Mixed Pictures: Mental Models of Malware Malware is a serious problem for users, who become affected as a result of the decisions they make online. This paper presents a study examining mental models related to malware and regular software, in hopes of finding clues to that will help us understand what users know about malware, and what we can do to help them make better decisions online. The study involved two drawing tasks, where participants were asked to draw their understanding of how a word processor and malware work, respectively. Several concerning patterns emerged. Participants seemed to regard malware as a fundamentally different kind of entity than regular software. They make blackandwhite distinctions between malware and regular software in terms of whether the software is helpful or harmful, who the software serves, and who controls it. Finally, participants showed lesser knowledge of malware compared to regular software.

ImageBased Unknown Malware Classification with FewShot Learning Models Knowing malware types in every malware attacks is very helpful to the administrators to have proper defense policies for their system. It must be a massive benefit for the organization as well as the social if the automatic protection systems could themselves detect, classify an existence of new malware types in the whole network system with a few malware samples. This feature helps to prevent the spreading of malware as soon as any damage is caused to the networks. An approach introduced in this paper takes advantage of Oneshotfewshot learning algorithms in solving the malware classification problems by using some wellknown models such as Matching Networks, Prototypical Networks. To demonstrate an efficiency of the approach, we run the experiments on the two malware datasets (namely, MalImg and Microsoft Malware Classification Challenge), and both experiments all give us very high accuracies. We confirm that if applying models correctly from the machine learning area could bring excellent performance compared to the other traditional methods, open a new area of malware research.