Research on Feature Extraction Strategies for Cybercrime Crimes Combined with Deep Learning and Their Probabilistic Models

Author:

Lei Yang1,Liao Lingyu1

Affiliation:

1. Investigation Department , Fujian Police College , Fuzhou , Fujian , , China .

Abstract

Abstract In this paper, in order to improve the accuracy and precision of the detection and identification of cybercrime cases, eliminating the incomplete and ambiguous information obtained from a single source of evidence, combined with the procedural steps of the sampling and forensics for the detection of cybercrime cases, a cybercrime detection and control model based on the fuzzy reasoning and the improvement of the D-S algorithm is proposed. Combined with the application of the improved D-S evidence theory in the fusion of cyber evidence, the weighted Bayes is combined to achieve effective forensics of cybercrime evidence. The improved D-S evidence theory algorithm achieves a detection rate of 0.989 for network anomalous data, which has an obvious advantage in network data fusion over the no-preprocessing, original D-S evidence fusion algorithm. The intrusion path generation algorithm considers the intrusion risk of intrusion path IV5 to be the highest at 0.186 with the intrusion paths <H1, W2, S2, S3, W5, S3, T5>.

Publisher

Walter de Gruyter GmbH

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