Computer Viruses Research Articles

The Driving Forces for the Involvement of Higher Learning Institution’s Students in Cybercrime Acts. A Case of Selected Higher Learning Institutions in Tanzania

This study investigates the extent and driving forces of cybercrime acts among students in higher learning institutions. The research digs into common cybercrime involvement and explores potential motives driving students' engagement in such illicit activities. A mixed-methods approach was adopted, involving online surveys and interviews with 308 samples size from a population of 1500 participants from selected higher learning institutions in Tanzania. Institutions involved in this study included Collage of Business Education (CBE), Dar es Salaam Institute of Technology (DIT), Institute of Finance Management (IFM) and Tanzania Institute of Accountancy (TIA). The findings revealed that digital piracy, hacking, computer viruses, spam mailing, computer-related forgery, and cyberbullying were prevalent forms of cybercrime among the participants. Significant associations were identified between cybercrime involvement and factors such as social-economic status, technological changes, peer influence, lack of awareness of cybercrime, individual traits, and internet usage contributing to students' involvement in cybercrime. In light of these findings, the study recommends implementing comprehensive educational programs, strengthening institutional cybersecurity, promoting positive peer influence, enhancing collaboration with law enforcement, and integrating ethical training into the curriculum. These efforts will foster a safe and responsible digital environment within higher learning institutions, protecting students from cybercrime risks and promoting ethical digital citizenship.

Cyber security laws and safety in e-commerce in India

In today’s information technology age, the issue of cyber Security is a complicated and fascinating area of law. The phenomenal growth and development of e-commerce in India is astounding. However, with the rising dependence on internet commerce, the dangers of fraud and security and trust problems have become severe impediments. Creating robust legal and regulatory frameworks that meet the growing concerns about online fraud, data security, and intellectual property protection in both local and international business contexts is critical. The e-commerce sector, like any expanding business, confronts various obstacles, primarily due to an inadequate and inefficient legal and regulatory framework that fails to guarantee the rights and duties of all players engaged sufficiently. To protect user data, tackle cyber threats, and maintain customer trust, e-commerce enterprises must comply with legal regulations. In India, cybersecurity governance falls under the Information Technology Act of 2000, regulating e-commerce, electronic contracts, data protection, and cybercrimes. The imminent passage of the Personal Data Protection Bill, 2019, is expected after ongoing review. The Indian Penal Code addresses unauthorized access, hacking, identity theft, phishing, and computer virus dissemination. The Reserve Bank of India oversees online payment and financial security, mandating two-factor authentication, encryption, and secure payment channels. CERT-In coordinates national cybersecurity incidents, while electronic signatures and digital certificates hold legal recognition. Intellectual Property Laws regulate online violations of patents, copyrights, and trademarks. The Indian government also enforces cybersecurity standards for enterprises and organizations, covering IT infrastructure and incident response. Nonetheless, further steps must be taken to improve the efficiency of India’s cyber security regulations. This research study uses a doctrinal and analytical approach to examine India’s present Cyber Security Laws and Guidelines. It assesses their effectiveness in addressing legal concerns with Security, privacy, and data protection inside the country. It also evaluates the legal structure that governs the link between e-commerce and Cyber Laws in India. This research will provide a thorough overview of the present condition of cyber security regulations in India, setting the way for prospective reforms and progress in this critical area.

A Probabilistic Graph Diffusion Model for Source Localization (Student Abstract)

Source localization, as a reverse problem of graph diffusion, is important for many applications such as rumor tracking, detecting computer viruses, and finding epidemic spreaders. However, it is still under-explored due to the inherent uncertainty of the diffusion process: after a long period of propagation, the same diffusion process may start with diverse sources. Most existing solutions utilize deterministic models and therefore cannot describe the diffusion uncertainty of sources. Moreover, current probabilistic approaches are hard to conduct smooth transformations with variational inference. To overcome the limitations, we propose a probabilistic framework using continuous normalizing flows with invertible transformations and graph neural networks to explicitly model the uncertainty of the diffusion source. Experimental results on two real-world datasets demonstrate the effectiveness of our model over strong baselines.

