NDLI: Web Server Protection against Application Layer DDoS Attacks Using Machine Learning and Traffic Authentication

Content Provider	IEEE Xplore Digital Library
Author	Ndibwile, J.D. Govardhan, A. Okada, K. Kadobayashi, Y.
Copyright Year	2015
Description	Author affiliation: Grad. Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Nara, Japan (Okada, K.; Kadobayashi, Y.) \|\| Sch. of IT, Jawaharlal Nehru Technol. Univ., Hyderabad, India (Ndibwile, J.D.; Govardhan, A.)
Abstract	Application layer Distributed Denial of Service (DDoS) attacks are among the deadliest kinds of attacks that have significant impact on destination servers and networks due to their ability to be launched with minimal computational resources to cause an effect of high magnitude. Commercial and government Web servers have become the primary target of these kinds of attacks, with the recent mitigation efforts struggling to deaden the problem efficiently. Most application layer DDoS attacks can successfully mimic legitimate traffic without being detected by Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). IDSs and IPSs can also mistake a normal and legitimate activity for a malicious one, producing a False Positive (FP) that affects Web users if it is ignored or dropped. False positives in a large and complex network topology can potentially be dangerous as they may cause IDS/IPS to block the user's benign traffic. Our focus and contributions in this paper are first, to mitigate the undetected malicious traffic mimicking legitimate traffic and developing a special anti-DDoS module for general and specific DDoS tools attacks by using a trained classifier in a random tree machine-learning algorithm. We use labeled datasets to generate rules to incorporate and fine-tune existing IDS/IPS such as Snort. Secondly, we further assist IDS/IPS by processing traffic that is classified as malicious by the IDS/IPS in order to identify FPs and route them to their intended destinations. To achieve this, our approach uses active authentication of traffic source of both legitimate and malicious traffic at the Bait and Decoy server respectively before destined to the Web server.
Starting Page	261
Ending Page	267
File Size	248316
Page Count	7
File Format	PDF
ISSN	07303157
e-ISBN	9781467365642
DOI	10.1109/COMPSAC.2015.240
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-01
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Web servers Computer crime Training Authentication Logic gates IDS/IPS DDoS Mitigation Machine Learning Java Script False Positives
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

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