Intrusion Detection Using Data Mining Thesis

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The anomaly IDS computes the similarity of the traffic in the system with the profiles to detect intrusions.

The biggest advantage of this model is that new attacks can be identified by the system as it will be a deviation from normal behavior.

Source of data that is another method, which is classified into Host based IDS and Network based IDS.

Misuse IDS Misuse based IDS is a very prominent system and is widely used in industries.

Rules and signatures define abnormal and unsafe behavior.

It analyzes the traffic flow over a network and matches against known signatures.There is a tradeoff in the level of detailed information available versus data volume.We introduce a novel way of characterizing intrusion detection activities: degree of attack guilt.This algorithm employs effective pruning techniques to progressively reduce the transaction database size.DHP utilizes a hashing technique to filter the ineffective candidate frequent 2 item sets.Commercial IDSs are always a combination of the two types mentioned above.Application Applications of intrusion detection by data mining are as follows: (1997) proposed Direct Hashing and Pruning [DHP] algorithm, an effective hash based technique for mining the association rules.A particularly promising approach to anomaly detection combines association raining with other forms of machine learning such as classification.Moreover, the data source that an intrusion detection system employs significantly impacts the types of attacks it can detect.Another algorithm called Matrix Algorithm developed by Yuan and Huang (2005) generates a matrix which entries 1 or 0 by passing over the cruel database only once.The frequent candidate sets are then obtained from the resulting matrix.


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