DoS and DDoS Attack Detection Using Deep Learning and
IDS
Mohammad Shurman1, Rami Khrais2,
and Abdulrahman Yateem1
1Jordan University of Science and
Technology, Network Engineering and Security Department, Jordan
2Jordan
University of Science and Technology, Computer Engineering Department, Jordan
Abstract: 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.
Keywords: Deep learning, DoS, DrDoS, IDS, IoT, LSTM.