AI-Powered Encryption: Innovative Approach for Malware Intrusions in File-Sharing Networks
Keywords:
Malware Intrusions, Encryption, Encryption, Adaptive Emperor Penguin tuned Bayesian Belief Networks (AEP-BBN), Artificial intelligence (AI).Abstract
Malware intrusions in file-sharing networks are a prevalent problem, compromising the integrity and security of encrypted data. These disguised attacks took the utilization of flaws in network designs, spreading intentionally and corrupting data shared by users. The dynamic and distributed organization of file-sharing networks complicates the detection and prevention of such attacks. In this study, we developed an optimized encryption model named Adaptive Emperor Penguin tuned Bayesian Belief Networks (AEP-BBN) for enhancing the prediction of malware activities in file-sharing networks. Initially, we gathered a dataset that includes infected shared files from the organizations to train our proposed prediction model. A robust Scaling (RS) algorithm is employed to pre-process the gathered raw data, to improve the quality of the data. We extracted significant features from the pre-processed data using Recursive Feature Elimination (RFE). Adaptive Emperor Penguin Optimization (AEPO) is used to enhance the primary features of the suggested BBN architecture. The recommended approach has been implemented in Python software. The result assessment phase is performed with numerous metrics such as recall, precision, f1 score and accuracy to evaluate the suggested AEP-BBN approach with other conventional approaches. The outcomes of the experiments demonstrate that the proposed AEP-BBN approach performed better than other existing approaches for enhancing the prediction of malware activities in file-sharing networks.
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