This paper discusses anomaly intrusion detection using data mining and statistics.
Written in 2007; 800 words; 4 sources; MLA; $ 28.95
Paper Summary:
This paper is a research project, which uses anomaly intrusion detection to determine if there are any abnormal patterns and, hence, intrusions in the provided log files. The author stresses that a statistics approach seems to be the easiest and most straightforward approach. The paper relates that a common practice in IDS software is to incorporate different techniques to detect intrusion so that other methods such as hierarchical clustering can still be included in the system to search for suspicious/ known data patterns such as viruses. The paper includes charts, graphs and a screen-shot.
From the Paper:
"Since we are not building a new system, we will try to implement and base the report on existing work. Viewing sequence algorithms for intrusion detection helps to determine which patterns look like patterns of intrusion. The statistics technique is discussed but will not be programmed at this current time. We will also attempt to show manually how this algorithm will detect the patterns using previous research as it correlates to this specific data using logs provided and some data mining algorithm."
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