Performance Analysis of LSTM, SVM, CNN, and CNN-LSTM Algorithms for Malware Detection in IoT Dataset

WSEAS TRANSACTIONS ON COMPUTER RESEARCH, Journal Year: 2025, Volume and Issue: 13, P. 288 - 296

Published: April 14, 2025

Machine learning is an effective technique to tackle both the detection and classification tasks of malware. This realized through algorithms that use various distinguishing features characterize Today's malware uses extremely sophisticated techniques, which means techniques combat it are intensively developed. When invisible, can compromise many different data a large number users. Therefore, necessary first analyze types malicious software then propose appropriate countermeasures. In this regard, work aims performance some well-known machine-learning based on neural networks support vector machines, originally developed as method for efficient training networks. For goal SVM, LSTM, CNN, CNN-LSTM analyzed concerning their effectiveness in IoT datasets. all studied, confusion matrices presented along with receiver operating characteristic curves. The best results were obtained using hybrid approach. Its showed accuracy 97% balanced across metrics.

Language: Английский

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A Comparative Study of Privacy-Preserving Techniques in Federated Learning: A Performance and Security Analysis

Information, Journal Year: 2025, Volume and Issue: 16(3), P. 244 - 244

Published: March 18, 2025

Federated learning (FL) is a machine technique where clients exchange only local model updates with central server that combines them to create global after training. While FL offers privacy benefits through training, privacy-preserving strategies are needed since can leak training data information due various attacks. To enhance and attack robustness, techniques like homomorphic encryption (HE), Secure Multi-Party Computation (SMPC), the Private Aggregation of Teacher Ensembles (PATE) be combined FL. Currently, no study has more than two or comparatively analyzed their combinations. We conducted comparative in FL, analyzing performance security. implemented using an artificial neural network (ANN) Malware Dataset from Kaggle for malware detection. privacy, we proposed models combining PATE, SMPC, HE. All were evaluated against poisoning attacks (targeted untargeted), backdoor attack, inversion man middle attack. The maintained while improving robustness. FL_SMPC, FL_CKKS, FL_CKKS_SMPC improved both resistance. outperformed base FL_PATE_CKKS_SMPC achieved lowest success rate (0.0920). best resisted untargeted (0.0010 rate). FL_CKKS defended targeted (0.0020 FL_PATE_SMPC (19.267 MSE). degradation accuracy (1.68%), precision (1.94%), recall F1-score (1.64%).

Language: Английский

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Physical layer security communication for IoT-aided intelligent transport systems: An approach in WFRFT signal domain

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 118, P. 109309 - 109309

Published: May 24, 2024

Language: Английский

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Securing the edge: privacy-preserving federated learning for insider threats in IoT networks

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 81(1)

Published: Dec. 5, 2024

Language: Английский

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A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(6)

Published: Aug. 1, 2024

Language: Английский

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Machine learning-based cyber threat detection: an approach to malware detection and security with explainable AI insights

Human-Intelligent Systems Integration, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Language: Английский

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