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- Student, R.M. Samant, Student, Student, Student,Department of Information Technology, NBN Sinhgad School of Engineering, Pune, Head of Department, Department of Information Technology, NBN Sinhgad School of Engineering, Pune, Department of Information Technology, NBN Sinhgad School of Engineering, Pune, Department of Information Technology, NBN Sinhgad School of Engineering, Pune, Department of Information Technology, NBN Sinhgad School of Engineering, Pune,Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra,India, India, India, India, India
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Abstract
n In recent years, both the development of Windows application clients and the uses of smart mobile phones have increased significantly. As the number of Windows application users continues to grow, there is a rise in malicious individuals who develop harmful Windows applications with the intent of unlawfully obtaining confidential information and engaging in fraudulent activities. These applications are designed to target vulnerable areas such as mobile banking and digital wallets, aiming to deceive users and misuse their sensitive data. There are so many malicious software, tools, and programmers that are available. However, it is essential to establish a system that is capable and effective for identifying and thwarting freshly developed dangerous programmes written by hackers or programmers. This system should be able to recognise and react to sophisticated threats in an efficient manner. The purpose of this study is to identify fraudulent Windows apps using machine learning techniques.n
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Keywords: Malicious Application Detection in Windows Using SVM Algorithm
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| Volume | 01 |
| Issue | 01 |
| Received | June 7, 2023 |
| Accepted | July 3, 2023 |
| Published | July 20, 2023 |
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