Analysis of the capacity of existing anti-virus protection systems and their based methods for detecting new malware in military information systems

The article solves the task of analyzing the ability of existing anti-virus systems and the methods based on them to detect new malicious software in information systems of critical infrastructure, in particular, the sector of the state defense forces. It is noted that the official data of the developers of antivirus systems often do not confirm the declared level of accuracy of detecting new malicious software in practice. In addition, in most cases, the declared accuracy rate of detecting new malware is higher than the similar rate of detection of known malware, which indicates that the antivirus systems in question are tested in specific conditions that are too different from real ones. The properties of new malicious software are described in order to find the most suitable class of computer viruses. Classes of polymorphic (oligomorphic) and metamorphic viruses demonstrate the most complete compliance with the specified properties, which allows us to assert their significant share in the use of new malicious software. The characteristics of malicious software detection methods are given, which due to their properties are able to adapt to a certain extent to their metamorphic (polymorphic) nature. Methods based on the theory of fuzzy logic demonstrate the most complete correspondence. The direction of improvement of the existing anti-virus systems in order to increase the adaptability to the detection of new (polymorphic, metamorphic) classes of malicious software is proposed. The obtained results should be considered as a basis for the implementation of new approaches to the detection of malicious software in order to identify previously unknown instances of it, which will allow to significantly increase the effectiveness of ensuring cyber security of modern information systems and networks.

An epidemiological model for computer virus with Atangana–Baleanu fractional derivative

The Era of data is transubstantiating into a Big Data model in this technological world in the early 21st century. In 2005, Roger Mougalas coined a combination of data for this future world of the human race. The information helps to find specific solutions for any physical problem under Catastrophic circumstances in high populations such as Covid-19. To store massive data and historical events in a computer, the possibility of damage occurred to the complete data. Hence, viruses are a crucial threat to such data worth millions and billions. For this purpose, we spend enormous costs and efforts to build defensive strategies to save that information. Analyzing the expansion and extension of viruses helps to protect data and prevent viruses. In this manuscript, we study optimal control analysis for the suggested model in the sense of the Atangana–Baleanu derivative (AB-derivative). We employed a fixed point theorem to analyze the solutions for the fractional order computer virus model. We verified the results numerically and expressed them graphically.

Stability and Hopf bifurcation analysis for fractional-order SVEIR computer virus propagation model with nonlinear incident rate and two delays

This paper is concerned with the stability and Hopf bifurcation for a fractional-order Susceptible-Vaccinated-Exposed-Infectious-Recovered (SVEIR) computer virus propagation model with a nonlinear incident rate. By employing the linearization technique and Routh-Hurwitz method, the sufficient criterion is established for the locally asymptotic stability of endemic equilibrium point. The Hopf bifurcation is also studied for the SVEIR computer virus model by taking time delay as the bifurcation parameter. The research results show that the stability and Hopf bifurcation of proposed model are significantly affected by both time delay and the order of the fractional derivative. Examples with proper parameters and several simulations are given to illustrate the validity of the theoretical results.

Computer network information security and protection strategies in the era of big data

The Internet has entered the big data era, and as more and more businesses employ big data, cloud computing, the Internet of Things, artificial intelligence, and other technologies, the amount of data on the Internet as a whole has also grown dramatically. While Big Data technology has improved the efficiency of many companies, it has also brought challenges for computer network information security protection technology, including massive data privacy leaks that seriously jeopardize the privacy of Internet users. This paper synthesizes literature review findings proving that computer network information security in the big data era has the characteristics of universality, suddenness, concealment, and intelligence in order to address this issue. It also studies the main network information security technologies, such as network situational awareness, vulnerability scanning, intrusion detection and firewall, and identity authentication. In addition, the paper finds that the current problems of network information security mainly include phishing attacks, computer viruses, ransomware, denial of service attacks, APT attacks and threats from inside the network. Finally, the paper explores a number of protective measures to address existing security issues, including the development of network monitoring and threat intelligence systems, the implementation of access control and authentication technologies, network isolation methods, regular software scanning and updates, and organising internal staff security training. Through this study we are able to fully understand the overall pulse of current computer network information security technology and development trends in the era of Big Data, thus providing a more comprehensive and accurate reference for Big Data-related participants to develop responsive information security protection measures and strategies to provide designation.

PENGONTROLAN KEAMANAN SISTEM KOMPUTER CLIENT DARI SERANGAN HACKER DAN VIRUS KOMPUTER SECARA JARAK JAUH (REMOTE SERVER) DENGAN MENGGUNAKAN SSH

Computer system security is very important to be considered by computer users to protect their computers from attacks such as hackers and computer viruses that can take data and damage the user's computer system. Hacker is a person or party who has the skill in breaking through and sneaking to access a computer without the user's permission and can take data and even damage the system on the user's computer. And a Computer Virus is a computer program that copies and inserts copies into the program and can damage the computer system. So that by using SSH (Secure Shell) can control and check computer security from hacker attacks and computer viruses without having to come to the location where there is a client computer or done remotely (Remote Server).

