DoS and DDos attack detection using deep learning and IDS

• Published in: International arab journal of information technology Vol. 17; no. 4A (s); pp. 655 - 661
• Main Authors: Shurman, Muhammad, Khrais, Rami, Yatim, Abd al-Rahman
• Format: Journal Article
• Language: English
• Published: Zarqa, Jordan Zarqa University, Deanship of Scientific Research 01.01.2020
• ISSN: 1683-3198; 2309-4524; 1683-3198
• DOI: 10.34028/iajit/17/4A/10

In the recent years, Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack has spread greatly and attackers make online systems unavailable to legitimate users by sending huge number of packets to the target system. In this paper, we proposed two methodologies to detect Distributed Reflection Denial of Service (DrDoS) attacks in IoT. The first methodology uses hybrid Intrusion Detection System (IDS) to detect IoT-DoS attack. The second methodology uses deep learning models, based on Long Short-Term Memory (LSTM) trained with latest dataset for such kinds of DrDoS. Our experimental results demonstrate that using the proposed methodologies can detect bad behaviour making the IoT network safe of Dos and DDoS attacks.