<![CDATA[Wireless Mesh Networks (WMNs) are susceptible to various security threats, including black hole attacks, where malicious nodes attract and drop packets, disrupting network communication. Traditional security mechanisms are often inadequate in detecting and mitigating these attacks due to their dynamic and evolving nature. In this paper, we propose a novel deep learning-based defense mechanism against black hole attacks in WMNs. It utilizes Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks to analyze network traffic patterns and detect abnormal behavior indicative of black hole attacks. The proposed approach offers several advantages, including the ability to adapt to new attack patterns and achieve high detection accuracy. The evaluations of this method using an NSL KDD demonstrate its effectiveness in mitigating black hole attacks. Results indicate a significant improvement in attack detection rates compared to traditional rule-based systems, reducing both false positives and the overall impact of such attacks on network performance. The proposed solution not only strengthens WMN security but also has the potential to adapt to evolving attack strategies through continuous learning. This research paves the way for future advancements in adversarial learning and autonomous, self-healing security systems for mesh networks. It offers scalable solutions to secure critical infrastructure like smart cities and IoT ecosystems, ensuring reliable communication. Integrating Deep Learning Algorithms security in WMNs enhances resilience against evolving cyber threats in next-generation wireless networks.]]>
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