Temporal Cascade Model for Analyzing Spread in Evolving Networks

Current approaches for modeling propagation in networks (e.g., of diseases, computer viruses, rumors) cannot adequately capture temporal properties such as order/duration of evolving connections or dynamic likelihoods of propagation along connections. Temporal models on evolving networks are crucial in applications that need to analyze dynamic spread. For example, a disease spreading virus has varying transmissibility based on interactions between individuals occurring with different frequency, proximity, and venue population density. Similarly, propagation of information having a limited active period, such as rumors, depends on the temporal dynamics of social interactions. To capture such behaviors, we first develop the Temporal Independent Cascade (T-IC) model with a spread function that efficiently utilizes a hypergraph-based sampling strategy and dynamic propagation probabilities. We prove this function to be submodular, with guarantees of approximation quality. This enables scalable analysis on highly granular temporal networks where other models struggle, such as when the spread across connections exhibits arbitrary temporally evolving patterns. We then introduce the notion of “reverse spread” using the proposed T-IC processes, and develop novel solutions to identify both sentinel/detector nodes and highly susceptible nodes. Extensive analysis on real-world datasets shows that the proposed approach significantly outperforms the alternatives in modeling both if and how spread occurs, by considering evolving network topology alongside granular contact/interaction information. Our approach has numerous applications, such as virus/rumor/influence tracking. Utilizing T-IC, we explore vital challenges of monitoring the impact of various intervention strategies over real spatio-temporal contact networks where we show our approach to be highly effective.

Distributed immune time-delay SEIR-S model for new power system information network virus propagation

The new power system information network has the security problem of computer virus attack, and the study of its transmission mechanism is helpful to discover the law and influence of virus transmission. In this paper, the research method of epidemic theory is introduced, and a new Susceptible-Exposed-Infectious-Recovered-Susceptible(SEIR-S) virus model is proposed. The immune time-delay parameter is introduced to simulate the evolution and mutation of the virus so that nodes immune to the virus can still be re-infected after a certain time interval. At the same time, the immune time of different nodes is different, and the distributed immune time delay is used to enhance the authenticity of the simulated virus transmission; and considering the influence of the scale-free characteristics of the information network, this paper establishes a continuous Markov chain based on time. The transmission process of the virus, and then deduce the theoretical analysis results of the virus infection rate threshold. Based on theoretical analysis, the propagation process of the SEIR-S virus model with distributed immune time delay was simulated by using the Monte Carlo method, and the accuracy of the threshold formula of virus infection rate was verified. The influence rule of the hysteresis parameter, that is, increasing the average immune time of nodes to viruses can reduce the infection density of the network in a steady, and at the same time, making the immune time of network nodes obey a normal distribution can effectively reduce the oscillation effect of viruses on the network.

Extrapolated nonstandard numerical schemes for solving an epidemiological model for computer viruses

Abstract— The aim of this work is to construct high-order numerical schemes that preserve the dynamical properties of a mathematical model describing the spread of computer viruses on the Internet. For this purpose, we first apply Mickens’ methodology to formulate a dynamically consistent nonstandard finite difference (NSFD) scheme for the model under consid­eration. After that, the constructed NSFD scheme is combined with Richardson’s extrapolation method to generate higher-accuracy numerical approximations. The result is that we obtain extrapolated numerical schemes that not only preserve the dynamical proper­ties of the computer virus propagation model but also provide higher-accuracy numerical approximations. In addition, a set of numerical examples is conducted to illustrate and support the theoretical findings and to show the advantages of the proposed numerical schemes.

INFORMATION SECURITY AND CYBER SECURITY

In the era of digital transformation, the dependence on information technology services and products has increased significantly. Accordingly, the challenges in information security and cyber security have increased. Social distancing, while playing an important role in containing the spread of COVID-19, increases the risk of infected workstations, unauthorized access, data leakage, malicious code execution, and all the types of risks that come with working in a vulnerable environment. Computer security, cyber security or information security - protection of computer systems and control points of networks from its interference, unauthorized use or damage to hardware, software and electronic data, as well as from destruction and violation of the services they provide to users. This area is becoming more and more important for computer systems, the Internet and wireless networks such as Bluetooth and Wi-Fi. Because of its complexity, in terms of politics and technology, cyber security is also one of the main challenges in modern life. Cyber security is one of the most important fields in today's world. This branch is connected with such demanding professions and specialties as: security analyst, security engineer, security architect, security administrator, security specialist, consultant, etc. Types of Malware - Malware (Malware); computer virus; mobile media means; Trojan (Trojan Horse); rootkit; Backdoor (Backdoor); Worm (Computer worm); Keylogger (Keylogger). In 2003, the computer virus Slammer penetrated the management system of the electric distribution network and slowed down its operation, which also caused the speed of management to slow down. As a result, even a small incident like a tree falling on a power line can set off a serious chain reaction. In particular, in the US state of Ohio, the voltage began to change, and the equipment, which was installed to prevent the cascade effect, failed to isolate the high voltage area in time. As a result, 8 US states, 2 Canadian provinces (a total of 50 million people) were left without electricity and services dependent on it. This example shows what can result from a little carelessness, ignoring security issues, in the digital information space. Today, cyber security is at risk like never before.

