Malware Propagation Model Research Articles

A Delayed Malware Propagation Model for Cloud Computing Security

A Delayed Malware Propagation Model for Cloud Computing Security

A clustered-LPSEIRS malware propagation model in complex networks

In this paper, we present a new malware propagation model that integrates epidemic spread, clustering, and link prediction techniques, tailored for complex network networks. Our model is based on the clustered-link prediction-susceptible-exposed-infected-recovered (clustered-LPSEIRS) epidemic model, which simulates malware dissemination within the network. Our findings reveal a significant decrease in the rate of malware spread compared to the traditional SEIR model, with this enhancement in containment attributed to the integration of clustering and link prediction methods. We also compute the basic reproduction ratio (R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_{0}$$\\end{document}) for our model, providing insights into the potential ramifications of malware within the network. By examining parameter variations, we enhance our understanding of the model's behavior under diverse scenarios. Additionally, we assess the influence of clustering and link prediction on mitigating malware spread, emphasizing its effectiveness in diminishing the overall impact.

Modeling of Malware Propagation Under the Clustering Approach in Scale‐Free Networks

ABSTRACTIn this paper, we present an innovative malware propagation model that combines epidemic spread and clustering techniques, specifically designed for scale‐free networks. The proposed model builds upon the susceptible–exposed–infected–recovered (SEIR) framework, effectively simulating the dissemination of malware across the network. Our research demonstrates a notable reduction in the rate of malware transmission compared to the traditional SEIR model, with this improvement in containment being attributed to the incorporation of clustering methodologies. We also calculate the basic reproduction ratio (R0) for the proposed model, which offers valuable insights into the potential consequences of malware outbreaks within the network. By exploring variations in key parameters, we deepen our understanding of the model's behavior under different conditions. Furthermore, we evaluate the role of clustering in mitigating malware spread, highlighting its significant effectiveness in reducing the overall impact of infections.

Multi-agent reinforcement learning based algorithm detection of malware-infected nodes in IoT networks

Abstract The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in the network. Through the use of a multi-agent reinforcement learning algorithm, this paper proposes a novel algorithm for malware detection in IoT devices. The proposed algorithm is not strongly dependent on the size of the IoT network due to the that its training is adapted using time differences if the IoT network size is small or Monte Carlo otherwise. To validate the proposed algorithm in an environment as close to reality as possible, we proposed a scenario based on a real IoT network, where we tested different malware propagation models. Different simulations varying the number of agents and nodes in the IoT network have been developed. The result of these simulations proves the efficiency and adaptability of the proposed algorithm in detecting malware, regardless of the malware propagation model.

Delayed dynamics analysis of SEI2RS malware propagation models in cyber–Physical systems

Cyber-physical systems facilitate seamless interaction between the physical and digital elements for improved efficiency, automation, and real-time monitoring across domains. This study analyzes a novel virus-spreading model called the delayed SEI2RS model, which is specifically designed for cyber-physical systems. This model incorporates a saturated incidence rate and treatment. An emphasis of this research is to explore the impact of time delay on the transient immunity interval of restored nodes. By using the time delay associated with the transitory immunity interval of recovered nodes as the bifurcation parameter, we derive a comprehensive set of appropriate conditions to assess the local stability of the malware-existence equilibrium and determine Hopf bifurcation. The center manifold theorem and normal form theory are employed to investigate the path and stability of Hopf bifurcation. Numerical calculations were used to validate the results, providing empirical evidence for the proposed model and its implications.

Interconnected Takagi-Sugeno system and fractional SIRS malware propagation model for stabilization of Wireless Sensor Networks

This paper is devoted to investigate the dynamical behaviors of malware attack on a familiar kind of complex heterogeneous networks, namely Wireless Sensor Network, and discuss an effective immunization treatment based on fractional interconnected Takagi-Sugeno (T-S) fuzzy systems. Our approach is based on the mathematical modeling to establish a controlled fractional network-based SIRS malware propagation model that better describes the attacking behavior of malicious objects on Wireless Sensor Network. After that, we point out some qualitative properties of the proposed network-based SIRS malware propagation model such as the existence of positively invariant set, backward bifurcation and asymptotic behavior. Especially, in order to study the model's stability, we evaluate an epidemiological threshold value R0, namely basic reproductive ratio, which ensures the existence of at least one endemic equilibrium P⁎ and the local asymptotic stability of malware-free equilibrium P0. As a consequence of theoretical result, the malware-free equilibrium P0 is unstable when R0>1 and hence, the rest of this paper is to address a stabilization problem for the proposed controlled network-based model and establish some sufficient conditions related to linear matrix inequalities and positive definite matrices. Finally, we illustrate the obtained theoretical results by a computational example.

