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Cyber Threat Detection and Analysis Using Dual-Layered Approach.

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Titel: Cyber Threat Detection and Analysis Using Dual-Layered Approach.
Autoren: Gudnavar, Anand1 (AUTHOR) anand_gudnavar@yahoo.co.in, Naregal, Keerti2 (AUTHOR), Madagouda, Basavaraj K.3 (AUTHOR)
Quelle: Journal of Computer Information Systems. Sep2025, p1-18. 18p. 12 Illustrations.
Schlagwörter: *COMPUTER network traffic, *ELECTRONIC data processing, ANOMALY detection (Computer security), MACHINE learning, DENIAL of service attacks, GENERATIVE adversarial networks, INTERNET security
Abstract: Integrating anomaly detection with network traffic analysis presents a promising dual-layered approach to enhance cybersecurity defense and improve threat detection. The challenge is to effectively detect and mitigate sophisticated cyber threats by combining anomaly detection and network traffic analysis to enhance overall security. The objective is to develop a robust dual-layered framework that combines anomaly detection and network traffic analysis to improve the accuracy and efficiency of cyber threat detection and response. Data pre-processing for anomaly detection and network traffic analysis involves cleaning, normalizing, feature extraction, and addressing class imbalance to prepare data for machine learning models. Hierarchical Capacity Particle Filtering (HCPF) enhances detection by tracking dynamic network states and identifying complex anomalies like Distributed Denial of Service (DDoS) attacks. Intrusion Detection Systems with Generative Adversarial Networks (IDS-GAN) enhance cybersecurity by detecting anomalies, generating synthetic traffic for training, improving adaptability to new threats, reducing false positives, and providing comprehensive analysis of network patterns for effective threat mitigation. Agglomerative Hierarchical Clustering Algorithm (AHCA) aids in anomaly detection by identifying outliers, analyzing traffic patterns, revealing relationships between data points, enabling multi-level analysis, and enhancing threat identification in network environments. This proposes a dual-layered approach that integrates anomaly detection with network traffic analysis to enhance cybersecurity defense. The result shows that the proposed method achieves the lowest rate at 1.5%, outperforming all other approaches, implemented using Python software. The future scope for integrating anomaly detection and network traffic analysis in a dual-layered approach lies in enhancing real-time threat identification through advanced machine learning models and adaptive algorithms. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
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Abstract:Integrating anomaly detection with network traffic analysis presents a promising dual-layered approach to enhance cybersecurity defense and improve threat detection. The challenge is to effectively detect and mitigate sophisticated cyber threats by combining anomaly detection and network traffic analysis to enhance overall security. The objective is to develop a robust dual-layered framework that combines anomaly detection and network traffic analysis to improve the accuracy and efficiency of cyber threat detection and response. Data pre-processing for anomaly detection and network traffic analysis involves cleaning, normalizing, feature extraction, and addressing class imbalance to prepare data for machine learning models. Hierarchical Capacity Particle Filtering (HCPF) enhances detection by tracking dynamic network states and identifying complex anomalies like Distributed Denial of Service (DDoS) attacks. Intrusion Detection Systems with Generative Adversarial Networks (IDS-GAN) enhance cybersecurity by detecting anomalies, generating synthetic traffic for training, improving adaptability to new threats, reducing false positives, and providing comprehensive analysis of network patterns for effective threat mitigation. Agglomerative Hierarchical Clustering Algorithm (AHCA) aids in anomaly detection by identifying outliers, analyzing traffic patterns, revealing relationships between data points, enabling multi-level analysis, and enhancing threat identification in network environments. This proposes a dual-layered approach that integrates anomaly detection with network traffic analysis to enhance cybersecurity defense. The result shows that the proposed method achieves the lowest rate at 1.5%, outperforming all other approaches, implemented using Python software. The future scope for integrating anomaly detection and network traffic analysis in a dual-layered approach lies in enhancing real-time threat identification through advanced machine learning models and adaptive algorithms. [ABSTRACT FROM AUTHOR]
ISSN:08874417
DOI:10.1080/08874417.2025.2553156