Threat Detection And Mitigation In Self-Organizing Wireless Communication Network

Self-organizing networks (SON) have emerged as a promising approach for managing and optimizing the performance of modern wireless networks. However, the dynamic and autonomous nature of SON also introduces security vulnerabilities and risks, making effective threat detection and mitigation crucial for ensuring the integrity and resilience of the network infrastructure. This research aims to investigate advanced techniques for threat detection and mitigation in SON networks. The study focuses on developing intelligent algorithms and mechanisms that can proactively identify and respond to various security threats, such as malware, denial-of-service (DoS) attacks, and intrusion attempts. The research also explores the integration of machine learning and artificial intelligence techniques to enhance the accuracy and efficiency of threat detection in real-time SON environments. Furthermore, the study aims to propose effective countermeasures and mitigation strategies that can be deployed within the SON framework to minimize the impact of security threats and maintain the overall network performance. The outcomes of this research will contribute to enhancing the security posture of self-organizing networks, ensuring the reliable and secure operation of wireless infrastructures in the face of evolving cybersecurity challenges.