By using feature vectors and multi-set similarity comparison mechanisms, the problems of low efficiency and low accuracy in deep malware detection are solved, enabling fast and accurate identification of backdoor attack sources and improving the security of deep malware detection systems.
CN121302364APending Publication Date: 2026-01-09JIANGNAN INFORMATION SECURITY (BEIJING) TECH CO LTD
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Patent Information
- Application Number
- CN202511383344.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-26
- Publication Date
- 2026-01-09
AI Technical Summary
Technical Problem
Existing technologies suffer from low efficiency, low accuracy, and poor adaptability to deep malware in deep malware detection, making it difficult to quickly respond to and accurately identify the source of backdoor attacks.
Method used
A feature vector + multi-set similarity comparison mechanism is adopted. By generating feature vectors, calculating similarity, evaluating and probabilities of the data to be used for evidence and the known attack data, and combining the evaluation threshold, evidence judgment is made, thereby improving accuracy and efficiency.
Benefits of technology
It improves the accuracy and efficiency of digital forensics, enables rapid identification of deep malware attack sources, and provides security assurance for deep malware detection systems.
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Figure CN121302364A_ABST
Abstract
The embodiment of the invention discloses an evidence obtaining method and system for detecting back door attack for deep malicious codes, and the method comprises the steps: obtaining an attacked data set of back door attack data to be subjected to evidence obtaining and a known back door attack data set, carrying out the preprocessing, and generating a corresponding feature vector; for the feature vector of the back door attack data to be obtained, performing similarity calculation on the feature vector of the back door attack data to be obtained and the feature vector of attack data belonging to each malicious code category in a known back door attack data set; and according to the similarity value, the evaluation value and the probability value of each malicious code category to which the evidence obtaining backdoor attack data belongs are calculated to determine the backdoor attack data of final evidence obtaining in the evidence obtaining backdoor attack data set, so that the accuracy and the efficiency of digital evidence obtaining are greatly improved, and the safety of a deep malicious code detection system is powerfully ensured.
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