Spread Of Worms Research Articles

New computations of the fractional worms transmission model in wireless sensor network in view of new integral transform with statistical analysis; an analysis of information and communication technologies

Wireless sensor networks (WSNs) have attracted a lot of interest due to their enormous potential for both military and civilian uses. Worm attacks can quickly target WSNs because of the network's weak security. The worm can spread throughout the network by interacting with a single unsafe node. Moreover, the analysis of worm spread in WSNs can benefit from the use of mathematical epidemic models. We suggest a five-compartment model to characterize the mechanisms of worm proliferation with respect to time in WSN. Taking into account the ZZ transform convoluted with the Atangana-Baleanu-Caputo (ABC) fractional derivative operator, we employ it to analyze the characteristics and applications of the ZZ transformation using the Mittag-Leffler kernel. Moreover, we construct a new algorithm for the homotopy perturbation method (HPM) in conjunction with the ZZ transform technique to generate analytical solutions for the worm transmission model. Also, we address the qualitative aspects such as positivity, boundness, worm-free state, endemic state, basic reproduction number (R0) and worm-free equilibrium stability. Furthermore, we prove that the virus rate in sensor nodes is extinct if R0<1 and the virus persists if R0>1. In addition, we develop analytical findings to evaluate the series of solutions. Furthermore, a detailed statistical analysis is conducted to verify the nonlinear dynamics of the system by verifying the 0−1 test to determine whether uncertainty exists using approximation entropy and the C0 data. An extensive analysis of the vaccination class with respect to the transmitting class as well as the susceptible class is being used to investigate the effects of stepping up precautions on WP in WSN. Moreover, the modeling of the WSN revealed that reducing the fractional-order from 1 requires that the recommended approach be implemented at the highest rate so that there is no long-lasting immunization; instead, nodes remain briefly defensive before becoming vulnerable to future worm attacks.

Comprehensive analysis of a stochastic wireless sensor network motivated by Black-Karasinski process

Wireless sensor networks (WSNs) encounter a significant challenge in ensuring network security due to their operational constraints. This challenge stems from the potential infiltration of malware into WSNs, where a single infected node can rapidly propagate worms to neighboring nodes. To address this issue, this research introduces a stochastic SEIRS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{S}\ extsf{E}\ extsf{I}\ extsf{R}\ extsf{S}$$\\end{document} model to characterize worm spread in WSNs. Initially, we established that our model possesses a globally positive solution. Subsequently, we determine a threshold value for our stochastic system and derive a set of sufficient conditions that dictate the persistence or extinction of worm spread in WSNs based on the mean behavior. Our study reveals that environmental randomness can impede the spread of malware in WSNs. Moreover, by utilizing various parameter sets, we obtain approximate solutions that showcase these precise findings and validate the effectiveness of the proposed SEIRS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{S}\ extsf{E}\ extsf{I}\ extsf{R}\ extsf{S}$$\\end{document} model, which surpasses existing models in mitigating worm transmission in WSNs.

SIHQR model with time delay for worm spread analysis in IIoT-enabled PLC network

Industrial Internet of Things (IIoT) is a product of deep integration between Internet of Things (IoT) and industrial control networks. As an important component of IIoT, Programmable Logic Controller (PLC) has become a springboard for many hackers to disrupt industrial networks by spreading viruses. It is known that worm can cause enormous loss. Therefore, revealing the rules of worm spread in PLC networks and exploring methods to inhibit worm spread are increasingly important. To this end, we propose a new worm spread model named SIHQR with time delay. Additionally, we establish a game model between susceptible and infected nodes in order to express the infection rate by several game parameters. Through analysis, we discuss the conditions for the emergence of three scenarios: disease-free equilibrium, endemic equilibrium and Hopf bifurcation. We study the stability at the equilibrium points and obtain an expression for a threshold value which determines whether the system exhibits endemic equilibrium or Hopf bifurcation. In the experimental section, we analyze the impact of initial values of node states and the magnitude of game parameters on worm spread in the three aforementioned scenarios. Finally, we propose a method to mitigate or even eliminate the Hopf bifurcation. The experimental results show that the proposed model has welldone performance.

