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1 - Analyzing Weighted Attack Graphs Using Genetic Algorithms
M. Abadi Saeed Jalili
20.1001.1.16823745.1386.5.12.1.2

Each attack graph represents a collection of possible attack scenarios in a computer network. In this paper, we use weighted attack graphs (WAGs) for vulnerability assessment of computer networks. In these directed graphs, a weight is assigned to each exploit by the sec More

Each attack graph represents a collection of possible attack scenarios in a computer network. In this paper, we use weighted attack graphs (WAGs) for vulnerability assessment of computer networks. In these directed graphs, a weight is assigned to each exploit by the security analyst. The weight of an exploit is proportionate to the cost required to prevent that exploit. The aim of analyzing a weighted attack graph is to find a critical set of exploits such that the sum of their weights is minimum and by preventing them no attack scenario is possible. In this paper, we propose a greedy algorithm, a genetic algorithm with a greedy mutation operator, and a genetic algorithm with a dynamic fitness function for analyzing the weighted attack graphs. The proposed algorithms are used to analyze a sample weighted attack graph and several randomly generated large-scale weighted attack graphs. The results of experiments show that the proposed genetic algorithms outperform the greedy algorithm and find a critical set of exploits with less total weight. Finally, we compare the performance of the second genetic algorithm with an approximation algorithm for analyzing several randomly generated large-scale simple attack graphs. The results of experiments show that our proposed genetic algorithm has better performance than the approximation algorithm and finds a critical set of exploits with less cardinality. Manuscript profile
Open Access Article
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- Full-Text
2 - Fuzzy Voting for Anomaly Detection in Cluster-Based Mobile Ad Hoc Networks
Mohammad Rahmanimanesh Saeed Jalili
20.1001.1.16823745.1391.10.4.2.2

In this paper, an attack analysis and detection method in cluster-based mobile ad hoc networks with AODV routing protocol is proposed. The proposed method uses the anomaly detection approach for detecting attacks in which the required features for describing the normal More

In this paper, an attack analysis and detection method in cluster-based mobile ad hoc networks with AODV routing protocol is proposed. The proposed method uses the anomaly detection approach for detecting attacks in which the required features for describing the normal behavior of AODV protocol are defined via step by step analysis of AODV protocol and independent of any attack. In order to learn the normal behavior of AODV, a fuzzy voting method is used for combining support vector data description (SVDD), mixture of Gaussians (MoG), and self-organizing maps (SOM) one-class classifiers and the combined model is utilized to partially detect the attacks in cluster members. The votes of cluster members are periodically transmitted to the cluster head and final decision on attack detection is carried out in the cluster head. In the proposed method, a fuzzy voting method is used for aggregating the votes of cluster members in the cluster head by which the performance of the method improves significantly in detecting blackhole, rushing, route error fabrication, packet replication, and wormhole attacks. In this paper, an attack analysis method based on feature sensitivity ranking is also proposed that determines which features are influenced more by the mentioned attacks. This sensitivity ranking leads to the detection of the types of attacks launched on the network. Manuscript profile

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List of Articles jalili jalili