Deep Learning Intrusion Detection and Mitigation of DoS Attacks [1]
| Tytuł | Deep Learning Intrusion Detection and Mitigation of DoS Attacks |
| Publication Type | Conference Paper |
| Rok publikacji | 2024 |
| Autorzy | Nasereddin M [2], Nakip M [3], Gelenbe E [4] |
| Conference Name | 32nd International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS24) |
| Publisher | IEEE |
| Conference Location | Krakow, Poland |
| Słowa kluczowe | Deep Random Neural Network [5], DoS attacks [6], Internet of Things (IoT) [7], Intrusion Detection and Mitigation [8] |
| Abstract | 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. |
