ﻻ يوجد ملخص باللغة العربية
The perpetual opposition between antiviruses and malware leads both parties to evolve continuously. On the one hand, antiviruses put in place solutions that are more and more sophisticated and propose more complex detection techniques in addition to the classic signature analysis. This sophistication leads antiviruses to leave more traces of their presence on the machine they protect. To remain undetected as long as possible, malware can avoid executing within such environments by hunting down the modifications left by the antiviruses. This paper aims at determining the possibilities for malware to detect the antiviruses and then evaluating the efficiency of these techniques on a panel of antiviruses that are the most used nowadays. We then collect samples showing this kind of behavior and propose to evaluate a countermeasure that creates false artifacts, thus forcing malware to evade.
Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware variants can be generated by reusing codes, malware attacks can be easily launched and thus become common in rece
This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning. According to the attackers capability and affected stage of the machine learning pipeline, the attack surfaces are recognize
Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that even a simple adversary can evade them. A practically useful malware c
Increasing numbers of mobile computing devices, user-portable, or embedded in vehicles, cargo containers, or the physical space, need to be aware of their location in order to provide a wide range of commercial services. Most often, mobile devices ob
Malware analysis has been extensively investigated as the number and types of malware has increased dramatically. However, most previous studies use end-to-end systems to detect whether a sample is malicious, or to identify its malware family. In thi