Adebayo, Olawale Surajudeen and Abdul Aziz, Normaziah (2019) Improved Malware detection model with Apriori Association rule and particle swarm optimization. Security and Communication Networks, 2019. pp. 1-13. ISSN 1939-0114 E-ISSN 1939-0122
|
PDF
- Published Version
Download (1MB) | Preview |
|
|
PDF (SCOPUS)
- Supplemental Material
Download (216kB) | Preview |
|
|
PDF (WOS)
- Supplemental Material
Download (301kB) | Preview |
Abstract
The incessant destruction and harmful tendency of malware on mobile devices has made malware detection an indispensable continuous field of research. Different matching/mismatching approaches have been adopted in the detection of malware which includes anomaly detection technique, misuse detection, or hybrid detection technique. In order to improve the detection rate of malicious application on the Android platform, a novel knowledge-based database discovery model that improves apriori association rule mining of a priori algorithm with Particle Swarm Optimization (PSO) is proposed. Particle swarm optimization (PSO) is used to optimize the random generation of candidate detectors and parameters associated with apriori algorithm (AA) for features selection. In this method, the candidate detectors generated by particle swarm optimization form rules using apriori association rule. These rule models are used together with extraction algorithm to classify and detect malicious android application. Using a number of rule detectors, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. The results of the experiments show that the proposed a priori association rule with Particle Swarm Optimization model has remarkable improvement over the existing contemporary detection models. © 2019 Olawale Surajudeen Adebayo and Normaziah Abdul Aziz.
| Item Type: | Article (Journal) |
|---|---|
| Additional Information: | 5505/79657 |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science |
| Depositing User: | Dr. Normaziah Abdul Aziz |
| Date Deposited: | 19 Mar 2020 16:21 |
| Last Modified: | 19 Mar 2020 16:22 |
| URI: | http://irep.iium.edu.my/id/eprint/79657 |
Actions (login required)
 |
View Item |
Download Statistics
Download Statistics