ShielDroid: a hybrid ML and DL approach for real-time malware detection system in Android
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| বিন্যাস: | গবেষণাপত্র |
| ভাষা: | English |
| প্রকাশিত: |
Brac University
2021
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/10361/15550 |
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10361-15550 |
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10361-155502022-01-26T10:19:58Z ShielDroid: a hybrid ML and DL approach for real-time malware detection system in Android Ahmed, Md Faisal Biash, Zarin Tasnim Shakil, Abu Raihan Ryen, Ahmed Ann Noor Hossain, Arman Hossain, Muhammad Iqbal Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Malware analysis Cyber-security Malware detection Real-time analysis Android application Neural network Computer security Malware (Computer software) Machine learning Application software--Development Mobile computing This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 40-41). Due to the rapid development of the advanced world of technology, there is a high increase in devices such as smartphones and tablets, which increase the number of applications used. Though an application has to pass the malware detection test before appearing in the play store, many applications successfully get trusted and accepted even though they contain malicious software variants that are challenging to detect. The application requires physical execution to see these malicious contents, which get undetected during the rst screening test. Due to the physical implementation of the application, it may be too late to undo the malware's damage. In this work, the usage of real-time Android malware detection analyzing Android applications to detect and swiftly distinguish complex malware has been discussed. This work focuses on the use of dynamic algorithms implemented by hybrid detection techniques of Android malware. After ltrating the collected dataset, the process of separation between harmful and benign apps is discussed. Then summarization and evaluation of the various techniques and classi cation algorithms employed have been discussed, identifying the best-suited method that gives the most accurate result in a minimum amount of time. The best way to reach the target is a hybrid Random Forest, and Multilayer perceptron network, where the overall accuracy achieved was 97.5% with an execution time of 22.945 seconds. An Android application, namely,\Shield: Malware Scanner", was developed using Java in determining if malware is present in an application. If there is any malware, it detects the type of malware and advises the user on securing their data and privacy and recovering from it. Md Faisal Ahmed Zarin Tasnim Biash Abu Raihan Shakil Ahmed Ann Noor Ryen Arman Hossain B. Computer Science 2021-10-26T06:28:41Z 2021-10-26T06:28:41Z 2021 2021-09 Thesis ID 21341042 ID 18141008 ID 18101632 ID 18101583 ID 18101707 http://hdl.handle.net/10361/15550 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 41 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Malware analysis Cyber-security Malware detection Real-time analysis Android application Neural network Computer security Malware (Computer software) Machine learning Application software--Development Mobile computing |
| spellingShingle |
Malware analysis Cyber-security Malware detection Real-time analysis Android application Neural network Computer security Malware (Computer software) Machine learning Application software--Development Mobile computing Ahmed, Md Faisal Biash, Zarin Tasnim Shakil, Abu Raihan Ryen, Ahmed Ann Noor Hossain, Arman ShielDroid: a hybrid ML and DL approach for real-time malware detection system in Android |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Ahmed, Md Faisal Biash, Zarin Tasnim Shakil, Abu Raihan Ryen, Ahmed Ann Noor Hossain, Arman |
| format |
Thesis |
| author |
Ahmed, Md Faisal Biash, Zarin Tasnim Shakil, Abu Raihan Ryen, Ahmed Ann Noor Hossain, Arman |
| author_sort |
Ahmed, Md Faisal |
| title |
ShielDroid: a hybrid ML and DL approach for real-time malware detection system in Android |
| title_short |
ShielDroid: a hybrid ML and DL approach for real-time malware detection system in Android |
| title_full |
ShielDroid: a hybrid ML and DL approach for real-time malware detection system in Android |
| title_fullStr |
ShielDroid: a hybrid ML and DL approach for real-time malware detection system in Android |
| title_full_unstemmed |
ShielDroid: a hybrid ML and DL approach for real-time malware detection system in Android |
| title_sort |
shieldroid: a hybrid ml and dl approach for real-time malware detection system in android |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15550 |
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