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Privacy-Preserving Collaborative Anomaly Detection
| Citations: | 6 - 0 self |
BibTeX
@MISC{Ringberg_privacy-preservingcollaborative,
author = {Haakon Andreas Ringberg},
title = {Privacy-Preserving Collaborative Anomaly Detection},
year = {}
}
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Abstract
Unwanted traffic is a major concern in the Internet today. Unwanted traffic includes Denial of Service attacks, worms, and spam. Identifying and mitigating unwanted traffic costs businesses many billions of USD every year. The process of identifying this traffic is called anomaly detection, and Intrusion Detection Systems (IDS’es) are among the most prevalent techniques. IDS’es, such as Snort, allow users to write “rules ” that specify the properties of traffic that should be detected and the corrective action to be taken in response. Unfortunately, applying these rules in an online setting can be prohibitively expensive for large networks, such as Tier-1 ISPs, which may have tens of thousands of links and many Gbps of traffic. In the first chapter of this thesis we present a system that leverages machine learning algorithms to detect the same type of unwanted traffic as Snort, but on summarized data for faster processing. Our results demonstrate that this system can effectively learn to classify many Snort rules with a high degree of accuracy. Unfortunately, distinguishing good traffic from unwanted traffic is challenging even in an offline setting because many types of unwanted traffic traffic, such as
Keyphrases
privacy-preserving collaborative anomaly detection unwanted traffic id e large network prevalent technique first chapter offline setting summarized data unwanted traffic traffic many gbps anomaly detection high degree many snort rule many type online setting tier-1 isps intrusion detection system good traffic major concern service attack corrective action allow user faster processing internet today
