A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security

Sabar, N, Yi, X and Song, A 2018, 'A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security', IEEE Access, vol. 6, pp. 10421-10431.

Document type: Journal Article
Collection: Journal Articles

Title A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security
Author(s) Sabar, N
Yi, X
Song, A
Year 2018
Journal name IEEE Access
Volume number 6
Start page 10421
End page 10431
Total pages 11
Publisher IEEE
Abstract Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classification problems. However, to establish an effective SVM, the user needs to define the proper SVM configuration in advance, which is a challenging task that requires expert knowledge and a large amount of manual effort for trial and error. In this paper, we formulate the SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics. The high-level strategy uses the search performance to control the selection of which low-level heuristic should be used to generate a new SVM configuration. The low-level heuristics each use different rules to effectively explore the SVM configuration search space. To address bi-objective optimization, the proposed framework adaptively integrates the strengths of decomposition- and Pareto-based approaches to approximate the Pareto set of SVM configurations. The effectiveness of the proposed framework has been evaluated on two cyber security problems: Microsoft malware big data classification and anomaly intrusion detection. The obtained results demonstrate that the proposed framework is very effective, if not superior, compared with its counterparts and other algorithms.
Subject Engineering not elsewhere classified
Keyword(s) big data
cyber security
DOI - identifier 10.1109/ACCESS.2018.2801792
Copyright notice © 2018 IEEE. Translations and content mining are permitted for academic research only.
ISSN 2169-3536
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 14 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 91 Abstract Views  -  Detailed Statistics
Created: Tue, 26 Mar 2019, 09:36:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us