Impact of cybersecurity awareness on mobile malware propagation: A dynamical model

Despite the recent increase in mass media attention to mobile malware, it is still one of the major mobile security threats due to the lack of cybersecurity awareness. The compartment mode can divide the individuals into different states whose name is often a combination of the state names. To explore the impact of the security awareness level of users on susceptible and infected mobile devices, we propose a novel Unaware-Susceptible Aware-Susceptible Latent Breakout Quarantine Recovery (SuSaLBQR) compartment model for mobile malware propagation. In this model, the susceptible mobile devices are divided into two sub-classes: the Su compartment and the Sa compartment depending on the security awareness level of their corresponding users. Based on some reasonable assumptions, a dynamical system is proposed to describe mobile malware propagation behaviors. To explore how malware ultimately spreads, two distinct equilibria (i.e., the malware-spreading and malware-free equilibria) are obtained. Furthermore, the global asymptotic stabilities of those two equilibria are proved by constructing the Lyapunov functions, respectively. The above analysis can provide a theoretical basis for controlling mobile malware propagation. Finally, based on the theoretical and numerical results, a discussion is provided. We find that the above two factors can significantly affect the presence and prevalence of malware in mobile networks.

Complex pattern evolution of a two-dimensional space diffusion model of malware spread

In order to investigate the propagation mechanism of malware in cyber-physical systems (CPSs), the cross-diffusion in two-dimensional space is attempted to be introduced into a class of susceptible-infected (SI) malware propagation model depicted by partial differential equations (PDEs). Most of the traditional reaction-diffusion models of malware propagation only take into account the self-diffusion in one-dimensional space, but take less consideration of the cross-diffusion in two-dimensional space. This paper investigates the spatial diffusion behaviour of malware nodes spreading through physical devices. The formations of Turing patterns after homogeneous stationary instability triggered by Turing bifurcation are investigated by linear stability analysis and multiscale analysis methods. The conditions under the occurence of Hopf bifurcation and Turing bifurcation in the malware model are obtained. The amplitude equations are derived in the vicinity of the bifurcation point to explore the conditions for the formation of Turing patterns in two-dimensional space. And the corresponding patterns are obtained by varying the control parameters. It is shown that malicious virus nodes spread in different forms including hexagons, stripes and a mixture of the two. This paper will extend a new direction for the study of system security theory.

How to regulate pattern formations for malware propagation in cyber-physical systems.

Malware propagation can be fatal to cyber-physical systems. How to detect and prevent the spatiotemporal evolution of malware is the major challenge we are facing now. This paper is concerned with the control of Turing patterns arising in a malware propagation model depicted by partial differential equations for the first time. From the control theoretic perspective, the goal is not only to predict the formation and evolution of patterns but also to design the spatiotemporal state feedback scheme to modulate the switch of patterns between different modes. The Turing instability conditions are obtained for the controlled malware propagation model with cross-diffusion. Then, the multi-scale analysis is carried out to explore the amplitude equations near the threshold of Turing bifurcation. The selection and stability of pattern formations are determined based on the established amplitude equations. It is proved that the reaction-diffusion propagation model has three types of patterns: hexagonal pattern, striped pattern, and mixed pattern, and selecting the appropriate control parameters can make the pattern transform among the three patterns. The results of the analysis are numerically verified and provide valuable insights into dynamics and control of patterns embedded in reaction-diffusion systems.

A novel model for malware propagation on wireless sensor networks.

The main goal of this work was to propose a novel mathematical model for malware propagation on wireless sensor networks (WSN). Specifically, the proposed model was a compartmental and global one whose temporal dynamics were described by means of a system of ordinary differential equations. This proposal was more realistic than others that have appeared in the scientific literature since. On the one hand, considering the specifications of malicious code propagation, several types of nodes were considered (susceptible, patched susceptible, latent non-infectious, latent infectious, compromised non-infectious, compromised infectious, damaged, ad deactivated), and on the other hand, a new and more realistic term of the incidence was defined and used based on some particular characteristics of transmission protocol on wireless sensor networks.

