Bio-inspired for Features Optimization and Malware Detection

被引:23
|
作者
Ab Razak, Mohd Faizal [1 ,2 ]
Anuar, Nor Badrul [1 ]
Othman, Fazidah [1 ]
Firdaus, Ahmad [1 ,2 ]
Afifi, Firdaus [1 ]
Salleh, Rosli [1 ]
机构
[1] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Comp Syst & Technol, Kuala Lumpur 50603, Malaysia
[2] Univ Malaysia Pahang, Fac Comp Syst & Software Engn, Kuantan 26300, Pahang, Malaysia
关键词
Android; Mobile devices; Bio-inspired algorithm; Features optimization; Machine learning; PARTICLE SWARM OPTIMIZATION; ANDROID MALWARE; CLASSIFICATION;
D O I
10.1007/s13369-017-2951-y
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The leaking of sensitive data on Android mobile device poses a serious threat to users, and the unscrupulous attack violates the privacy of users. Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. This paper aims to evaluate the effectiveness of the machine learning approach for detecting Android malware. In this paper, we applied the bio-inspired algorithm as a feature optimization approach for selecting reliable permission features that able to identify malware attacks. A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. This technique shows that the use of Android permissions is a potential feature for malware detection. The study compares the bio-inspired algorithm [particle swarm optimization (PSO)] and the evolutionary computation with information gain to find the best features optimization in selecting features. The features were optimized from 378 to 11 by using bio-inspired algorithm: particle swarm optimization (PSO). The evaluation utilizes 5000 Drebin malware samples and 3500 benign samples. In recognizing the Android malware, it appears that AdaBoost is able to achieve good detection accuracy with a true positive rate value of 95.6%, using Android permissions. The results show that particle swarm optimization (PSO) is the best feature optimization approach for selecting features.
引用
收藏
页码:6963 / 6979
页数:17
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