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Yangchun, Z., Zhao, Y., Yang, J..  2020.  New Virus Infection Technology and Its Detection. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). :388—394.

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.

Liu, Haitian, Han, Weihong, jia, Yan.  2019.  Construction of Cyber Range Network Security Indication System Based on Deep Learning. 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC). :495–502.
The main purpose of this paper is to solve the problem of quantitative and qualitative evaluation of network security. Referring to the relevant network security situation assessment algorithms, and by means of advanced artificial intelligence deep learning technology, to build a network security Indication System based on Cyber Range, and optimize the guidance model of deep learning technology.
Tenentes, Vasileios, Das, Shidhartha, Rossi, Daniele, Al-Hashimi, Bashir M..  2019.  Run-time Detection and Mitigation of Power-Noise Viruses. 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS). :275–280.
Power-noise viruses can be used as denial-of-service attacks by causing voltage emergencies in multi-core microprocessors that may lead to data corruptions and system crashes. In this paper, we present a run-time system for detecting and mitigating power-noise viruses. We present voltage noise data from a power-noise virus and benchmarks collected from an Arm multi-core processor, and we observe that the frequency of voltage emergencies is dramatically increasing during the execution of power-noise attacks. Based on this observation, we propose a regression model that allows for a run-time estimation of the severity of voltage emergencies by monitoring the frequency of voltage emergencies and the operating frequency of the microprocessor. For mitigating the problem, during the execution of critical tasks that require protection, we propose a system which periodically evaluates the severity of voltage emergencies and adapts its operating frequency in order to honour a predefined severity constraint. We demonstrate the efficacy of the proposed run-time system.
Nallusamy, T., Ravi, R..  2017.  Node energy based virus propagation model for bluetooth. 2017 International Conference on Communication and Signal Processing (ICCSP). :1778–1780.

With the continuous development of mobile based Wireless technologies, Bluetooth plays a vital role in smart-phone Era. In such scenario, the security measures are needed to be enhanced for Bluetooth. We propose a Node Energy Based Virus Propagation Model (NBV) for Bluetooth. The algorithm works with key features of node capacity and node energy in Bluetooth network. This proposed NBV model works along with E-mail worm Propagation model. Finally, this work simulates and compares the virus propagation with respect to Node Energy and network traffic.

Hongyo, K., Kimura, T., Kudo, T., Inoue, Y., Hirata, K..  2017.  Modeling of countermeasure against self-evolving botnets. 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). :227–228.

Machine learning has been widely used and achieved considerable results in various research areas. On the other hand, machine learning becomes a big threat when malicious attackers make use it for the wrong purpose. As such a threat, self-evolving botnets have been considered in the past. The self-evolving botnets autonomously predict vulnerabilities by implementing machine learning with computing resources of zombie computers. Furthermore, they evolve based on the vulnerability, and thus have high infectivity. In this paper, we consider several models of Markov chains to counter the spreading of the self-evolving botnets. Through simulation experiments, this paper shows the behaviors of these models.

Choejey, P., Fung, Chun Che, Wong, Kok Wai, Murray, D., Sonam, D..  2015.  Cybersecurity challenges for Bhutan. 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). :1–5.

Information and Communications Technologies (ICTs), especially the Internet, have become a key enabler for government organisations, businesses and individuals. With increasing growth in the adoption and use of ICT devices such as smart phones, personal computers and the Internet, Cybersecurity is one of the key concerns facing modern organisations in both developed and developing countries. This paper presents an overview of cybersecurity challenges in Bhutan, within the context that the nation is emerging as an ICT developing country. This study examines the cybersecurity incidents reported both in national media and government reports, identification and analysis of different types of cyber threats, understanding of the characteristics and motives behind cyber-attacks, and their frequency of occurrence since 1999. A discussion on an ongoing research study to investigate cybersecurity management and practices for Bhutan's government organisations is also highlighted.

Trajanovski, S., Kuipers, F. A., Hayel, Y., Altman, E., Mieghem, P. Van.  2015.  Designing virus-resistant networks: A game-formation approach. 2015 54th IEEE Conference on Decision and Control (CDC). :294–299.

Forming, in a decentralized fashion, an optimal network topology while balancing multiple, possibly conflicting objectives like cost, high performance, security and resiliency to viruses is a challenging endeavor. In this paper, we take a game-formation approach to network design where each player, for instance an autonomous system in the Internet, aims to collectively minimize the cost of installing links, of protecting against viruses, and of assuring connectivity. In the game, minimizing virus risk as well as connectivity costs results in sparse graphs. We show that the Nash Equilibria are trees that, according to the Price of Anarchy (PoA), are close to the global optimum, while the worst-case Nash Equilibrium and the global optimum may significantly differ for small infection rate and link installation cost. Moreover, the types of trees, in both the Nash Equilibria and the optimal solution, depend on the virus infection rate, which provides new insights into how viruses spread: for high infection rate τ, the path graph is the worst- and the star graph is the best-case Nash Equilibrium. However, for small and intermediate values of τ, trees different from the path and star graphs may be optimal.