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Detecting DDoS Attacks on Multiple Network Hosts: Advanced Pattern Detection Method for the Identification of Intelligent Botnet Attacks
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Author(s): Konstantinos F. Xylogiannopoulos (University of Calgary, Canada), Panagiotis Karampelas (Hellenic Air Force Academy, Greece)and Reda Alhajj (University of Calgary, Canada and Global University, Lebanon)
Copyright: 2019
Pages: 19
Source title:
Developments in Information Security and Cybernetic Wars
Source Author(s)/Editor(s): Muhammad Sarfraz (Kuwait University, Kuwait)
DOI: 10.4018/978-1-5225-8304-2.ch006
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Abstract
The proliferation of low security internet of things devices has widened the range of weapons that malevolent users can utilize in order to attack legitimate services in new ways. In the recent years, apart from very large volumetric distributed denial of service attacks, low and slow attacks initiated from intelligent bot networks have been detected to target multiple hosts in a network in a timely fashion. However, even if the attacks seem to be “innocent” at the beginning, they generate huge traffic in the network without practically been detected by the traditional DDoS attack detection methods. In this chapter, an advanced pattern detection method is presented that is able to collect and classify in real time all the incoming traffic and detect a developing slow and low DDoS attack by monitoring the traffic in all the hosts of the network. The experimental analysis on a real dataset provides useful insights about the effectiveness of the method by identifying not only the main source of attack but also secondary sources that produce low traffic, targeting though multiple hosts.
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