GCN-MHSA: A novel malicious traffic detection method based on graph convolutional neural network and multi-head self-attention mechanism

Computers & Security, Journal Year: 2024, Volume and Issue: 147, P. 104083 - 104083

Published: Aug. 30, 2024

Language: Английский

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Transformer-based knowledge distillation for explainable intrusion detection system

Computers & Security, Journal Year: 2025, Volume and Issue: unknown, P. 104417 - 104417

Published: March 1, 2025

Language: Английский

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eBiTCN: Efficient bidirectional temporal convolution network for encrypted malicious network traffic detection

Journal of Computer Security, Journal Year: 2025, Volume and Issue: unknown

Published: April 13, 2025

The growing prevalence of encrypted malicious network traffic poses significant challenges for cybersecurity, as it conceals the content from traditional detection methods. Temporal convolutional networks (TCNs) present promising capabilities extracting complex temporal features and patterns dynamic flow data. However, unidirectional nature TCNs limits their effectiveness in capturing full context traffic, which often exhibits bidirectional dependencies. Consequently, a few studies have proposed TCN (BiTCN) architectures to address limitations. these methods that require amount parameters be learned, imposes high memory requirements on computational resources training such models. In this study, we introduce efficient (eBiTCN) model, an BiTCN requires fewer yet not at expense cost effective detection. eBiTCN framework combines processor, lightweight gating mechanism, attention, dropout, novel loss function, dense layers. Extensive experiments show outperforms eight state-of-the-art competing models terms efficacy, speed, scalability. model showcased robust performance detecting evolving attacks excelled across various real-world datasets. Its efficiency speed reduced usage translates lower infrastructure costs, making accessible choice deployment. These findings highlight eBiTCN’s practicality dependability addressing contemporary security needs.

Language: Английский

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DCM-GIFT: An Android malware dynamic classification method based on gray-scale image and feature-selection tree

Information and Software Technology, Journal Year: 2024, Volume and Issue: 176, P. 107560 - 107560

Published: Aug. 23, 2024

Language: Английский

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DELM: Deep Ensemble Learning Model for Anomaly Detection in Malicious Network Traffic-based Adaptive Feature Aggregation and Network Optimization

ACM Transactions on Privacy and Security, Journal Year: 2024, Volume and Issue: 27(4), P. 1 - 36

Published: Aug. 29, 2024

With the rapid advancements in internet technology, complexity and sophistication of network traffic attacks are increasing, making it challenging for traditional anomaly detection systems to analyze detect malicious attacks. The increasing advancedness cyber threats calls innovative approaches identify patterns within precisely. primary issue lies fact that these do not focus on essential adaptive features traffic. We proposed an effective system called Deep Ensemble Learning Model (DELM). leverage structure Feedforward Neural Network (FDNN), Belief (DBN), incorporating multiple hidden layers with non-linear activation functions. Integrating Adaptive Feature Aggregation (AFA) FDNN algorithm dynamically adjusts feature aggregation process based incoming characteristics improve adaptability. Conditional Generative was employed enhance DELM generating data minority classes. To model’s accuracy, we applied batch normalization augmentation techniques preprocessing, utilized n-gram, one-hot encoding, methods extraction. This study significantly contributes security by enhancing detecting its interpretability adaptability, our model shows promise addressing evolving threat fortifying critical infrastructure. experimental results demonstrate performs higher stability than existing state-of-the-art approaches, as reflected precision, recall, F1-score, AUC-ROC.

Language: Английский

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A New Hash-Based Enhanced Privacy ID Signature Scheme

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 37 - 71

Published: Jan. 1, 2024

Language: Английский

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Malicious Encrypted Traffic Detection Method Based on Spatial-Temporal Features and Cost Sensitivity

Published: May 17, 2024

Language: Английский

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An Accurate And Lightweight Intrusion Detection Model Deployed on Edge Network Devices

2022 International Joint Conference on Neural Networks (IJCNN), Journal Year: 2024, Volume and Issue: 34, P. 1 - 8

Published: June 30, 2024

Language: Английский

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CDDA-MD: An efficient malicious traffic detection method based on concept drift detection and adaptation technique

Computers & Security, Journal Year: 2024, Volume and Issue: unknown, P. 104121 - 104121

Published: Sept. 1, 2024

Language: Английский

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DDP-DAR: Network Intrusion Detection Based on Denoising Diffusion Probabilistic Model and Dual-Attention Residual Network

Neural Networks, Journal Year: 2024, Volume and Issue: 184, P. 107064 - 107064

Published: Dec. 19, 2024

Language: Английский

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