Intrusion detection is a crucial part for security of information systems. Most intrusion detection systems use all features in their databases while some of these features may be irrelevant or redundant and they do not contribute to the process of intrusion detection. Therefore, different feature ranking and feature selection techniques are proposed. In this paper, hybrid feature selection methods are used to select and rank reliable features and eliminate irrelevant and useless features to have a more accurate and reliable intrusion detection process. Due to the low cost and low accuracy of filtering methods, a combination of these methods could possibly improve their accuracy by a reasonable cost and create a balance between them. In the first phase, two subsets of reliable features are created by application of information gain and symmetrical uncertainty filtering methods. In the second phase, the two subsets are merged, weighted and ranked to extract the most important features. This feature ranking which is done by the combination of two filtering methods, leads to higher the accuracy of intrusion detection. KDD99 standard dataset for intrusion detection is used for experiments. The better detection rate obtained in proposed method is shown by comparing it with other feature selection methods that are applied on the same dataset.

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C4ISR framework describes architecture in three views of information architecture and defines a set of products, which are main outputs of enterprise architecture design. Formats and templates, which are presented for C4ISR products, cannot describe uncertainty in process or data. Meanwhile, uncertainty in many information systems is unavoidable and using the concept of fuzzy numbers in architecture design helps architects handle uncertainty in process and data of organization. In this paper, we present a new template based on fuzzy-UML concept for some of C4ISR products such as Logical Data Model (OV-7), Operational Event/Trace Description (OV-6c) and Systems Event/Trace Description (SV-10c). To explain further, fictional Fast Pass system used at OilCo gas stations is used to demonstrate details of our proposed model.

Mobile Ad hoc Network (MANET) consists of some nodes, which are randomly placed in operational environment. This type of network does not have any infrastructure and nodes are completely dynamic and moveable. Each node can contact with the other in-range nodes. One of the disadvantages of MANET is the low lifetime of nodes power in which the energy consumption of the network goes up due to connectivity overheads. In this paper, by presenting a Fuzzy clustering algorithm, we endeavor for a more optimized energy consumption of this network by reducing the number of re-election of clusterheads (CHs) and reducing the reaffiliation of nodes and also by selecting the best node as CH amongst current members of each cluster. The result of simulation shows that our algorithm performs better than other existing algorithm.

Abstract-In this paper we express a new intrusion detection system based on counters and a cache memory to detect malicious nodes and selfish nodes in ad hoc network. Network survivability, networks performance and network security are important issues in mentioned networks. The presented work enhances the mentioned factors because it doesn't need heavy computation and also its speed and accuracy in detecting suspicious nodes are great. We simulated our methods by NS2 software. The simulation results show that the proposed method has significant performance.Index Terms-Intrusion detection system, malicious node, security, ad hoc network.