Evaluation of machine learning classifiers for mobile malware detection

FA Narudin, A Feizollah, NB Anuar, A Gani - Soft Computing, 2016 - Springer
Mobile devices have become a significant part of people's lives, leading to an increasing
number of users involved with such technology. The rising number of users invites hackers …

A taxonomy and qualitative comparison of program analysis techniques for security assessment of android software

A Sadeghi, H Bagheri, J Garcia… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
In parallel with the meteoric rise of mobile software, we are witnessing an alarming
escalation in the number and sophistication of the security threats targeted at mobile …

N-opcode analysis for android malware classification and categorization

BJ Kang, SY Yerima, K McLaughlin… - … conference on cyber …, 2016 - ieeexplore.ieee.org
Malware detection is a growing problem particularly on the Android mobile platform due to
its increasing popularity and accessibility to numerous third party app markets. This has also …

Machine learning classification model for network based intrusion detection system

S Kumar, A Viinikainen… - 2016 11th international …, 2016 - ieeexplore.ieee.org
With an enormous increase in number of mobile users, mobile threats are also growing
rapidly. Mobile malwares can lead to several cybersecurity threats ie stealing sensitive …

N-gram opcode analysis for android malware detection

BJ Kang, SY Yerima, S Sezer, K McLaughlin - arXiv preprint arXiv …, 2016 - arxiv.org
Android malware has been on the rise in recent years due to the increasing popularity of
Android and the proliferation of third party application markets. Emerging Android malware …

Cooperative network behaviour analysis model for mobile Botnet detection

M Eslahi, M Yousefi, MV Naseri… - … IEEE Symposium on …, 2016 - ieeexplore.ieee.org
Recently, the mobile devices are well integrated with Internet and widely used by normal
users and organizations which employ Bring Your Own Device technology. On the other …

[PDF][PDF] Mobile botnet detection model based on retrospective pattern recognition

M Eslahi, M Yousefi, MV Naseri… - … Journal of Security …, 2016 - researchgate.net
The dynamic nature of Botnets along with their sophisticated characteristics makes them one
of the biggest threats to cyber security. Recently, the HTTP protocol is widely used by …

Classifying android malware with dynamic behavior dependency graphs

Z Lin, R Wang, X Jia, S Zhang… - 2016 IEEE Trustcom …, 2016 - ieeexplore.ieee.org
Malware, a significant threat to maintain a healthy Android ecosystem, always receives
considerable attentions. This paper proposes a new dynamic Android malware classification …

A multi-level feature extraction technique to detect moble botnet

M Yang, Q Wen - 2016 2nd IEEE International Conference on …, 2016 - ieeexplore.ieee.org
Android malware detection has been heavily studied, which classical android malware
detecting approaches are signature-based or behavior-based detection based on the files …

基于特征分析Android 恶意应用检测方法的研究

孙承庭, 吴凯娇, 马文海 - 南京邮电大学学报: 自然科学版, 2016 - cqvip.com
Android 是目前广泛应用的移动操作系统, 也是恶意软件首选的攻击目标.
为了在恶意应用发布和攻击用户前将其分析, 识别出来, 文中提出了一种动态检测Android …