Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/588908
Title: | An Efficient Deep Learning Framework for Android Malware Detection |
Researcher: | Lakshman Rao A |
Guide(s): | Shashi M |
Keywords: | Computer Science Computer Science Software Engineering Engineering and Technology |
University: | Andhra University |
Completed Date: | 2023 |
Abstract: | Cybersecurity affects every aspect of our lives, whether or not we are conscious newlineof it. The term quotcyber securityquot encompasses any measures used to prevent harm to or newlineunauthorized access to digital resources such as computers, mobile devices, and newlinesoftware. Some of the major issues in cyber security are spam detection, intrusion newlinedetection, malware detection, etc. Among several cyber security issues, malware newlinedetection is one of the most important research areas in cyber security. Any software newlinedesigned to cause damage to a computer, mobile device, network, or server is called newlinemalware. Malware is a short term for quotmalicious softwarequot. Malicious software comes newlinein many forms, including viruses, spyware, worms, Trojan horses, rootkits, adware, newlinebotnets, and many more. Malware can be influenced by both PCs and mobile devices. newlineBut, there is a difference between PC malware and mobile malware. PC malware also newlinehas differences based on the operating system used. For example, the malware on a newlineWindows PC is different from the malware on a Linux PC. newlineWith the introduction of smartphones, mobile phone usage has increased newlinedramatically. The advent of smartphones has altered many aspects of our culture, newlineincluding media consumption, commerce, and day-to-day life. While security newlinesoftware is routinely used on laptops and desktops, the vast majority of mobile newlinedevices lack security protection and are thus susceptible to a new and rising kind of newlinemobile malware. The detection mechanisms used for mobile malware detection are newlinedifferent from those used for PC malware. The two most widely used mobile newlineoperating systems are Android and iOS. The market shares of the Android and iOS newlineoperating systems are 71% and 27%, respectively. So, there is a need to focus more newlineon Android malware detection. The Android operating system is a mobile operating newlinesystem developed by Google. It is based on the Linux environment. newlineThe most commonly used programming language for creating Android newlineapplications is Java. An Android application is an archived file |
Pagination: | 168 Pg |
URI: | http://hdl.handle.net/10603/588908 |
Appears in Departments: | Department of Computer Science & Systems Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 176.99 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 126.76 kB | Adobe PDF | View/Open | |
03_abstract.pdf | 100.65 kB | Adobe PDF | View/Open | |
04_content.pdf | 79.8 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 693.02 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 469.09 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.6 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 2.46 MB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1.13 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 1.8 MB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 391.95 kB | Adobe PDF | View/Open | |
12_annexure.pdf | 3.31 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 2.03 MB | Adobe PDF | View/Open | |
9743 - annemneedi lakshmanarao @ award.pdf | 2.35 MB | Adobe PDF | View/Open |
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