Ammar Falih Mahdi

Background: With the continued advance in cyber threats, traditional network security systems offer little returns to organizations. AI has turned out to be a useful technology in improving network security because it proactively identifies and responds to threats in a short time. Objective: This article seeks to discuss the role played by AI self-defending mechanisms in autonomous network security given their effectiveness in threat detection, response time, and the overall harm that can be caused to networks by cyber criminals. Methods: Three separate studies were made, including conventional security systems, and analytically compared them with the AI-driven system across 100 different network environments. Machine learning (ML), deep learning (DL), and other forms of AI were applied to identify and counteract distinct threats like viruses, phishing, and even DDoS attacks. Detecting accuracy, response time and ability to mitigate attacks where among some of the other factors that were examined. Results: Automated threat intelligence systems have a 92% accuracy while legacy systems only have 78%. Mean response time was also decreasing by 65% from 45 seconds to 15 seconds. A significant increase to attack mitigation rates was noted with fifty percent effectiveness of the AI programs averting 85 percent of the threats in the first 30 seconds of identification. Conclusion: Autonomous threat response systems substantiate AI, which function as a radically superior replacement to conventional network security structures, minimizing threat response time and boosting the overall threat neutralization outcome. Incorporation of these types of secure mechanisms into contemporary security landscapes is important as a means of counteraction against new forms of cyber threats.

Background: The growing complexity of telecommunications networks, fueled by advancements like the Internet of Things (IoT) and 5G, necessitates dynamic and real-time network performance monitoring. Traditional static systems often fail to address challenges related to scalability, adaptability, and response speed in high-demand environments. Integrating artificial intelligence (AI) with unmanned aerial vehicles (UAVs) presents a transformative approach to overcoming these limitations. Objective: This study aims to evaluate the effectiveness of AI-driven drones for real-time network performance monitoring, focusing on key metrics such as latency, signal strength, throughput, and anomaly detection. Methods: A comprehensive framework was developed, employing reinforcement learning (RL) for path planning and a hybrid temporal-spectral anomaly detection (HTS-AD) algorithm. Experimental validation was conducted using 10 UAVs across simulated and real-world environments, collecting over 3.2 million data points. Statistical analyses, including MANOVA and Bayesian regression, were used to evaluate performance. Results: The proposed system demonstrated significant improvements over traditional methods, including a 24.6% increase in anomaly detection accuracy, a 30% reduction in energy consumption, and 99.9% network coverage in high-density UAV deployments. Conclusion: AI-driven drones offer a scalable, efficient, and reliable solution for network monitoring. By addressing limitations of traditional systems, this study establishes a foundation for next-generation telecommunications infrastructure. Future research should focus on real-world deployment and hybrid security models.