Intrusion Detection Using Data Mining Thesis

Intrusion Detection Using Data Mining Thesis-30
Network Based IDS The source of data for these types of IDS is obtained by listening to all nodes in a network.Attacks from illegitimate user can be identified using a network based IDS.There is a tradeoff in the level of detailed information available versus data volume.

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It is useful for qualifying the degree of confidence associated with detection events, providing a framework in which we analyze detection quality versus cost.

Network security is one of the most important non-functional requirements in a system.

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.

Misuse detection attempts to match known patterns of intrusion, while anomaly detection searches for deviations from normal behavior.

Between the two approaches, only anomaly detection has the ability to detect unknown attacks.Host Based IDS When the source of data for IDS comes from a single host (System), then it is classified as Host based IDS.They are generally used to monitor user activity and useful to track intrusions caused when an authorized user tries to access confidential information.Most of the organizations that develop anti-virus solutions base their design methodology on Misuse IDS.The system is constructed based on the signature of all-known attacks.The need for IDS in a system environment and the generic blocks in IDS is also mentioned. Intrusion detection is primarily focused on identifying possible incidents, logging information about them, and reporting attempts.The examples are as follows: (1) Misuse intrusion detection system that uses state transition analysis approach, (2) Anomaly based system that uses payload modeling and (3) Hybrid model that combines the best practices of Misuse and Anomaly based intrusion systems. In addition, organizations use IDS for other purposes, such as identifying problems with security policies, documenting existing threats, and deterring individuals from violating security policies.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.This demands for constant upgrade and modification of new attack signatures from the vendors and paying more to vendors for their support.s Anomaly IDS Anomaly IDS is built by studying the behavior of the system over a period of time in order to construct activity profiles that represent normal use of the system.

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