The impact of Caputo-Fabrizio fractional derivative and the dynamics of noise on worm propagation in wireless IoT networks

The main objective of this article on Wireless sensor network of the Internet of Things (IoT). The wireless network, B bluetooth network, infrared network, and other networks are the key components of the Internet of Things (IoT). The major emphasis of this work was on the impact of Caputo-Fabrizio fractional derivative on worm propagation in heterogeneous susceptible-exposed-infected-recovered wireless IoT devices. We first determined the equilibrium points and fundamental reproduction number for the Caputo-Fabrizio HSEIR system, and then we discussed the stability of the system at the worm propagation equilibrium point. Using the Picard-Lindeof method, we determine the existence and unique solution for the fractional CF system of the heterogeneous SEIR model. Next, we use fixed point theory to judge the stability of the iterative method. We investigate the impact of the derivative order on the behaviour of the resultant functions and acquired numerical values by computing the model's findings for various fractional orders. In addition, we compute the integer-order model's results and contrast them with the results of the fractional-order model. We develop a periodically intermittent controller driven by white noise with the amazing benefits of reduced cost and more adaptable control technique to restrict the spread of worms in wireless IoT networks. To clearly define the conditions for stability in probability one, we employ the stochastic analysis technique. Our results show that the nonlinear worm propagation system may be stabilised by intermittent stochastic perturbation under the parameters of intermittent time linked to stochastic perturbation strength. Our theoretical conclusions may be used to analyse the observable processes of the worm, design countermeasures to prevent its spread, and evaluate the consequences of various system parameters.

Lévy impact on the transmission of worms in wireless sensor network: Stochastic analysis

This manuscript addresses the security challenges wireless sensor networks (WSNs) face due to their operational limitations. The primary challenge stems from the infiltration of worms into the network, where one infected node could uncontrollably propagate the malware to neighboring node(s). First, we proposes a stochastic system based on Lévy noise to explain the spread of worms in WSNs. Then, we establish a unique positive global solution for the proposed model. We also examine the presence and potential extinction of worms within the networks. The results reveal that random environmental perturbations can confine the spread of worms and that the deterministic model tends to overestimate the worms’ spreading capacity. Using different parameter sets, the study obtains approximate solutions to validate these analytical findings and demonstrate the effectiveness of the suggested SEIRS system. The findings of the work reveal that the proposed model surpasses existing models in mitigating worm transmission in WSNs. Our inference suggests that the transmission dynamics of the system are influenced by both white noise and Lévy noise.

THE PREVALENCE OF INVASIVE DISEASES AMONG CATS IN URALSK

The frequent spread of intestinal worms among carnivorous animals is found in a number of diseases that are dangerous to the human body. Intestinal worming infection rates are high among stray cats and dogs as well as domesticated animals. In the research work, studies were conducted to differentiate intestinal worming in cats brought in from micro-districts of Uralsk, West Kazakhstan region. The results of the studies determined the extent of intestinal worming among cats, their species composition, seasonal dynamics of infections and the level of the age of the disease. The city of Uralsk revealed a widespread of cat helminth infections. (IE- 36.1%). By intestinal helminth infestation, the primary toxocariasis Toxocara mystax) is 19,7%, and by spread toxocariasis (Toxascaris leonina) is 12,4%. The degree of the extensiveness of infestation, depending on the time of the year, was determined; it was found that in summer and autumn, the infestation reaches a high degree of extensiveness. It was found that intestinal worm infestation in cats is associated with age. Toxocariasis is more common in 6-month-old cats. And with age, the incidence of this worming disease decreases significantly. In cats over five years old, this worming disease is sporadic.

