Intelligent malicious code fragment evidence obtaining method and system

Abstract

The invention belongs to the technical field of digital forensics, and particularly relates to a malicious code fragment intelligent forensics method and system, and the method comprises the steps: constructing a code fragment training set and a code fragment test set for training and testing through extracting the underlying data features of a storage medium; training the set full-connection neural network model by using the data in the code fragment training set, the input being a feature vector, and the output being a normal or malicious prediction result; for the code fragment test set, performing test output by utilizing the trained full-connection neural network model to judge whether model input is a malicious code fragment; and performing feature extraction on the target code snippets, and inputting the target code snippets into a fully-connected neural network model generated through training and testing to obtain an intelligent malicious code recognition result of the targetcode snippets. According to the method, malicious code fragments in storage media such as computers, mobile phones and tablets and evidence containers such as RAW, E01 and AFF can be recognized, and the method has a good application prospect in the field of digital evidence collection such as crime event evidence underlying data automatic analysis.

Description

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AI Technical Summary

Benefits of technology

This technology helps detect harmful software that has been installed on computer systems or other devices without being detected beforehand. It uses advanced techniques like machine learning algorithms to learn from large amounts of stored data about known threat patterns found during an attack. By analyzing these patterns with this algorithm, it becomes possible to predict future attacks based solely upon past behavioral changes made against them.

Problems solved by technology

This patented technical solution describes an algorithm called Deep Learning Thesis used by FBI's National Center for Criminative Analytical Technologies (NCCIT), where they analyze electronic documents containing suspicious codes over time with advanced techniques like image processing or machine learning algorithms. These results help identify potential threats during investigations on terrorist attacks.

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