Biblio

Experts often design security and privacy technology with specific use cases and threat models in mind. In practice however, end users are not aware of these threats and potential countermeasures. Furthermore, mis-conceptions about the benefits and limitations of security and privacy technology inhibit large-scale adoption by end users. In this paper, we address this challenge and contribute a qualitative study on end users' and security experts' perceptions of threat models and potential countermeasures. We follow an inductive research approach to explore perceptions and mental models of both security experts and end users. We conducted semi-structured interviews with 8 security experts and 13 end users. Our results suggest that in contrast to security experts, end users neglect acquaintances and friends as attackers in their threat models. Our findings highlight that experts value technical countermeasures whereas end users try to implement trust-based defensive methods.

Computer virus detection technology is an important basic security technology in the information age. The current detection technology has a high success rate for the detection of known viruses and known virus infection technologies, but the development of detection technology often lags behind the development of computer virus infection technology. Under Windows system, there are many kinds of file viruses, which change rapidly, and pose a continuous security threat to users. The research of new file virus infection technology can provide help for the development of virus detection technology. In this paper, a new virus infection technology based on dynamic binary analysis is proposed to execute file virus infection. Using the new virus infection technology, the infected executable file can be detected in the experimental environment. At the same time, this paper discusses the detection method of new virus infection technology. We hope to provide help for the development of virus detection technology from the perspective of virus design.

Cloud computing significantly increased the security threats because intruders can exploit the large amount of cloud resources for their attacks. However, most of the current security technologies do not provide early warnings about such attacks. This paper presents a Finite State Hidden Markov prediction model that uses an adaptive risk approach to predict multi-staged cloud attacks. The risk model measures the potential impact of a threat on assets given its occurrence probability. The attacks prediction model was integrated with our autonomous cloud intrusion detection framework (ACIDF) to raise early warnings about attacks to the controller so it can take proactive corrective actions before the attacks pose a serious security risk to the system. According to our experiments on DARPA 2000 dataset, the proposed prediction model has successfully fired the early warning alerts 39.6 minutes before the launching of the LLDDoS1.0 attack. This gives the auto response controller ample time to take preventive measures.