From the Dialectical Perspective: Modeling and Exploiting of Hybrid Worm Propagation

The hierarchical network is the more effective platform, which provides multiple channels for various worm propagation. Thus, emerging worms can infect vulnerable hosts by scanning strategy and social media. However, the spread of scan-based worm is restrained due to uneven distribution of vulnerable hosts and NAT (Network Address Translation) technique. Meanwhile, topological dependency dictates to topology-based worm only infecting those hosts in social networks. To avoid their respective disadvantages, modern hybrid worm, which combines the above two propagation mechanisms, can implement efficient IP-address scanning by enhanced combination-scanning strategy, and spread more aggressively in social networks using enhanced reinfection mechanism. This paper presents a Hierarchical-Stochastic Propagation model to understand hybrid worm propagation. Inspired by hybrid worm, we design a new vaccine based on the Hierarchical-Measure Immunization strategy. For physical networking layer, we can estimate vulnerable-host distribution to find vulnerable hosts effectively through Maximum Likelihood estimation. For social networking layer, we use a novel propagation centrality measure to discover vital social nodes accurately. The experimental results show that our model can characterize the propagation mechanism of hybrid worms more comprehensively, and greatly outperforms state of the art models in terms of estimation accuracy. Meanwhile, our strategy is more effective to restrain the hybrid worm from spreading in networks.

Stability analysis and optimal control of worm propagation model with saturated incidence rate

Worms spreading through Web-based scanning and removable devices pose a serious threat to Internet security. This paper is devoted to solving the problems of computer worms. A nonlinear mathematical model of the problem is established, and the characteristics and mechanisms of worm propagation are analyzed by means of the stability theory of differential equations, optimal control and computer simulation. The results provide some new insights to computer security, which is to predict the tendency of worm propagation through the stability of endemic equilibrium, identify the epidemic control strategies by the stability of disease-free equilibrium, and evaluate the prevalence of worms based on the final scale of infected devices. The performance of our model is evaluated by numerical simulation, and the results indicate that our combined strategy can combat the worm propagation. The results may be helpful to inhibit the worm spread widely in the network.

A blockchain-empowered authentication scheme for worm detection in wireless sensor network

Wireless Sensor Network (WSN) is a distributed sensor network composed a large number of nodes with low cost, low performance and self-management. The special structure of WSN brings both convenience and vulnerability. For example, a malicious participant can launch attacks by capturing a physical device. Therefore, node authentication that can resist malicious attacks is very important to network security. Recently, blockchain technology has shown the potential to enhance the security of the Internet of Things (IoT). In this paper, we propose a Blockchain-empowered Authentication Scheme (BAS) for WSN. In our scheme, all nodes are managed by utilizing the identity information stored on the blockchain. Besides, the simulation experiment about worm detection is executed on BAS, and the security is evaluated from detection and infection rate. The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

Deep Learning CNN Framework for Detection and Classification of Internet Worms

A worm is a standalone program, which is self-replicating malware that distributes itself to other computers and networks. An Internet worm can spread across the network and infect millions of computers in truly little time and the damages caused from such attacks are considered extremely high. In addition, these worms also affect the network packet and its performance, where the packets are analyzed by the signature-based intrusion detection system (IDS) and the network performance is analyzed by the NetFlow based IDS. Hence, this article proposes a joint detection of both the signature based and NetFlow based Internet worms using deep learning convolution neural network (DLCNN) with respect to various attacks and it can also prevent the suspicious actions of attackers (cyber-criminals). Additionally, it provides the security for users’ data maintenance, countermeasures, and controls the spreading of the internet worms. The effectiveness of proposed DLCNN model is evaluated using both packet capture (PCAP) and KDD-CUP-99 datasets. Finally, various quality metrics are employed to disclose the superiority of proposed DLCNN model as compared existing machine learning, and back propagated neural network models.