A new Takagi-Sugeno fuzzy system approach for fuzzy state feedback controller design and its application to malware propagation on heterogeneous complex network

Abstract— Nowadays, digital transformation has brought many great changes and is becoming an essential part of real life, however, it also goes along with a considerable likelihood of being targeted in cyberattack. Indeed, the more businesses embrace digital transformation or use online services, the more opportunities hackers have to expand their cyberattacks. Hence, there is an essential need on analyzing and predicting of cyberattack on network systems. For this aim, we propose to study a controlled fractional network-based SCIRS (Susceptible - Carrier - Infectious - Recovered - Susceptible) malware propagation model and its stabilization problem based on fractional interconnected Takagi-Sugeno fuzzy system. A fuzzy state feedback controller is proposed to asymptotically stabilize the unstable malware-free equilibrium of the proposed malware propagation model and then, we establish sufficient conditions in terms of linear matrix inequalities. The effectiveness of proposed approach is illustrated by a case study of SCIRS malware propagation model on heterogeneous complex network.

A Malware Propagation Model with Dual Delay in the Industrial Control Network

The malware attacks targeting the industrial control network are gradually increasing, and the nonlinear phenomenon makes it difficult to predict the propagation behavior of malware. Once the dynamic system becomes unstable, the propagation of malware will be out of control, which will seriously threaten the security of the industrial control network. So, it is necessary to model and study the propagation of malware in the industrial control network. In this paper, a SIDQR model with dual delay is proposed by fully considering the characteristics of the industrial control network. By analyzing the nonlinear dynamics of the model, the Hopf bifurcation is discussed in detail when the value of dual delay is greater than zero, and the expression for the threshold is also provided. The results of the experiments indicate that the system may have multiple bifurcation points. By comparing different immune and quarantine rates, it is found that the immune rate can be appropriately increased and the isolation rate can be appropriately reduced in the industrial control network, which can suppress the spread of malware without interrupting the industrial production.

A Comparison of Monte Carlo-Based and PINN Parameter Estimation Methods for Malware Identification in IoT Networks

Malware propagation is a growing concern due to its potential impact on the security and integrity of connected devices in Internet of Things (IoT) network environments. This study investigates parameter estimation for Susceptible-Infectious-Recovered (SIR) and Susceptible–Infectious–Recovered–Susceptible (SIRS) models modeling malware propagation in an IoT network. Synthetic data of malware propagation in the IoT network is generated and a comprehensive comparison is made between two approaches: algorithms based on Monte Carlo methods and Physics-Informed Neural Networks (PINNs). The results show that, based on the infection curve measured in the IoT network, both methods are able to provide accurate estimates of the parameters of the malware propagation model. Furthermore, the results show that the choice of the appropriate method depends on the dynamics of the spreading malware and computational constraints. This work highlights the importance of considering both classical and AI-based approaches and provides a basis for future research on parameter estimation in epidemiological models applied to malware propagation in IoT networks.

Modeling and Analysis of Malware Propagation for IoT Heterogeneous Devices

With the rapid development and application of Internet of Things (IoT) technology, a large number of various types of IoT devices with security vulnerabilities have been connected to the public network. Attackers only need to use malware to successfully infect a few types of IoT devices to generate large-scale propagation in the network and capture a large number of broilers, which in turn lays the foundation for subsequent attacks. In this study, an innovative malware propagation model [i.e., IoT-susceptible-latent-propagated-recovered (IoT-SLPR)] based on epidemiological theory is developed for malware propagation in multiple IoT heterogeneous devices. The proposed model incorporates the propagation mechanism of malware in real IoT networks, and considers heterogeneous devices with different infection rates and recovery rates. This study calculates and proves the basic reproductive rate of malware and the stability of equilibrium points for different types of devices. In addition, this study designs a dynamic recovery rate optimal defense strategy, which comprehensively considers the cost and the overall infection rate of the device. The experimental results show that the proposed propagation model can effectively reflect the propagation dynamics of malware in multiple heterogeneous devices, and the achieved dynamic recovery rate defense strategy performs better than the static strategy regarding the overall results.

Malware propagation model of fractional order, optimal control strategy and simulations

In this paper, an improved SEIR model of fractional order is investigated to describe the behavior of malware propagation in the wireless sensor network. Firstly, the malware propagation model of fractional order is established based on the classical SEIR epidemic theory, the basic reproductive number is obtained by the next-generation method and the stability condition of the model is also analyzed. Then, the inverse approach for the uncertainty propagation based on the discrete element method and least square algorithm is presented to determine the unknown parameters of the propagation process. Finally, the optimal control strategy is also discussed based on the adaptive model. Simulation results show the proposed model works better than the propagation model of integer order. The error of proposed model is smaller than integer order models.

