Zero Day
Zero-day attacks exploit previously unknown software vulnerabilities, posing a significant threat to various systems, from vehicles and IoT devices to online services. Current research focuses on developing robust detection methods, employing diverse machine learning approaches such as autoencoders, deep belief networks, and federated learning, often combined with techniques like behavioral fingerprinting and graph-based analysis of network traffic. These advancements aim to improve the accuracy and speed of zero-day detection, mitigating the risks associated with these unpredictable threats and enhancing overall cybersecurity.
Papers
AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks
Rabah Rahal, Abdelaziz Amara Korba, Nacira Ghoualmi-Zine, Yacine Challal, Mohamed Yacine Ghamri-Doudane
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks
Abdelaziz Amara korba, Abdelwahab Boualouache, Bouziane Brik, Rabah Rahal, Yacine Ghamri-Doudane, Sidi Mohammed Senouci
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV
Abdelaziz Amara korba, Abdelwahab Boualouache, Yacine Ghamri-Doudane