01127nas a2200145   4500000000100000000000100001008004100002260002500043100002400068700001500092700001700107245006800124856005900192520073000251       2024                            d  bIEEEaKrakow, Poland1 aMohammed Nasereddin1 aMert Nakip1 aErol Gelenbe00aDeep Learning Intrusion Detection and Mitigation of DoS Attacks  uhttps://ieeexplore.ieee.org/abstract/document/107865603 a<p>Internet of Things (IoT) networks are highly vulnerable to common network DoS and DDoS attacks, which flood limited system resources or IoT devices, overwhelming them with large numbers of attack packets. In order to mitigate such attacks, this paper develops a lightweight yet effective Intrusion Detection and Prevention System (IDPS), that sequentially detects and mitigates the attack via a Deep Random Neural Network (DRNN) and a Drop-Idle-Repeat process. The IDPS is evaluated for UDP Floods, attacks on an experimental test-bed. The results show that UDP Flood attacks can be mitigated with the proposed IDPS, allowing the system to continue routine operations, and resume communications when the attack ends.</p>