MALWARE DETECTION USING HYBRID ANALYSIS

MALWARE DETECTION USING HYBRID ANALYSIS

The open source Android platform allows developers to take full advantage of the mobile operating system, but also raises significant issues related to malicious applications.Mobile malware is the highest threat to the security of IoT data, users personal information, identity, and corporate/financial information. We considered static, dynamic, and hybrid detection analysis.

ABSTRACT. The open source Android platform allows developers to take full advantage of the mobile operating system, but also raises significant issues related to malicious applications.Mobile malware is the highest threat to the security of IoT data, users personal information, identity, and corporate/financial information. We considered static, dynamic, and hybrid detection analysis. In this performance analysis, we compared static, dynamic, and hybrid analyses on the basis of data set, feature extraction techniques, feature selection techniques, detection methods, and the accuracy achieved by these methods. Therefore, we identify suspicious API calls, system calls, and the permissions and use them as features to detect mobile malware. This will assist application developers in the safe use of APIs when developing applications for industrial IoT networks. We propose to combine permission and API (Application Program Interface) calls and use machine learning methods to detect malicious Android Apps.

In our design, the permission is extracted from each App’s profile information and the APIs are extracted from the packed App file by using packages and classes to represent API calls. By using permissions and API calls as features to characterize each Apps,we can train a classifier to identify whether an App is potentially malicious or not.

INTRODUCTION

1.1 PROBLEMDOMAIN

The open source Android platform allows developers to take full advantage of the mobile operating system, but also raises significant issues related to malicious applications. On one hand, the popularity of Android absorbs attention of most developers for producing their applications on this platform. The increased number of applications, on the other hand, prepares a suitable prone for some users to develop different kinds of malware and insert them in Google Android market or other third party markets as safe applications. Malware has become more harmful than in the past as the number of intelligent systems and Internet-connected devices increased dramatically. Therefore one of the most important issues in cybersecurity has become the detection of previously unknown malware in the shortest time possible inorder to stop it from becoming epidemic and from harming users.

malware android machine-learning artificial-intelligence security

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