Implications of false alarms in dynamic games on cyber-security

Cyber-threats including computer viruses, data breaches, and denial-of-service attacks have become increasingly prevalent and need to be handled effectively. Imperfect prevention of cyber-attacks inevitably results in false alarms from the defense system. We consider a dynamic game in which a defender operating an intrusion detection system is penalized for false alarms. By characterizing the equilibrium of the game, we search for the implications of false alarms under various circumstances. Specifically, we consider the cyber-insurance market, operational risk, and government intervention. Our model shows that both demand and supply of cyber-insurance can be low, which explains the underdevelopment of the cyber-insurance market. The analysis of an extended model with operational risk shows that reducing the operational risk decreases the cyber-risks but increases the false alarm rate. Regarding government intervention, we suggest a simple policy that achieves a socially optimal outcome.

A Numerical Study Based on Haar Wavelet Collocation Methods of Fractional-Order Antidotal Computer Virus Model

Computer networks can be alerted to possible viruses by using kill signals, which reduces the risk of virus spreading. To analyze the effect of kill signal nodes on virus propagation, we use a fractional-order SIRA model using Caputo derivatives. In our model, we show how a computer virus spreads in a vulnerable system and how it is countered by an antidote. Using the Caputo operator, we fractionalized the model after examining it in deterministic form. The fixed point theory of Schauder and Banach is applied to the model under consideration to determine whether there exists at least one solution and whether the solution is unique. In order to calculate the approximate solution to the model, a general numerical algorithm is established primarily based on Haar collocations and Broyden’s method. In addition to being mathematically fast, the proposed method is also straightforward and applicable to different mathematical models.

Finding the Source in Networks: An Approach Based on Structural Entropy

The popularity of intelligent devices provides straightforward access to the Internet and online social networks. However, the quick and easy data updates from networks also benefit the risk spreading, such as rumor, malware, or computer viruses. To this end, this article studies the problem of source detection, which is to infer the source node out of an aftermath of a cascade, that is, the observed infected graph G N of the network at some time. Prior arts have adopted various statistical quantities such as degree, distance, or infection size to reflect the structural centrality of the source. In this article, we propose a new metric that we call the infected tree entropy (ITE), to utilize richer underlying structural features for source detection. Our idea of ITE is inspired by the conception of structural entropy [ 21 ], which demonstrated that the minimization of average bits to encode the network structures with different partitions is the principle for detecting the natural or true structures in real-world networks. Accordingly, our proposed ITE based estimator for the source tries to minimize the coding of network partitions brought by the infected tree rooted at all the potential sources, thus minimizing the structural deviation between the cascades from the potential sources and the actual infection process included in G N . On polynomially growing geometric trees, with increasing tree heterogeneity, the ITE estimator remarkably yields more reliable detection under only moderate infection sizes, and returns an asymptotically complete detection. In contrast, for regular expanding trees, we still observe guaranteed detection probability of ITE estimator even with an infinite infection size, thanks to the degree regularity property. We also algorithmically realize the ITE based detection that enjoys linear time complexity via a message-passing scheme, and further extend it to general graphs. Extensive experiments on synthetic and real datasets confirm the superiority of ITE to the baselines. For example, ITE returns an accuracy of 85%, ranking the source among the top 10%, far exceeding 55% of the classic algorithm on scale-free networks.

