Published in the El-Cezeri Journal (Scopus Q1)
This article explores innovative AI-supported approaches to enhancing the security of embedded systems. It focuses on the security threats faced by critical infrastructures, smart cities, networks, and factories in an increasingly digitized world. The research analyzes the vulnerability of embedded systems to passive attacks and demonstrates that AI algorithms can detect these threats with high accuracy. Experiments conducted in a test environment created with real systems emphasized the detection of passive attacks. The findings provide valuable insights for future efforts to improve the security of embedded systems.
Published in the Cluster Computing Journal (SCI Q1)
This article introduces innovative AI-based solutions addressing drone security and wireless communication vulnerabilities. The research investigates the weaknesses in wireless connections of commercial drones, such as the DJI Ryze Tello, revealing how these vulnerabilities can be exploited through attacks like DEAUTH ATTACK, Port Scan DOS, DDoS, and MitM. By utilizing AI-enhanced algorithms, the study evaluates the detection rates of these threats and achieves effective results. The data obtained represents a significant step forward in improving the security of drone technology.
