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Full-Text Articles in Engineering
Automated Dynamic Detection Of Self-Hiding Behaviors, Luke Baird
Automated Dynamic Detection Of Self-Hiding Behaviors, Luke Baird
Student Works
Certain Android applications, such as but not limited to malware, conceal their presence from the user, exhibiting a self-hiding behavior. Consequently, these apps put the user’s security and privacy at risk by performing tasks without the user’s awareness. Static analysis has been used to analyze apps for self-hiding behavior, but this approach is prone to false positives and suffers from code obfuscation. This research proposes a set of three tools utilizing a dynamic analysis method of detecting self-hiding behavior of an app in the home, installed, and running application lists on an Android emulator. Our approach proves both highly accurate …
Automated Dynamic Detection Of Self-Hiding Behavior In Android Apps, Luke Baird, Seth Rodgers
Automated Dynamic Detection Of Self-Hiding Behavior In Android Apps, Luke Baird, Seth Rodgers
Student Works
Android applications that conceal themselves from a user, defined as exhibiting a “self-hiding behavior,” pose a threat to the user’s privacy, as these applications can live on a device undetected by the user. Malicious applications can do this to execute without being found by the user. Three lists are analyzed in particular—the home, running, and installed lists—as they are directly related to the typical Android app life cycle. Additionally, self-hiding behavior in the device admin list is analyzed due to the potential for catastrophic actions to be taken by device admin malware. This research proposes four dynamic analysis tools that …
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework, Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, Lee Giles
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework, Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, Lee Giles
Computer Science Faculty Publications
Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library …