Abstract-Today, information collectors, particularly statistical organizations, are faced with two conflicting issues. On one hand, according to their natural responsibilities and the increasing demand for the collected data, they are committed to propagate the information more extensively and with higher quality and on the other hand, due to the public concern about the privacy of personal information and the legal responsibility of these organizations in protecting the private information of their users, they should guarantee that while providing all the information to the population, the privacy is reasonably preserved. This issue becomes more crucial when the datasets published by data mining methods are at risk of attribute and identity disclosure attacks. In order to overcome this problem, several approaches, called p-sensitive k-anonymity, p+-sensitive k-anonymity, and (p, α)-sensitive k-anonymity, were proposed. The drawbacks of these methods include the inability to protect micro datasets against attribute disclosure and the high value of the distortion ratio. In order to eliminate these drawbacks, this paper proposes an algorithm that fully protects the propagated micro data against identity and attribute disclosure and significantly reduces the distortion ratio during the anonymity process.

Abstract-Alert correlation is a process that analyzes the alerts produced by one or more intrusion detection systems and provides a more succinct and high-level view of occurring or attempted intrusions. Several alert correlation systems use pairwise alert correlation in which each new alert is checked with a number of previously received alerts to find its possible correlations with them. An alert selection policy defines the way in which this checking is done. There are different alert selection policies such as select all, window-based random selection and random directed selection. The most important drawback of all these policies is their high computational costs. In this paper a new selection policy which is named Enhanced Random Directed Time Window (ERDTW) is introduced. It uses a limited time window with a number of sliding time slots, and selects alerts from this time window for checking with current alert. ERDTW classifies time slots to Relevant and Irrelevant slots based on the information gathered during previous correlations. More alerts are selected randomly from relevant slots, and less or no alerts are selected from irrelevant slots. ERDTW is evaluated by using DARPA2000 and netforensicshoneynet data. The results are compared with other selection policies. For LLDoS1.0 and LLDoS2.0 execution times are decreased 60 and 50 percent respectively in comparing with select all policy. While the completeness, soundness and false correlation rate for ERDTW are comparable with other more time consuming policies. For larger datasets like netforensicshoneynet, performance improvement is more considerable while the accuracy is the same.

Malware poses one of the most serious threats to computer information systems. The current detection technology of malware has several inherent constraints. Because signature-based traditional techniques embedded in commercial antiviruses are not capable of detecting new and obfuscated malware, machine learning algorithms are applied in identifing patterns of malware behavior through features extracted from programs. There, a method is presented for detecting malware based on the features extracted from the PE header and section table PE files. The packed files are detected and then unpacke them. The PE file features are extracted and their static features are selected from PE header and section tables through forward selection method. The files are classified into malware files and clean files throughs different classification methods. The best results are obtained through DT classifier with an accuracy of 98.26%. The results of the experiments consist of 971 executable files containing 761 malware and 210 clean files with an accuracy of 98.26%.

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