{"title":"Static Method to Locate Risky Features in Android Applications","authors":"Vaibhav Khullar, Tanya Gera, Tanya Mehta","doi":"10.1109/DELCON57910.2023.10127577","DOIUrl":null,"url":null,"abstract":"Over the past many years, there’s been an exponential development in the number of Android phone users across the world. Allowing for the exchange of real-time data and information that may revolutionize people’s lives. However, this provided an edge to hackers as well. They distribute thousands of malware apps to steal people’s data and make money. They employ reverse engineering to launch their harmful programs at the victim. Android application developers make every effort to avoid copying. But hackers always come up with various different attacks, techniques, and tools to avoid the identification of malware by anti-malware software. Android’s operating system is vulnerable to a variety of security exploits and weaknesses due to security flaws. In this article, we have devised a simple yet prominent strategy to extract the top risky features used by suspicious applications. Our research shows that the bulk of current research makes use of different designs, data sources, and approaches, including static, dynamic, and hybrid. Static analysis will be used in this article to identify the security vulnerabilities and risks in mobile applications. We have used a dataset of around 3000 applications and carried out a methodical investigation of it. Existing studies focus heavily on the safety of smartphone operating systems. We feel, however, that there is a need for detailed coverage of Android security issues, including the proliferation of malware, the investigation of anti-analysis tactics, and the analysis of current detection procedures. In this study, we cover topics such as Android’s security enforcement systems, threats to those mechanisms, associated difficulties, the evolution of malware from 2019 to 2022, and the cover tactics malware developers use to avoid detection. This study sheds light on the benefits and drawbacks of existing research methods and offers academics and practitioners a starting point for developing innovative approaches to Android malware detection, analysis, and protection.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DELCON57910.2023.10127577","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Over the past many years, there’s been an exponential development in the number of Android phone users across the world. Allowing for the exchange of real-time data and information that may revolutionize people’s lives. However, this provided an edge to hackers as well. They distribute thousands of malware apps to steal people’s data and make money. They employ reverse engineering to launch their harmful programs at the victim. Android application developers make every effort to avoid copying. But hackers always come up with various different attacks, techniques, and tools to avoid the identification of malware by anti-malware software. Android’s operating system is vulnerable to a variety of security exploits and weaknesses due to security flaws. In this article, we have devised a simple yet prominent strategy to extract the top risky features used by suspicious applications. Our research shows that the bulk of current research makes use of different designs, data sources, and approaches, including static, dynamic, and hybrid. Static analysis will be used in this article to identify the security vulnerabilities and risks in mobile applications. We have used a dataset of around 3000 applications and carried out a methodical investigation of it. Existing studies focus heavily on the safety of smartphone operating systems. We feel, however, that there is a need for detailed coverage of Android security issues, including the proliferation of malware, the investigation of anti-analysis tactics, and the analysis of current detection procedures. In this study, we cover topics such as Android’s security enforcement systems, threats to those mechanisms, associated difficulties, the evolution of malware from 2019 to 2022, and the cover tactics malware developers use to avoid detection. This study sheds light on the benefits and drawbacks of existing research methods and offers academics and practitioners a starting point for developing innovative approaches to Android malware detection, analysis, and protection.