• Dr. Venkata Naga Rani Bandaru
• Nukaraju Neradabil
• Sangepu Tejaswini
• Thota Mery Sowmya
• Peeka kiran Kumar

Nowadays, Challenges arise in intrusion detection in Reconfigurable Wireless Networks (RWNs), as these networks consist of dynamic topologies, support diverse communication protocols, and involve sophisticated cyber threats or routing attacks. The performance of traditional rule-based systems can even be dubious when it comes to detecting and adequately responding to such evolving attacks. For this reason, this project proposes a strong DL-based intrusion detection system capable of performing both binary classification to decide the presence of an attack and multi-level classification to decide which threat it is, such as DoS attacks, spoofing, unauthorized access, and many others. This DL-IDS uses advanced neural network architectures like CNNs, combined with machine learning models, that enhance the accuracy of the detection and provide a comprehensive security framework. These models are designed to learn the complex attack patterns and dynamic behaviors of the network. Optimized for deployment in resource-constrained environments typical of RWNs, the system not only enhances security but also promptly alerts network administrators to detected threats. This ensures timely precautions and mitigations to protectcommunication systems from cyber threats, maintaining the integrity and reliability of next-gen wireless networks.

Cyber Threat Detection, Reconfigurable Wireless Networks (RWNs), Unauthorized Access, Routing attacks

" Deep Learning for Intrusion Detection in Reconfigurable Wireless Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.d172-d182, March-2025, Available :http://www.jetir.org/papers/JETIR2503321.pdf

" Deep Learning for Intrusion Detection in Reconfigurable Wireless Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppd172-d182, March-2025, Available at : http://www.jetir.org/papers/JETIR2503321.pdf