Optimized Multi-Task Evolutionary Artificial Neural Network Fostered Crypto-Ransomware Prevention in File-Sharing Scenarios with Encrypted Traffic
Corresponding author: Vyom Kulshreshtha
Abstract
This study addresses the substantial threat of ransomware to both home users and enterprises, particularly in corporate settings where shared servers store critical data accessed by users. A novel approach is proposed to prevent crypto-ransomware attacks in File-Sharing Scenarios with Encrypted Traffic (CRP-MTEANN-BWOA-FSET). Ransomware samples are collected from encrypted files shared in the network during infection and from benign activities of office users. Employing Adaptive Self-Guided Filtering (ASGF) for normalization and General Synchroextracting Chirplet Transform (GSCT) for feature extraction, the proposed method extracts critical network traffic features for analysis. Subsequently, MTEANN is utilized to classify ransomware samples as safe or risky, with an emphasis on blocking crypto-ransomware activities in file-sharing scenarios. The proposed method uses the Beluga Whale Optimization Algorithm (BWOA) to optimize the weight parameters in MTEANN, improving the accuracy of ransomware classification. The efficiency of the CRP-MTEANN-BWOA-FSET method was evaluated using accuracy, precision, recall, F-measure, and phi coefficient, and the results showed that it achieved better performance compared to existing methods.
Keywords:
adaptive self-guided filtering, beluga whale optimization, file-sharing, multi-task evolutionary artificial neural network, ransomwareDownloads
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