Dynamical behaviors of an epidemic model for malware propagation in wireless sensor networks

To explore malware propagation mechanisms in networks and to develop optimal strategies for controlling the spread of malware, we propose a susceptible-unexposed-infected-isolation-removed epidemic model. First, we establish a non-linear dynamic equation of malware propagation. Then, the basic reproductive number is derived by using the next-generation method. Finally, we carry out numerical simulations to observe the malware spreading in WSNs to verify the obtained theoretical results. Furthermore, we investigate the communication range of the nodes to make the results more complete. The optimal range of the nodes is designed to control malware propagation.

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.

Generalized Defensive Modeling of Malware Propagation in WSNs Using Atangana–Baleanu– Caputo (ABC) Fractional Derivative

The malware spreading in Wireless Sensor Network (WSN) has lately attracted the attention of many researchers as a hot problem in nonlinear systems. WSN is a collection of sensor nodes that communicate with each other wirelessly. These nodes are linked in a decentralised and distributed structure, allowing for efficient data collection and communication. Due to their decentralised architecture and limited resources, WSN is vulnerable to security risks, including malware attacks. Malware can attack sensor nodes, causing them to malfunction and consume more energy. These attacks can spread from one infected node to others in the network, making it essential to protect WSN against malware attacks. In this paper, we focus on the analysis of a novel fractional epidemiology model, specifically the fractional order SEIVR epidemic model in the sense of Caputo’s fractional derivative of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0{ &lt; {\alpha }{\leq }}1$ </tex-math></inline-formula> with the goal of examining the efficacy of vaccination strategies and the heterogeneity of a scale-free network on epidemic spreading. First, using the next-generation technique and obtain the basic reproduction number of the proposed epidemic model, which is essential for determining both the locally asymptotically stable equilibrium point of the worm-free system and the unique existence of the endemic equilibrium point. To numerically solve the model, the Adam-Bashforth-Moulton predictor-corrector (ABM) method is applied. The fractional calculus enables us to deal directly with the “memory effect” of numerous phenomena, taking into account the system’s dependence on previous stages. This method provides the results of a complex system. Additionally, research demonstrates that vaccine treatments are quite effective at preventing the spread of malware. The outcome of the study reveals that the applied ABM predictor-corrector method is computationally strong and effective to analyse fractional order dynamical systems in the SEIVR epidemic model for malware propagation in WSN. The results show that the order of the fractional derivative has a significant effect on the dynamic process. Also, from the result, it is obvious that the memory effect is zero for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\alpha }$ </tex-math></inline-formula> = 1. When the fractional order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\alpha }$ </tex-math></inline-formula> is decreased from 1, the memory effect appears, and its dynamics vary according to the time. This memory effect points out the difference between derivatives of fractional and integer orders. The theorems and their proofs are presented to validate the validity of the proposed model. To validate the proposed model, extensive theoretical study and computational analysis have also been applied.

A Malware Propagation Model Considering Conformity Psychology in Social Networks

At present, malware is still a major security threat to computer networks. However, only a fraction of users with some security consciousness take security measures to protect computers on their own initiative, and others who know the current situation through social networks usually follow suit. This phenomenon is referred to as conformity psychology. It is obvious that more users will take countermeasures to prevent computers from being infected if the malware spreads to a certain extent. This paper proposes a deterministic nonlinear SEIQR propagation model to investigate the impact of conformity psychology on malware propagation. Both the local and global stabilities of malware-free equilibrium are proven while the existence and local stability of endemic equilibrium is proven by using the central manifold theory. Additionally, some numerical examples and simulation experiments based on two network datasets are performed to verify the theoretical analysis results. Finally, the sensitivity analysis of system parameters is carried out.

New continuous-discrete model for wireless sensor networks security

A wireless sensor network (WSN) is a group of "smart" sensors with a wireless infrastructure designed to monitor the environment. This technology is the basic concept of the Internet of Things (IoT). The WSN can transmit confidential information while working in an insecure environment. Therefore, appropriate security measures must be considered in the network design. However, computational node constraints, limited storage space, an unstable power supply, and unreliable communication channels, and unattended operations are significant barriers to the application of cybersecurity techniques in these networks. This paper considers a new continuous-discrete model of malware propagation through wireless sensor network nodes, which is based on a system of so-called dynamic equations with impulsive effect on time scales.