Problem statement:To distinguish the activities of the network traffic that the intrusion and normal is very difficult and to need much time consuming. An analyst must review all the data that large and wide to find the sequence of intrusion on the network connection. Therefore, it needs a way that can detect network intrusion to reflect the current network traffics. Approach: In this study, a novel method to find intrusion characteristic for IDS using decision tree machine learning of data mining technique was proposed. Method used to generate of rules is classification by ID3 algorithm of decision tree. Results: These rules can determine of intrusion characteristics then to implement in the firewall policy rules as prevention. Conclusion: Combination of IDS and firewall so-called the IPS, so that besides detecting the existence of intrusion also can execute by doing deny of intrusion as prevention.

Nowadays the security risk assessment play a crucial role, which is applied to the entire life cycle of information systems and communication technologies but still so many models for security risk assessment are non practical, therefore, it should be measured and improved. In this paper, a novel approach, in which Analytic Hierarchy Process (AHP) and Particles Swarm Optimization (PSO) can be combined with some changes, is presented. The method consists of; firstly, the analytic hierarchy structure of the risk assessment is constructed and the method of PSO comprehensive judgment is improved according to the actual condition of the information security. Secondly, the risk degree put forward is PSO estimation of the risk probability, the risk impact severity and risk uncontrollability. Finally, it gives examples to prove that this method Multi Objectives Programming Methodology (MOPM) can be well applied to security risk assessment and provides reasonable data for constituting the risk control strategy of the information systems security. Based on the risk assessment results, the targeted safety measures are taken, and the risk is transferred and reduced, which is controlled within an acceptable range.

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The information has evolved rapidly over the World Wide Web in the past few years. To satisfy information needs, users mostly submit a query via traditional search engines, which retrieve results on the basis of keyword matching principle. However, a keyword-based search cannot recognize the meanings of keywords and the semantic relationship among the terms in the user's query; thus, this technique cannot retrieve satisfactory results. The expansion of an initial query with relevant meaningful terms can solve this issue and enhance information retrieval. Generally, query expansion methods consider concepts that are semantically related to query terms within the ontology as candidates in expanding the initial query. An analysis of the correct sense of query terms, rather than only considering semantic relations, is necessary to overcome language ambiguity problems. In this work, we proposed a query expansion framework on the basis of query sense analysis and semantics mining using computer science domain ontology, followed by working prototype of the system. The experts analyzed the results of system prototype over test dataset and Web data, and found a remarkable improvement in the overall search performance. Furthermore, the proposed framework demonstrated better mean average precision and recall values than the baseline method.

Problem statement: Although it is very important to test any system extensively it is usually too expensive to do so owing to the cost and the resources that are involved in it. Software testing is a very important phase of software development to ensure that the developed system is reliable. Some systematic approach for testing is essential to test any system and make it acceptable. Combinatorial software interaction testing is one which tests all possible software interactions. This interaction could be at various levels such as two way interaction (pairwise) or three or four or five or six way interactions. Combinatorial interaction testing had been used in several fields. It was reported in literature that pairwise combinatorial interaction testing had identified most of the software faults. Approach: In this study we proposed a new strategy for test suite generation, a tree generation strategy for pairwise combinatorial software testing, with parameters of equal values. The algorithm considered one parameter at a time systematically to generate the tree until all the parameters were considered. This strategy used a cost calculation technique iteratively for each of the leaf nodes to generate the test suite until all the combinations were covered. Results: The experimental data showed that we had achieved about 88% (or more in some cases) of reduction in the number of test cases needed for a complete pairwise combinatorial software interaction testing. Conclusion: Thus, the strategy proposed had achieved a significant reduction in minimizing the number of test cases that was generated