Identifying multiple influence sources in social networks based on latent space mapping

We are currently in a network era which enables us to communicate more widely and more easily via the social networks. Meanwhile, negative information, such as fake news, rumors and computer viruses, often spread in social network. In order to restrain the propagation of such negative influence, we must find its sources in the network. But in real-world applications, we usually only know the scope of the negative influence spreading, and do not know who first propagates the negative influence. However, we can identify the sources of the negative influence based on the information of some observed nodes which are negatively influenced. This is the problem of influence sources locating. To tackle this problem, we present a latent space mapping-based method for identifying the multiple influence sources in the independent cascade model. The method first detects the candidate sources of the observed nodes based on message passing in a reversed network. An algorithm is presented to calculate the activation probability between nodes according to the influence spreading pattern in the independent cascade model. To evaluate each node’s rationality as the propagation source, we use the difference between the length of the path influencing an observed node and its activation time. We define two latent spaces, namely the influence senders and receivers’ latent spaces, and map the nodes into these two latent spaces to form a model describing the influence propagation. An estimation-maximization-based algorithm is proposed to optimize the propagation model. Based on this model, we propose a latent space mapping-based algorithm to identify the influence sources. The probability for each node to be a source is calculated by its positions in the latent spaces. Finally, k nodes with the largest probabilities are selected as the sources. Empirical results demonstrate that the influence sources identified by the proposed method can influence more observed nodes at more accurate time than other methods.

DYNAMICAL ASPECTS OF A DELAYED COMPUTER VIRUSES MODEL WITH HORIZONTAL AND VERTICAL DISSEMINATION OVER INTERNET

In this paper, to explore propagation dynamics of computer viruses over the Internet, a delayed SLBRS (susceptible–latent–breaking-out–recovering–susceptible) model of computer viruses considering horizontal and vertical dissemination over Internet is studied. The basic reproduction number at which computer viruses are prone to be extinct or continue to exist is calculated with the help of the next generation matrix method. By choosing the time delay because of processing time consumed to recover the breaking-out computers as bifurcation parameter, a series of sufficient criteria for experience of Hopf bifurcation are derived. Specially, optimal strategies to control dissemination of computer viruses over Internet are established. Finally, numerical simulations for certificating correctness of our theoretical findings are carried out. The findings show that the exhibition of Hopf bifurcation has an important impact on prediction and control of the propagation of computer viruses over the Internet.

Dynamically consistent nonstandard numerical schemes for solving some computer virus and malware propagation models

This work is devoted to constructing reliable numerical schemes for some computer virus and malware propagation models. We apply the Mickens' methodology to formulate nonstandard finite difference (NSFD) schemes for some epidemiological models describing the spread of computer viruses and malware. Positivity, boundedness and global asymptotic stability (GAS) of the proposed NSFD schemes are studied rigorously. It should be emphasized that the GAS of the NSFD models are established based on an extension of the classical Lyapunov's direct method. As an important consequence, we conclude that the constructed NSFD schemes are dynamically consistent with respect to the positivity, boundedness and GAS of the continuous models. Finally, a set of numerical examples is conducted to support the theoretical findings and to demonstrate advantages of the NSFD schemes over some well-known standard ones. The numerical examples show that the used standard numerical schemes fail to preserve the qualitative dynamical properties of the continuous models for all finite step sizes; consequently, they can generate numerical approximations that are completely different from the exact solutions. Conversely, the NSFD schemes can provide reliable numerical solutions regardless of chosen step sizes.

Computer viruses: Security risks and solution

Computer viruses are malicious computer programs that can affect the working of a computer system, thus affecting important data and files. This study provides an overview of the history of computer viruses, their evolution, and the ways in which they spread and infect systems. It discusses the various security vulnerabilities that viruses exploit, such as software bugs and human errors, and the damage they can cause, such as data loss, system crashes, and identity theft. The study also analyzes various preventive measures that can be taken to minimize the risk of virus infection, such as software updates, firewalls, and anti-virus software. It also discusses the importance of user education and awareness for preventing virus attacks.

Modeling of Computer Virus Propagation with Fuzzy Parameters

Typically, a computer has infectivity as soon as it is infected. It is a reality that no antivirus programming can identify and eliminate all kinds of viruses, suggesting that infections would persevere on the Internet. To understand the dynamics of the virus propagation in a better way, a computer virus spread model with fuzzy parameters is presented in this work. It is assumed that all infected computers do not have the same contribution to the virus transmission process and each computer has a different degree of infectivity, which depends on the quantity of virus. Considering this, the parameters and being functions of the computer virus load, are considered fuzzy numbers. Using fuzzy theory helps us understand the spread of computer viruses more realistically as these parameters have fixed values in classical models. The essential features of the model, like reproduction number and equilibrium analysis, are discussed in fuzzy senses. Moreover, with fuzziness, two numerical methods, the forward Euler technique, and a nonstandard finite difference (NSFD) scheme, respectively, are developed and analyzed. In the evidence of the numerical simulations, the proposed NSFD method preserves the main features of the dynamic system. It can be considered a reliable tool to predict such types of solutions.