Toward Robust Monitoring of Malicious Outbreaks

Recently, diffusion processes in social networks have attracted increasing attention within computer science, marketing science, social sciences, and political science. Although the majority of existing works focus on maximizing the reach of desirable diffusion processes, we are interested in deploying a group of monitors to detect malicious diffusion processes such as the spread of computer worms. In this work, we introduce and study the [Formula: see text]-Monitoring Game} on networks. Our game is composed of two parties an attacker and a defender. The attacker can launch an attack by distributing a limited number of seeds (i.e., virus) to the network. Under our [Formula: see text]-Monitoring Game, we say an attack is successful if and only if the following two conditions are satisfied: (1) the outbreak/propagation reaches at least α individuals without intervention, and (2) it has not been detected before reaching β individuals. Typically, we require that β is no larger than α in order to compensate the reaction delays after the outbreak has been detected. On the other end, the defender’s ultimate goal is to deploy a set of monitors in the network that can minimize attacker’s success ratio in the worst-case. (We also extend the basic model by considering a noisy diffusion model, where the propagation probabilities on each edge could vary within an interval.) Our work is built upon recent work in security games, our adversarial setting provides robust solutions in practice. Summary of Contribution: Although the diffusion processes in social networks have been extensively studied, most existing works aim at maximizing the reach of desirable diffusion processes. We are interested in deploying a group of monitors to detect malicious diffusion processes, such as the spread of computer worms. To capture the impact of model uncertainty, we consider a noisy diffusion model in which the propagation probabilities on each edge could vary within an interval. Our work is built upon recent work in security games; our adversarial setting leads to robust solutions in practice.

Worm computing: A blockchain-based resource sharing and cybersecurity framework

Recently, the rapid growth of network resources and the cybersecurity issues become more and more rigorous due to the rapid progress of Internet of Things (IoTs). How to integrate the network resources and enhance the defense capabilities effectively is an urgent problem. In this paper, inspired by the spread of friendly worms in the network, we put forward a novel concept of worm computing which is a decentralized computing platform. The worm computing model is able to integrate the useful information for resource sharing and cooperative defense, which can achieve the resource utilization refreshing, computing capacity improvement and network security enhancement. We use a blockchain platform to build a fundamental decentralization system and store collaborative data by delivering a private chain, which solves the problem of data sharing and trust computing in the traditional collaboration model by providing decentralized and credible services to ensure the unchangeable modification of transactions. We conduct experiments on resource utilization and collaborative defense of malicious URLs and the results validate the effectiveness of the proposed worm computing model in improving resource utilization and network security.

A Fractional SAIDR Model in the Frame of Atangana–Baleanu Derivative

It is possible to produce mobile phone worms, which are computer viruses with the ability to command the running of cell phones by taking advantage of their flaws, to be transmitted from one device to the other with increasing numbers. In our day, one of the services to gain currency for circulating these malignant worms is SMS. The distinctions of computers from mobile devices render the existing propagation models of computer worms unable to start operating instantaneously in the mobile network, and this is particularly valid for the SMS framework. The susceptible–affected–infectious–suspended–recovered model with a classical derivative (abbreviated as SAIDR) was coined by Xiao et al., (2017) in order to correctly estimate the spread of worms by means of SMS. This study is the first to implement an Atangana–Baleanu (AB) derivative in association with the fractional SAIDR model, depending upon the SAIDR model. The existence and uniqueness of the drinking model solutions together with the stability analysis are shown through the Banach fixed point theorem. The special solution of the model is investigated using the Laplace transformation and then we present a set of numeric graphics by varying the fractional-order θ with the intention of showing the effectiveness of the fractional derivative.

Dynamics and Control of Worm Epidemic Based on Mobile Networks by SEIQR-Type Model with Saturated Incidence Rate

The mobile networks have increasingly facilitated our daily life but are also breeding grounds for malicious worms, which are considered as the main threat to cyber security. The purpose of this paper is to analyze the dynamics of worm propagation and to control the worm epidemic based on mobile-phone networks. Accordingly, we establish an SEIQR-type model to explore the worm epidemic with saturated incidence rate. This paper shows that if the basic reproduction number is less than 1, the worm-free equilibrium is asymptotically stable, and the epidemic of worm will eventually disappear and remain under control; in addition, if the basic reproduction number is greater than 1, the asymptotical stability of worm-existence equilibrium is derived to imply that the epidemic will remain persistent and uncontrollable. Our results give new insights to mobile network security, namely, that is predicting the worm spreading tendency, identifying the epidemic control strategies, and estimating the worm popularity level. Numerical experiments are conducted to show the rationality of our obtained results and the effectiveness of the control strategies.

