Research Area:  Machine Learning

Abstract:

In the recent years,Denial-of-Service (DoS) orDistributed Denial-of-Service (DDoS) attack has spread greatly and attackers makeonline 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) attacksin IoT.The first methodology useshybrid Intrusion Detection System (IDS) to detect IoT-DoSattack.The second methodology usesdeep learning models,based on Long Short-Term Memory (LSTM) trained with latest dataset for suchkinds of DrDoS. Our experimental results demonstrate that using the proposed methodologies can detectbad behaviour making the IoT network safe of Dos and DDoSattacks.

Keywords:  

Author(s) Name:  Mohammad Shurman, Rami Khrais, andAbdulrahman Yateem

Publisher name:  IAJIT

DOI:  10.34028/iajit/17/4A/10

Volume Information:  Vol. 17, No. 4A, Special Issue 202

Paper Link:   http://iajit.org/PDF/Special%20Issue%202020,%20No.%204A/19490.pdf