Search Results - Anomaly detection and rules-based intrusion detection techniques*

Comparative Performance Analysis of Machine Learning Algorithms: Random Cut Forest, Robust Random Cut Forest, and Amazon Sage Maker Random Cut Forest for Intrusion Detection Systems Using the CIS IDS 2017 Dataset.

Authors: Perumal, Senthilkumar Devarajan, Kumaresan

Source: Turkish Journal of Engineering (TUJE); 2025, Vol. 9 Issue 3, p535-543, 9p

Subject Terms: DIGITAL technology, MACHINE learning, COMPUTER network traffic, ANOMALY detection (Computer security), CYBERTERRORISM, INTRUSION detection systems (Computer security)

Adaptive network anomaly detection using machine learning approaches.

Authors: Pai, Vasudeva Pai, Karthik S., Manjunatha et al.

Source: EURASIP Journal on Information Security; 10/21/2025, Vol. 2025 Issue 1, p1-23, 23p

Subject Terms: ANOMALY detection (Computer security), MACHINE learning, SUPPORT vector machines, ENSEMBLE learning, COMPUTER network security, K-nearest neighbor classification, INTRUSION detection systems (Computer security)

Cybersecurity threat detection based on a UEBA framework using Deep Autoencoders.

Authors: Fuentes, Jose Ortega-Fernandez, Ines Villanueva, Nora M. et al.

Source: AIMS Mathematics; 2025, Vol. 10 Issue 10, p1-22, 22p

Subject Terms: ANOMALY detection (Computer security), DEEP learning, ARTIFICIAL neural networks, INTRUSION detection systems (Computer security), ARTIFICIAL intelligence, INTERNET security, DATA analytics

Towards Proactive Cloud Security: A Survey on ML and Deep Learning-Based Classification Techniques on Network Intrusion Systems

Authors: Barhaiya, Mr. Himanshu

Subject Terms: Anomaly Detection, Machine Learning (ML), Deep Learning (DL), Cloud computing, Intrusion Detection Systems (IDS), Proactive Threat Detection, Cloud Security

TBAC-IDS: enhancing intrusion detection with transformer-based alerts correlation.

Authors: Bouguessa, Abdelkader Mostefaoui, Sid Ahmed Mokhtar Daoud, Mohamed Amine et al.

Source: Cluster Computing; Dec2025, Vol. 28 Issue 16, p1-18, 18p

Subject Terms: INTRUSION detection systems (Computer security), TRANSFORMER models, FALSE alarms, ANOMALY detection (Computer security), DEEP learning, INTERNET security

Efficient Anomaly Detection in IoT Network: A Hybrid MHHO-CVTF-Net Approach.

Authors: Jose, Jisha Judith, J. E.

Source: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 8, p607-630, 24p

Subject Terms: INTERNET of things, DEEP learning, INTRUSION detection systems (Computer security), FEATURE selection, INTERNET security, MACHINE learning, ANOMALY detection (Computer security)

ANOMALY DETECTION IN DIGITAL SUBSTATIONS USING SEMI-SUPERVISED LEARNING.

Authors: Ho Trong Tai Yong-Hwa Kim Le Tien Dung

Source: University of Danang - Journal of Science & Technology; 2025, Vol. 23 Issue 9C, p7-11, 5p

Subject Terms: ANOMALY detection (Computer security), INTRUSION detection systems (Computer security), INFORMATION & communication technologies, INTERNET security, SUPERVISED learning, COMPUTER network traffic, ELECTRIC substations, SUPERVISORY control & data acquisition systems

AI-Driven Identification of Attack Precursors: A Machine Learning Approach to Predictive Cybersecurity.

Authors: Abdulwahid, Abdulwahid Al

Source: Computers, Materials & Continua; 2025, Vol. 85 Issue 1, p1751-1777, 27p

Subject Terms: MACHINE learning, ANOMALY detection (Computer security), CYBERTERRORISM, ARTIFICIAL intelligence, REAL-time computing, DEEP learning, INTERNET security, HAZARDS

One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study.

Authors: Paolini, Davide Dini, Pierpaolo Soldaini, Ettore et al.

Source: Computers (2073-431X); Jul2025, Vol. 14 Issue 7, p281, 46p

Subject Terms: ANOMALY detection (Computer security), INTRUSION detection systems (Computer security), OPERATIONS research, INDUSTRIAL applications, OUTLIER detection, SINGLE-board computers

Mitigating Iot Botnets With CNN-LSTM And Anomaly Detection.

Authors: Suryawanshi, Preeti Kailas Jagtap, Sonal Kirankumar

Source: International Journal of Environmental Sciences (2229-7359); 2025 Special Issue, Vol. 11, p1216-1223, 8p

Subject Terms: INTERNET of things, BOTNETS, DEEP learning, CONVOLUTIONAL neural networks, ANOMALY detection (Computer security), LONG short-term memory, COMPUTER network security, INTRUSION detection systems (Computer security)

Digital Twin-Driven Intrusion Detection for Industrial SCADA: A Cyber-Physical Case Study.

