Mohammed NasereddinMert NakıpErol GelenbeE. Bulut , F. Tschorsch , K. Thilakarathna2025-10-062023979835030073410.1109/LCN58197.2023.10223358https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164680462&doi=10.1109%2FLCN58197.2023.10223358&partnerID=40&md5=ba84a7ea7daa3d56013e5ff868f65383https://gcris.yasar.edu.tr/handle/123456789/8547The IoT is vulnerable to network attacks and Intrusion Detection Systems (IDS) can provide high attack detection accuracy and are easily installed in IoT Servers. However IDS are seldom evaluated in operational conditions which are seriously impaired by attack overload. Thus a Local Area Network testbed is used to evaluate the impact of UDP Flood Attacks on an IoT Server whose first line of defence is an accurate IDS. We show that attacks overload the multi-core Server and paralyze its IDS. Thus a mitigation scheme that detects attacks rapidly and drops packets within milli-seconds after the attack begins is proposed and experimentally evaluated. © 2024 Elsevier B.V. All rights reserved.EnglishCybersecurity, G-networks, Internet Of Things, Intrusion Detection, Local Area Networks, Mitigation, Random Neural Networks, Udp Flood Attacks, Cybersecurity, Floods, Internet Of Things, Local Area Networks, Network Security, Attack Detection, Cyber Security, Flood Attacks, G-networks, Intrusion Detection Systems, Intrusion-detection, Measurement-based, Mitigation, Random Neural Network, Udp Flood Attack, Intrusion DetectionCybersecurity, Floods, Internet of things, Local area networks, Network security, Attack detection, Cyber security, Flood attacks, G-networks, Intrusion Detection Systems, Intrusion-Detection, Measurement-based, Mitigation, Random neural network, UDP flood attack, Intrusion detectionConference Object