The exponential growth in wireless network faults, vulnerabilities, and attacks make the WLAN security management a challenging research area [29]. Data mining applied to intrusion detection is an active area of research. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require processing. Data mining follows anomaly based intrusion detection. The drawback of the anomaly based intrusion detection in a wireless network is the high rate of false positive. This can be solved by a designing a hybrid intrusion detection system by connecting a misuse detection module to the anomaly detection module. In this paper, we propose to develop a hybrid intrusion detection system for wireless local area networks, based on Fuzzy logic. In this Hybrid Intrusion Detection system, anomaly detection is performed using the Bayesian network technique and misuse detection is performed using the Support Vector Machine (SVM) technique. The overall decision of system is performed by the fuzzy logic. For anomaly detection using Bayesian network, each node has a monitoring agent and a classifier within it for its detection and a mobile agent for information collection. The anomaly is measured based on the naïve Bayesian technique. For misuse detection using SVM, all the data that lie within the hyper plane are considered to be normal whereas the data that lie outside the hyper plane are considered to be intrusive. The outputs of both anomaly detection and misuse detection modules are applied by the fuzzy decision rules to perform the final decision making. Hybrid detection system improves the detection performance by combining the advantages of the misuse and anomaly detection [33].
Problem statement: Web usage mining is the technique of extracting useful information from server logs (users history) and finding out what users are looking for on the Internet. This type of web mining allows for the collection of Web access data for Web pages. Scope: The web usage data provides the paths leading to accessed Web pages with prefrences and higher priorities. This information is often gathered automatically into access logs through the Web server. Approach: In this study we propose Induction based decision rule model for generating inferences and implicit hidden behavioral aspects in the web usage mining which investigates at the web server and client logs. The decision based rule induction mining combines a fast decision rule induction algorithm and a method for converting a decision tree to a simplified rule set. Results: The experimentation is conducted by weka tool and the performance of proposed Induction based decision rule algorithm is evaluated in terms of mined decisive rules, Execution time, root mean square error and mean absolute error. Proposed induction rule mining needs 400 ms of execution time for decisive rule generation, whereas previous work expectation maximization algorithm needs 600ms. Conclusion: Web usage mining is evaluated with decisive rules of user page navigation and preferences. Decisive rule provide the web site developers and owners to known the site presentation likeness and demands of the web users
Due to the large size of the database, the entire training dataset could not be used to construct the classifiers. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk and then integrate all base classifiers to form Multiple classifier system (MCS). Sometimes this data streams does not include all the classes in its equal proportion as in the entire training data set. So we have newly introduced a method of Re-Sampling based on the statistical value of the class attribute. In the Proposed Method, the probability of occurrences of every class for the entire training data set have been estimated. Based on the probability, thresholds have been fixed for all the classes. When the data set have been selected randomly, the probabilities of the classes have been checked against the thresholds. The sample, which satisfies all the thresholds, is allowed to construct the Model. Otherwise, Re-sampling is performed and the process is repeated until the sample satisfies all the thresholds for the classes. The proposed method yields more accuracy than the one which does not have threshold on classes in the random samples. We have also compared the accuracy of different classifiers. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method
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