Deep learning for collective anomaly detection
by Mohiuddin Ahmed; Al-Sakib Khan Pathan
International Journal of Computational Science and Engineering (IJCSE), Vol. 21, No. 1, 2020

Abstract: Deep learning has been performing well in a number of application domains. Inspired by its popularity in domains such as image processing, speech recognition, etc., in this paper we explore the effectiveness of deep learning and other supervised learning algorithms for collective anomaly detection. Recently, collective anomaly has become popular for denial of service (DoS) attack detection, however, all these approaches are unsupervised in nature and often have high false alarm rate due to being unsupervised. Therefore, to reduce the false alarm rates, we have experimented using the deep learning method which is supervised in nature. Our experimental results on UNSW-NB15 and KDD Cup 1999 datasets show that the deep learning implemented using H₂O achieves ≈97% recall for collective anomaly detection. Deep learning outperforms a wide range of unsupervised techniques for collective anomaly detection. The key insight of this paper is to report the efficiency of deep learning for collective anomaly detection. To the best of our knowledge, this paper is the first one to address the collective anomaly detection problem using deep learning.

Online publication date: Sat, 22-Feb-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com