Search Results - "Intrusion detection systems (Computer security)"

Cloud-enhanced network security system using explainable AI and lightweight deep learning for multi-threat detection.

Authors: Khan, Farhan Miriyala, Jeevan Reddy Bhola, Asha Sanjaykumar et al.

Source: EPJ Web of Conferences. 6/19/2026, Vol. 373, p1-13. 13p.

Graph-Attentive Cyber–Physical Attack Detection and Forensic Attribution in Smart Grids: A Two-Stage Pipeline Combining Physical Anomaly Detection with Network Traffic Analysis.

Authors: Greco, Danilo Gaggero, Giovanni Battista

Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2394. 23p.

Subject Terms: *Anomaly detection (Computer security), *Intrusion detection systems (Computer security), *Transformer models, *Machine learning, *Cyber physical systems, *Smart power grids, *Computer network traffic, *Digital forensics

Smart Cyber Defence System Using Artificial Intelligence: A Machine-Learning-Based Web Application for Real-Time Network Threat Detection, Classification, and Automated.

Authors: Mishra, Satyam Santoshprasad Kadam, Neha Devidas Raut, Shambhavi Somnath et al.

Source: International Scientific Journal of Engineering & Management. May2026, Vol. 5 Issue 5, p1-5. 5p.

An intelligent intrusion detection approach utilizing kernel-driven deep neural architectures for securing Internet of Things (IoT) infrastructure.

Authors: Sowmya, B. Muthuluru, Nagendra

Source: Information Security Journal: A Global Perspective. 2026, Vol. 35 Issue 4, p573-586. 14p.

An Interpretable Intrusion Detection Approach for IoT Using Graph Attention Networks and Transformer Models with Balanced Learning.

Authors: WILSON, J. DESHPANDE, Abhijit P. PREMKUMAR, M. et al.

Source: Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p1056-1063. 8p.

Subjects: Intrusion detection systems (Computer security), Internet of things, Transformer models, Synthetic data, Artificial intelligence, Feature selection

БАГАТОСИГНАЛЬНІ МЕХАНІЗМИ РАННЬОГО ВИЯВЛЕННЯ ШИФРУВАННЯ ФАЙЛІВ НА ОСНОВІ ФАЙЛІВ-ПРИМАНОК.

Alternate Title: MULTI-SIGNAL MECHANISMS FOR EARLY DETECTION OF FILE ENCRYPTION USING CANARY FILES.

Authors: Яцків, В. В. Яцків, Н. Г. Смірнов, Д. С.

Source: Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì. 2026, Vol. 16 Issue 3, p579-593. 15p.

Subjects: Data encryption, Intrusion detection systems (Computer security), Inventions, Ransomware, Multisensor data fusion

Boosting Intrusion Detection Accuracy With a Multibranch Deep Learning Framework.

Authors: Li, Yang Bansal, Shonak

Source: Journal of Electrical & Computer Engineering. 6/16/2026, Vol. 2026, p1-12. 12p.

Subjects: Intrusion detection systems (Computer security), Deep learning, Feedforward neural networks, Data fusion (Statistics)

Anomaly Detection Based on System Call Sequences with Integrated Attention Mechanism.

Authors: Zhao, An Xu, Shuhua Liu, Zihao et al.

Source: IAENG International Journal of Computer Science. May2026, Vol. 53 Issue 5, p1706-1715. 10p.

Subjects: Anomaly detection (Computer security), Intrusion detection systems (Computer security), Deep learning, Internet security, Artificial neural networks

A survey of privacy-preserving federated learning for intrusion detection systems.

Authors: Bunko, Thomas Johnstone, Michael N. Yang, Wencheng et al.

Source: Artificial Intelligence Review. May2026, Vol. 59 Issue 5, p1-47. 47p.

Subjects: Intrusion detection systems (Computer security), Federated learning, Adversarial machine learning, Internet security, Data encryption, Data security failures, Data privacy

Large‐Scale Benchmarking of Intrusion Detection Datasets With GPU‐Accelerated Data Pipelines, Complexity Analysis, and Model Evaluation.

Authors: Aragão, Marcelo V. C. Figueiredo, Felipe A. P. de Mafra, Samuel B. et al.

Source: International Journal of Intelligent Systems. 5/8/2026, Vol. 2026, p1-30. 30p.

Subjects: Intrusion detection systems (Computer security), Data pipelining, Benchmark problems (Computer science), Computational complexity, Machine learning

A Deep Learning Approach Using Optimized LSTM for Anomaly‐Based Network Intrusion Detection.

Authors: Dardouri, Samia Binwal, Shikha

Source: Journal of Engineering (2314-4912). 5/4/2026, Vol. 2026, p1-13. 13p.

Subjects: Long short-term memory, Intrusion detection systems (Computer security), Deep learning, Feature selection, Internet security, Anomaly detection (Computer security)

An efficient and interpretable intrusion detection framework for software-defined networks with multi-class imbalanced data using genetic and GAN-based optimization.

Authors: Saykat, Md. Tamim Hasan Haque, Md. Ehsanul Farid, Fahmid Al et al.

Source: Scientific Reports. 7/9/2026, Vol. 16 Issue 1, p1-30. 30p.

Mapping the Research Landscape of Smart Metering Infrastructure Security: A Bibliometric Analysis of Trends, Themes, and Future Directions.

Authors: Mathew, Anu Sathyajith Sikora, Axel

Source: Microwave Review; Jul2026, Vol. 32 Issue 1, p34-43, 10p

A dual-branch temporal-spatial deep learning architecture for efficient and accurate CAN bus intrusion detection.

Authors: Zhang, Guohong¹ Guohongzhang11@outlook.com

Source: Kuwait Journal of Science. Jul2026, Vol. 53 Issue 3, p1-16. 16p.

Impact of Network Topology on Machine Learning-Based DDoS and Anomaly Detection in Software-Defined Networks.

Authors: Bakuła, Łukasz Jasinski, Andrzej

Source: Applied Sciences (2076-3417); Jun2026, Vol. 16 Issue 12, p6204, 25p

Hybrid Intrusion Detection System for Software-Defined Networks.

Authors: Łapczuk, Aleksandra Domżał, Jerzy Biernacka, Edyta et al.

Source: Applied Sciences (2076-3417); Jun2026, Vol. 16 Issue 12, p6122, 24p

Transformer–DDQN-Based Explainable and Active Intrusion Detection Architecture for Network Traffic Analysis.

Authors: Okutan Kara, Ayşe Boyacı, Aytuğ

Source: Applied Sciences (2076-3417); Jun2026, Vol. 16 Issue 12, p5912, 40p

Dynamics of botnet propagation model in complex networks considering hybrid method for botnet detection.

Authors: Maazalahi, Mahdieh Hosseini, Soodeh

Source: PLoS ONE. 6/9/2026, Vol. 21 Issue 6, p1-40. 40p.

DICYME: Dynamic Industrial Cyber Risk Modelling Based on Evidence.

Authors: García-Ochoa, Javier Rueda, Jaime Fernández, Rubén R. et al.

Source: Computer Science & Information Systems; Jun2026, Vol. 23 Issue 3, p1001-1026, 26p

Cosine Distance-Based Fuzzy C-Means Clustering for Local Classification in Imbalanced Network Intrusion Detection.

Authors: Thi, Ngoc-Bich Giap Nguyen, Van-Nhan Pham, Anh-Thu et al.

Source: International Journal of Computers, Communications & Control; Jun2026, Vol. 21 Issue 3, p1-13, 13p