Fuzzytime mine6/25/2023 ![]() The concept of sequence Data Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in the year 1995.Sequential pattern mining is a significant data-mining method for determining time-related behavior in sequence databases. ![]() As the database is reduced, the time taken to mine sequences also reduces and is faster than Apriori based algorithm. Also, as the interestingness of the itemset is increased with the dataset shrinking it leads to efficient sequences with high associativity. At the most, it also scans the database twice. Experimental results show that the modified algorithm shrinks the dataset size. In this paper, a simulation study of both the algorithms, a modified AprioriALL Algorithm to optimize the processing by including set theory techniques and the original AprioriALL algorithm is done on a network intrusion dataset from KDD cup 1999. The major drawback of the Apriori based algorithm is the multiple scans of the database, generating maximal patterns. The first algorithm is based on the characteristic of Apriori and the second uses a pattern growth approach. Sequential Pattern mining algorithms can be broadly classified into Apriori based, Pattern growth based and a combination of both. Sequential Pattern Mining is used to discover the frequent sequential pattern in the event dataset. Intrusion detection using sequential pattern mining is a research topic focusing on the field of information security. Intrusion is a multi step process in which a number of events must occur sequentially in order to launch a successful attack. Analyzing the order in which events occur can improve the attack detection accuracy and reduce false alarms. ![]() Many intrusions aren?t composed by single events, but by a series of attack steps taken in chronological order. How to detect network intrusions effectively becomes an important security technique. Computer Systems are exposed to an increasing number of different types of security threats due to the expanding of internet in recent years.
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