DoS and DDos attack detection using deep learning and IDS
Joint Authors
Shurman, Muhammad
Khrais, Rami
Yatim, Abd al-Rahman
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 4A (s) (31 Jul. 2020), pp.655-661, 7 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-07-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Economics & Business Administration
Abstract EN
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.
American Psychological Association (APA)
Shurman, Muhammad& Khrais, Rami& Yatim, Abd al-Rahman. 2020. DoS and DDos attack detection using deep learning and IDS. The International Arab Journal of Information Technology،Vol. 17, no. 4A (s), pp.655-661.
https://search.emarefa.net/detail/BIM-1432357
Modern Language Association (MLA)
Shurman, Muhammad…[et al.]. DoS and DDos attack detection using deep learning and IDS. The International Arab Journal of Information Technology Vol. 17, no. 4A (Special issue) (2020), pp.655-661.
https://search.emarefa.net/detail/BIM-1432357
American Medical Association (AMA)
Shurman, Muhammad& Khrais, Rami& Yatim, Abd al-Rahman. DoS and DDos attack detection using deep learning and IDS. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 4A (s), pp.655-661.
https://search.emarefa.net/detail/BIM-1432357
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 660-661
Record ID
BIM-1432357