Abstract
As the Internet offers increased connectivity between human beings, it has fallen prey to malicious users who exploit its resources to gain illegal access to critical information. In an effort to protect computer networks from external attacks, two common types of Intrusion Detection Systems (IDSs) are often deployed. The first type is signature-based IDSs which can detect intrusions efficiently by scanning network packets and comparing them with human-generated signatures describing previously-observed attacks. The second type is anomaly-based IDSs able to detect new attacks through modeling normal network traffic without the need for a human expert. Despite this advantage, anomaly-based IDSs are limited by a high false-alarm rate and difficulty detecting network attacks attempting to blend in with normal traffic. In this study, we propose a StreamPreDeCon anomaly-based IDS. StreamPreDeCon is an extension of the preference subspace clustering algorithm PreDeCon designed to resolve some of the challenges associated with anomalous packet detection. Using network packets extracted from the first week of the DARPA '99 intrusion detection evaluation dataset combined with Generic Http, Shellcode and CLET attacks, our IDS achieved 94.4% sensitivity and 0.726% false positives in a best case scenario. To measure the overall effectiveness of the IDS, the average sensitivity and false positive rates were calculated for both the maximum sensitivity and the minimum false positive rate. With the maximum sensitivity, the IDS had 80% sensitivity and 9% false positives on average. The IDS also averaged 63% sensitivity with a 0.4% false positive rate when the minimal number of false positives is needed. These rates are an improvement on results found in a previous study as the sensitivity rate in general increased while the false positive rate decreased.
Highlights
Since the explosion of internet usage in the early 1990s, people are able to communicate over larger distances at a faster rate than previously possible
StreamPreDeCon is an extension of the preference subspace clustering algorithm PreDeCon designed to resolve some of the challenges associated with anomalous packet detection
We describe the setup of our evaluation tests for the StreamPreDeCon Intrusion Detection Systems (IDSs)
Summary
Since the explosion of internet usage in the early 1990s, people are able to communicate over larger distances at a faster rate than previously possible. As the number of Internet-capable devices available to consumers increases, new forms of communication are created. This new level of connectivity is often exploited as computer attackers are able to share and distribute malicious programs and ideas effectively allowing inexperienced attackers to create sophisticated viruses and malware. Anomaly-based IDSs can automatically detect new attacks, they generally suffer from a high false positive rate (normal packets being classified as abnormal) and are vulnerable to polymorphic attacks. These attacks try to fool anomaly-based IDSs by making malicious packets appear normal. Because anomalybased IDSs can detect new attacks, several anomalybased IDSs have addressed the high false positive rate while improving detection
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