Authors: Sayghe, Ali

Source: Sensors (14248220); Aug2025, Vol. 25 Issue 16, p4963, 20p

Subject Terms: DIGITAL twin, INTRUSION detection systems (Computer security), CYBER physical systems, ANOMALY detection (Computer security), WATER treatment plants, MACHINE learning, INDUSTRIAL security, SUPERVISORY control & data acquisition systems

Classification of Cyber Threat Detection Techniques for Next-Generation Cyber Defense via Hesitant Bipolar Fuzzy Frank Information.

Authors: Waqas, Hafiz Muhammad Mahmood, Tahir Emam, Walid et al.

Source: Computers, Materials & Continua; 2025, Vol. 84 Issue 3, p4699-4727, 29p

Subject Terms: INTRUSION detection systems (Computer security), MACHINE learning, ANOMALY detection (Computer security), CYBERTERRORISM, INTERNET security, ARTIFICIAL intelligence, FUZZY logic

Sequential-Attention Based Neural Architecture Integrating BiLSTM and Multi-Head Attention for Dynamic Anomaly Detection in IoT Environments.

Authors: K. S., Renjini Rani Sujit, B. Ben

Source: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 8, p707-728, 22p

Subject Terms: INTERNET of things, ANOMALY detection (Computer security), DEEP learning, REAL-time computing, LONG short-term memory, MACHINE learning, SECURITY management

Generating detectors from anomaly samples via negative selection for network intrusion detection.

Authors: Li, Zhiyong Wei, Xiang Li, Chunyan et al.

Source: Scientific Reports; 10/17/2025, Vol. 15 Issue 1, p1-16, 16p

Subject Terms: ANOMALY detection (Computer security), INTRUSION detection systems (Computer security), CLASSIFICATION algorithms, DETECTORS, MACHINE learning

A comprehensive survey of cybersecurity techniques based on quality of service (QoS) on the Internet of Things (IoT).

Authors: Sefati, Seyed Salar Arasteh, Bahman Halunga, Simona et al.

Source: Cluster Computing; Nov2025, Vol. 28 Issue 12, p1-42, 42p

Subject Terms: QUALITY of service, INTERNET of things, RESOURCE allocation, INTERNET security, INTRUSION detection systems (Computer security), ARTIFICIAL intelligence

A two-layered collaborative approach for network intrusion detection system using blended shallow learning gaussian naïve bayes and support vector machine models.

Authors: Pardeshi, Nilesh Ghanshyam Patil, Dipak Vitthalrao

Source: International Journal of Advances in Intelligent Informatics; Aug2025, Vol. 11 Issue 3, p459-479, 21p

Subject Terms: INTRUSION detection systems (Computer security), MACHINE learning, SUPPORT vector machines, ANOMALY detection (Computer security), NAIVE Bayes classification, PRINCIPAL components analysis

BiTAD: An Interpretable Temporal Anomaly Detector for 5G Networks with TwinLens Explainability.

Authors: Lau, Justin Li Ting Pang, Ying Han Zarakovitis, Charilaos et al.

Source: Future Internet; Nov2025, Vol. 17 Issue 11, p482, 24p

Subject Terms: 5G networks, ANOMALY detection (Computer security), INTRUSION detection systems (Computer security), DEEP learning, INTERPRETATION (Philosophy)

A Comprehensive Survey on Intrusion Detection Systems for Healthcare 5.0: Concepts, Challenges, and Practical Applications.

Authors: Siqueira, Lucas P. Batista, Cassio L. Lui, Pedro H. et al.

Source: Sensors (14248220); Oct2025, Vol. 25 Issue 20, p6261, 36p

Subject Terms: INTRUSION detection systems (Computer security), ARTIFICIAL intelligence, INTERNET security, PATIENT care, ANOMALY detection (Computer security), MEDICAL informatics, INTERNET of medical things

Explainable AI-driven intrusion detection for securing IoT-enabled autonomous transportation systems.

Authors: Akshya, J. Sundarrajan, M. Vijayakumar, R. et al.

Source: Cluster Computing; Nov2025, Vol. 28 Issue 14, p1-31, 31p

Subject Terms: INTERNET of things, AUTONOMOUS vehicles, INTRUSION detection systems (Computer security), RECURRENT neural networks, ANOMALY detection (Computer security), INTERNET security, RANDOM forest algorithms, ARTIFICIAL intelligence

ZDBERTa: Advancing Zero-Day Cyberattack Detection in Internet of Vehicle with Zero-Shot Learning.

Authors: Mirza, Amal Arshad, Sobia Yousaf, Muhammad Haroon et al.

Source: Computers (2073-431X); Oct2025, Vol. 14 Issue 10, p424, 24p

Subject Terms: INTRUSION detection systems (Computer security), GENERATIVE adversarial networks, INTERNET security, ANOMALY detection (Computer security), ENCODING, INTELLIGENT transportation systems, CYBERTERRORISM