Treffer: A novel deep spectral self-organizing mapping-based cyber security and attack detection on manet communication system.

A Mobile ad hoc network (MANET) system presents unique challenges for secure communications because of its decentralized architecture, modified topology, and resource limitations. Conventional methods have failed to optimize these security threats in the networks which require novel approaches to detect and mitigate the threats. This work presents a novel approach for cyber security and attack detection in MANETs using Deep Spectral Self-Organizing Mapping (DSSOM) this technique can manage big data combined with the deep learning technique to improve spectral analysis and identify patterns in network traffic. The implemented DSSOM model is specifically designed for self-learning which can map complex patterns of network traffic to detect real-time threats. Also, this system effectively distinguishes between normal and malicious activity such as jamming, identity theft, and man-in-the-middle attacks including spectrum analysis. Deep learning is an evolving continuous process that will improve the accuracy and adaptability of the model's emerging threads. The effectiveness of the DSSOM approach in improving MANET security communications is verified using a simulation environment. The results show that the DSSOM has the most effective solution for secure communication in MANTES based on the error rate and false positive rate detections. These assessments encourage sustained research in the intelligent cyber security system by proposing a scalable, adaptable, and efficient model to respond in the MANET environment. The execution of the DSSOM model is estimated based on several criteria, including detection accuracy, false positive rate, and response time. Simulations are performed in a controlled environment using standard MANET protocols, while real-world testing involves the implementation of the model in a live MANET configuration which makes the DSSOM framework more reliable in communication. [ABSTRACT FROM AUTHOR]