Android Malware analysis using Java and SVM
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Author/Creator ORCID
Date
2020-01-20
Type of Work
Department
Computer Science and Electrical Engineering
Program
Computer Science
Citation of Original Publication
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Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Subjects
Abstract
The evolution of operating systems and the rising number of smartphones in the market has been grabbing a lot of attention in recent years. The capabilities and functionalities of android mobile devices have been recently expanded by a large number of third-party applications. Although technological advancements and innovative applications attract a lot of businessmen and scientists alike, it also entices malicious attackers and hackers. Applications installed in a device present a way for the attackers to breach the security of the system. With the growth of technology, there is a real need to understand the threats of malware and the process to nd them and protect the system from attacks. Application developers often ask unnecessary and wrong permissions such as misspelled, non-existent, deprecated, or protected ones. The data retrieved from such permissions are prone to malware attacks. Although the android system uses coarse-grained permissions from the user to warn about sensitive information access by applications, users usually have less knowledge of or control over how their privacy-sensitive data is used. The le operations are done by the application in the user's device has a severe impact if used for malicious purpose. This study is focused on analyzing multiple android applications and attempts to identify the characteristics of this application code with static and dynamic analysis using the Java Weka tool and classify them using the machine learning algorithm.