Biblio

Thousands of people have lost their life by COVID-19 infection. Authorities have seen the calamities caused by the corona virus in China. So, when the trace of virus was found in India, the only possible way to stop the spread of the virus was to go into lockdown. In a country like India where a major part of the population depends on the daily wages, being in lockdown started affecting their life. People where tend to go out for getting the food items and other essentials, and this caused the spread of virus. Many were infected and many lost their life by this. Due to the pandemic, the whole world was affected and many people working in foreign countries lost their jobs as well. These people who came back to India caused further spread of the virus. The main reason for the spread is lack of hygiene and a proper system to monitor the symptoms. Even though our country was in lockdown for almost 6 months the number of COVID cases doesn't get diminished. It is not practical to extend the lockdown any further, and people have decided to live with the virus. But it is essential to take the necessary precautions while interacting with the society. Automated system for checking that all the COVID protocols are followed and early symptom identification before entering to a place are essential to stop the spread of the infection. This research work proposes a smart door system, which evaluates the COVID-19 risk factors and collects the data of person before entering into any place, thereby ensuring that non-infected people are only entering to the place and thus the spread of virus can be avoided.

A browser extension, also known as a plugin or an addon, is a small software application that adds functionality to a web browser. However, security threats are always linked with such software where data can be compromised and ultimately trust is broken. The proposed research work jas developed a security model named WebSecAsst, which is a chrome plugin relying on the Machine Learning model XGBoost and VirusTotal to detect malicious websites visited by the user and to detect whether the files downloaded from the internet are Malicious or Safe. During this detection, the proposed model preserves the privacy of the user's data to a greater extent than the existing commercial chrome extensions.

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.

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.

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.

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.

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.

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.

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.