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Putting forward a system model based on association rule mining and improving the FP-Growth algorithm based on associative analysis. The experimental result shows that the network intrusion detection developed by this paper can work stably, find out intrusion activities accurately and promptly, improve the speed of data mining effectively, enhance the detective ability of intrusion detection greatly,...
In this article we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions...
The following topics are dealt with: computer science-technology; image matching; network intrusion detection system; data warehouse; data streams clustering algorithm; association rules; Web service; e-commerce; data security; Webpage content extraction; distance education; text classification; and image edge detection.
In this paper, we examine issues related to the research and applications of computational intelligence techniques in security data analysis. We focus on solve problems that involve incomplete, vague or uncertain information, which is difficult to come to a crisp solution. It is shown how an extended mass assignment framework can be used to extract relations between soft categories. These relations...
Recently association rule mining algorithms are using to solve data mining problem in a popular manner. Rule based mining can be performed through either supervised learning or unsupervised learning techniques. Among the wide range of available approaches, it is always challenging to select the optimum algorithm for rule based mining task. The aim of this research is to compare the performance between...
Malicious intrusion is the behavior that threats a large number of computers; therefore, recent research has focused on devising new techniques to detect and control internet intrusion with high efficiency and low cost. Unfortunately some anomaly detection system (ADS) over machine learning may get some false alarms if the results of machine learning cannot cover all the normal or abnormal data. In...
Computer systems are exposed to an increasing number and type of security threats due to the expanding of Internet in recent years. How to detect network intrusions effectively becomes an important techniques. This paper presents a novel fuzzy class association rule mining method based on Genetic Network Programming (GNP) for detecting network intrusions. GNP is an evolutionary optimization techniques,...
In a very large database, there exists sensitive information that must be protected against unauthorized accesses. The confidentiality protection of the information has been a long-term goal pursued by the database security research community and the government statistical agencies. In this paper, we proposed greedy methods for hiding sensitive rules. The experimental results showed the effectiveness...
Maritime surveillance systems analyze vast amounts of heterogeneous sensor data from a large number of objects. In order to support the operator while monitoring such systems, the identification of anomalous vessels or situations that might need further investigation may reduce the operator's cognitive load. While it is worth acknowledging that many existing mining applications support identification...
A technical scheme, which can properly arrange IDS and optimally apply the algorithms of detection and data mining to the Honeynet environment, is proposed in the building automation system completed by the author recently. In this specific environment, the position of IDS is deployed reasonably and the anomaly and misuse detection algorithm of IDS is designed and selected optimally. Meanwhile, the...
In this paper, intrusion detection approaches for relational database systems were studied. An immune based intrusion detection algorithm for relational databases was proposed. According to the algorithm, the data to be detected were encoded into binary strings after preprocessing. The philosophy of negative selection in biological immune systems was utilized to generate immune detectors. Intrusion...
With the increase of the web applications in information society, Web application software security become more and more important. Recent investigations show that web application vulnerabilities have become the largest security threat. Websense security report shows that in the first half of year 2008 above 75% of the most popular Web site have utilized by the hackers to run malicious code. Detecting...
This paper, first analyzes the method of wireless network intrusion detection, presents a wireless network intrusion detection algorithm based on association rule mining. The application of fuzzy association rules in the wireless network intrusion detection is mainly discussed, and the steps to implement the algorithm are expressed. A comparative analysis with the classical algorithm Apriori is made...
Association rules hiding algorithms often sanitize transactional databases for protecting sensitive information. Data modification is one of the most important sanitation approaches. However, the exist modification methods either focus on hiding sensitive rules only, or take measures to reduce the impact on non-sensitive rules from the whole database while hiding sensitive rules. In this paper, we...
Fuzzy logic based methods together with the techniques from Artificial Intelligence have gained importance. Association rules together with fuzzy logic to model the fuzzy association rules are being used for classifying data. These together with the techniques of genetic algorithms like genetic programming are producing better results. Therefore, in this article, we firstly analyze the current situation...
Masqueraders commonly impersonate legitimate userpsilas account to gain access to computer systems that they are not authorized to enter. Normally users exhibit some regularity in their behavior such as command usage. We propose a new approach to mine user command associations. Since each user may have different usage behavior, using the built behavior pattern to predict a masqueraderpsilas next command...
Intrusion detection is one of network security area of technology main research directions. Data mining technology was applied to network intrusion detection system (NIDS), may automatically discover the new pattern from the massive network data, to reduce the workload of the manual compilation intrusion behavior patterns and normal behavior patterns. This article reviewed the current intrusion detection...
Intrusion detection systems (IDSs) are increasingly a key part of systems defense. Various approaches to intrusion detection are currently being used, but they are relatively ineffective. Recently applying artificial intelligence, machine learning and data mining techniques to IDS are increasing. Artificial intelligence plays a driving role in security services. This paper proposes an Immune based...
A main concern for network intrusion detection systems is the ability of an intruder to evade the detection by routing through a chain of intermediate stepping-stone hosts. The intruders have developed some evasion techniques such as injecting chaff packets or timing jitter. Such evasion techniques cause most of the previous timing-based detection algorithms to fail. In this paper, we address these...
The specification language Z is almost entirely applied to design of large software system. However, nothing is done in applying Z to developing security-critical systems. The intrusion detection technology is one of the most important dynamic security technologies, which can be used in the critical security system construction and the basic service protection. Apparently, applying formal specification...
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