Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining

• Published in: Neural computing & applications Vol. 23; no. 7-8; pp. 2451 - 2463
• Main Authors: Sheikhan, Mansour, Sharifi Rad, Maryam
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2013; Springer
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Exact sciences and technology; Image Processing and Computer Vision; Information systems. Data bases; Memory and file management (including protection and security); Memory organisation. Data processing; Original Article; Probability and Statistics in Computer Science; Software; Theoretical computing; Gravitational search algorithm; Feature selection; Fuzzy clustering; Fuzzy association rules; Misuse detection; Neural network; Data mining; Cluster analysis; Grid; Very large databases; Fuzzy neural nets; Classification; Selection criterion; Computer security; Data analysis; Gravitation; Adaptive resonance theory; Redundancy; Statistical association; Computational complexity; Association scheme; False alarm rate; Dimensioning; Fuzzy logic; Experimental result; Heuristic method; Artificial intelligence; Intrusion detection systems
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-1204-y

Feature selection is one of the most important techniques for data preprocessing in classification problems. In this paper, fuzzy grids–based association rules mining, as an effective data mining technique, is used for feature selection in misuse detection application in computer networks. The main idea of this algorithm is to find the relationships between items in large datasets so that it detects correlations between inputs of the system and then eliminates the redundant inputs. To classify the attacks, a fuzzy ARTMAP neural network is employed whose training parameters are optimized by gravitational search algorithm. The performance of the proposed system is compared with some other machine learning methods in the same application. Experimental results show that the proposed system, when choosing optimum “feature subset size-adjustment” parameter, performs better in terms of detection rate, false alarm rate, and cost per example in classification problems. In addition, employing the reduced-size feature set results in more than 8.4 percent reduction in computational complexity.