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Classification rule mining is a practical data mining technique widely used in real world. In the previous work, we have put forward a fuzzy class association rule mining method based on genetic network programming and applied it to network intrusion detection system which proved its efficiency and advantage. In this paper, a detailed comparison not only between fuzzy class association rule minings(FCARMs)...
With more important function in information society, software dependability has been in higher demand. Web application vulnerability has become one of the biggest threats for software security. Detecting and solving vulnerability is the effective way to enhance software dependability. Most active method traverses all Web links and interactive units in traversing step, which is easy to cause low efficiency...
Applying the basic fuzzy theory and method into intrusion detection has achieved a series success. In this paper, an intrusion detection model base on fuzzy sets is presented to avoid the sharp boundary problem in rules mining. Considering Apriori algorithm is time-consuming as well as space-consuming; moreover, we propose a new rule mining algorithm base prefix tree (PTBA). PTBA algorithm compress...
It's an efficient approach to identify the application traffic through application-level signatures, but the performance of an application-level identification approach heavily depends on accuracy and abundance of signatures. Unfortunately, deriving the signatures manually is very time consuming and difficult. Machine learning has been widely used in network data analysis. But existing studies mostly...
Privacy preserving data mining (PPDM) has become a popular topic in the research community. How to strike a balance between privacy protection and knowledge discovery in the sharing process is an important issue. This study focuses on privacy preserving utility mining (PPUM) and presents two novel algorithms, HHUIF and MSICF, to achieve the goal of hiding sensitive itemsets so that the adversaries...
Privacy and security issues in data mining become an important property in any data mining system. A considerable research has focused on developing new data mining algorithms that incorporate privacy constraints. In this paper, we focus on privately mining association rules in vertically partitioned data where the problem has been reduced to privately computing Boolean scalar products. We propose...
The network intrusion detection (NIDS) is faced with the question to detect many kinds of intrusion. In order to detect the complex attack, network intrusion detection system need to analysis massive data captured form different network safety equipments. So a new multi relational mining algorithm MRA2 is proposed. MRA2 depend on the association rules mining technology and the probability function...
Automatic configuration of large and heterogeneous ICT systems and their dependability mechanisms is both desirable and daunting for the inherent complexity of these systems.Configurations are commonly designed based on personal expertise, best practice, empirical evidence, without any automatic process and formal validation mechanism. This approach leads to frequent and reiterate errors with severe...
The continuous growth in connection speed allows huge amounts of data to be transferred through a network. An important issue in this context is network traffic analysis to profile communications and detect security threats. Association rule extraction is a widely used exploratory technique which has been exploited in different contexts (e.g., network traffic characterization). However, to discover...
The fuzzy association rule has been proven to be effective to present userspsila network behavior offline from a huge amount of collected packets. However, not only effectiveness, efficiency is important as well for Network Intrusion Detection Systems (NIDSs). None of those proposed NIDSs subject to fuzzy association rule can meet the real-time requirement because they all applied static mining approach...
The recent advance of data mining technology to analyze vast amount of data has played an important role in marketing business, despite its benefits in such areas, data mining also opens new threats to privacy and information security if not done or used properly. The main problem is that from non-sensitive data, one is able to infer sensitive information, including personal information, fact or even...
Anomaly detection has the double purpose of discovering interesting exceptions and identifying incorrect data in huge amounts of data. Since anomalies are rare events which violate the frequent relationships among data, we propose a method to detect frequent relationships and then extract anomalies. The RADAR (Research of Anomalous Data through Association Rules) method is based on data mining techniques...
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