Securing a network from the attackers is a challenging task at present as many users involve in variety of computer networks. To protect any individual host in a network or the entire network, some security system must be implemented. In this case, the Intrusion Detection System (IDS) is essential to protect the network from the intruders. The IDS have to deal with a lot of network packets with different characteristics. A signature-based IDS is a potential tool to understand former attacks and to define suitable method to conquest it in variety of applications. This research article elucidates the objective of IDS with a mechanism which combines the network and host-based IDS. The benchmark dataset for DARPA is considered to generate the IDS mechanism. In this paper, a frame work IDSFS – a signature-based IDS with high pertinent feature selection method is framed. This frame work consists of earlier proposed Feature Selection method (HPFSM), Artificial Neural Network for classification of nodes or packets in the network, then the signatures or attack rules are configured by implementing Association Rule mining algorithm and finally the rules are restructured using a pattern matching algorithm-Aho-Corasick to ease the rule checking. The metrics like number of features, classification accuracy, False Positive Rate (FPR), Precision, Number of rules, Running Time and Memory consumption are checked and proved the proposed frame work’s efficiency.
Securing a network from the attackers is a challenging task at present as many users involve in variety of computer networks. To protect any individual host in a network or the entire network, some security system must be implemented. In this case, the Intrusion Detection System (IDS) is essential to protect the network from the intruders. The IDS has to deal with a lot of network packets with different characteristics. A signature-based IDS is a potential tool to understand former attacks and to define suitable method to conquest it in variety of applications. This research article elucidates the objective of IDS with a mechanism which combines the network and host-based IDS. The benchmark dataset for DARPA is considered to generate the IDS mechanism. In this paper, a frame work IDSFSC – signature-based IDS with high pertinent feature selection method is framed. This frame work consists of earlier proposed Feature Selection Method (HPFSM with Enhanced Artificial Neural Network (EANN) for classification of nodes or packets in the network, then the signatures or attack rules are configured by implementing Association Rule mining algorithm and finally the rules are restructured using a pattern matching algorithm-Aho-Corasick to ease the rule checking. The metrics classification accuracy, False Positive Rate (FPR) and Precision are checked and proved the proposed frame work’s efficiency.
The routing problems can be divided into two major classes. They are 1) Unicast routing and 2) Multicast routing. The Unicast routing problem is as follows. Given a source node sr, a destination node dn, a set of QoS constraints qc and an optimization goal (optional), find the best feasible path from sr to dn, which satisfies qc. The Multicast routing problem is as follows. Given a source node sr, a set st of destination nodes, a set of constraints cts and an optimization goal (optional), find the best feasible path covering sr and all nodes in st, which satisfies cts. This article presents two such Unicast QoS based algorithms called as Source Routing and the proposed Heuristic Routing. A Client Server based model has been generated to study the performance of the two algorithms with respect to the message overhead, response time and path delay. The Experiments and the results are analyzed.
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