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In order to solve the problem of the lack of prior knowledge in intrusion detection, as an unsupervised learning algorithm, the clustering algorithm is applied to intrusion detection. Aiming at the shortcomings of intrusion detection algorithm based on traditional hierarchical clustering, such as high time complexity and high false positive rate, a new clustering algorithm for intrusion detection...
It is required in the first step of malware analysis to determine whether a given malware program is a variant of known ones. If it is surely not a variant, manual analysis against it is required. However, it is impossible to perform manual analysis, the cost of which is very high, over all the enormous number of newly found malware programs. An automatic and accurate malware program classification...
To defend a network system from security risks, intrusion detection systems (IDSs) have been playing an important role in recent years. There are two types of detection algorithms of IDSs: misuse detection and anomaly detection. Because misuse detection is based on a signature which is created from the features of attack traffic by security experts, it can achieve accurate and stable detection. However,...
Biclustering the gene expressing data is an important task in bioinformatics. A parallel biclustering algorithm for gene expressing data is presented. The algorithm starts from the data sets containing pair of rows and columns of the data matrix, and gets the biclusters by gradually adding columns and rows on the data sets. A pruning technique is also proposed to reduce computing time. Experimental...
An important component of web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. A new clustering algorithm based on user transactions was proposed to provide personalized recommendation service for the websites. As an improvement on K-means algorithm, we got best cluster number and initial clustering centers automatically by competitive...
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