Influence of Clamor on the Transmission of Worms in Remote Sensor Network

Wireless Sensor Network (WSN) with remote sensing capability is gaining popularity in many of the real time applications such as military, healthcare, environment, home and other commercial applications.WSN is typically composed of various components such as sensor node, relay node, cluster head, gateway, base station. Such a critical network is vulnerable to most dangerous threats caused by worms towards the integrity and confidentiality of information passed through it. The study of the influence of clamor in propagation of potential of worm in WSN is of more significance. In this paper, a logical model is proposed that is reliant on pandemic theory. It is an improvement of the SIRS, SEIS and models. We propose an altered SEIRS (Susceptible-Exposed-Infectious-Recovered-Susceptible) model with the added substance background noise that overcomes the drawbacks of the existing models. The close by adequacy of the model has been affirmed using Lyapunov’s work. We similarly address the effect of node fluctuations in the model through numerical simulations that is carried out to prove that our proposed system is mean square stable and resistance against fluctuations with respect to the spread of worms.

Dynamics Stability and Optimal Control of Virus Propagation Based on the E-Mail Network

Currently, E-mail has become one of the most common communication methods in daily life, while Email virus constitutes one of the major Internet security threats and has attracted considerable attention. This paper addresses the optimal control problem when encountering out-breaks of E-mail virus. Based on the email network of North University of China, we establish a SEI email virus propagation model, study the influence of the heterogeneity of the email network on the worm virus propagation. We give a detail analysis for the model and the propagation threshold of system is obtained by stability theory, etc. Moreover, the effects of the heterogeneity of user for the spreading of worms are analyzed, and the optimal control theory is used to further study the time-varying control of worms. In final, the stability of system and the effectiveness of optimal control strategy are confirmed by numerical simulations.

An Improved Link Stability Based on Swift Exploring Packet Ratio Using Expected Time Matrix in Wireless Sensor Network

Wireless sensor networks become the integral part of network data transmission, to monitor information from a complex geographic range link may be failure due to transmission ratio. Expecting Power Saving, such a sensor network that has gained poor efficiency for the link broken, communication designed to be placed in a risk and non-access area, with wireless sensor networks playing an important in channel communication as stability of power. The Origin Challenge begins with a worm entering the wireless network to stabilize the link. The worm spreads to the entire network for infection in the link terminal. Mostly adjacent node as the affected nodes is rapidly blocked, and not infected. The target position is to route the target link when packets flow ground access to monitoring region is blocked, a solution is to send, the remote sensor. To propose an efficient method of Link stability based on swift exploring packet ratio using expected time matrix in wireless sensor network (SEPR-ETC). Improvement of the target coverage probability should be accomplished by accurate sensor arrangement, loss of large gait link density in the drop zone improved by estimating. The data collected from the sensors are sent to the central node for processing to cover the need to be constantly operated the communication Link. Swift state use the packet flow to monitor the network resources through good cooperative communication to reduce the amount of data that needs to be transmitted collectively, while the wireless sensor networks are in the industry to reduce bit energy consumption. The proposed SEPR-ETC model provides an advanced technique for controlling link exploring transmission compared to the existing model.

Impulsive Switching Epidemic Model with Benign Worm Defense and Quarantine Strategy

The issue on how to effectively control Internet malicious worms has been drawn significant attention owing to enormous threats to the Internet. Due to the rapid spreading of malicious worms, it is necessary to explore the integrated measures to automatically mitigate the propagation on the Internet. In this paper, a novel worm propagation model is established, which combines both impulsive quarantine and benign worm implementation. Then, sufficient conditions for the global stability of worm-free periodic solution and the permanence of the benign worm are obtained. Finally, the effects of quarantine strategy are assessed and some feasible strategies that can constrain the propagation of malicious worm are provided by numerical simulation.

Countermeasures against Worm Spreading

Vehicular ad hoc networks (VANETs) are essential components of the intelligent transport systems. They are attracting an increasing amount of interest in research and industrial sectors. Vehicular nodes are capable of transporting, sensing, processing information, and wireless communication, which makes them more vulnerable to worm infections than conventional hosts. This survey provides an overview on worm spreading over VANETs. We first briefly introduce the computer worms. Then the V2X communication and applications are discussed from malware and worms propagation perspective to show the indispensability of studying the characteristics of worm propagating on VANETs. The recent literature on worm spreading and containment on VANETs are categorized based on their research methods. The improvements and limitations of the existing studies are discussed. Next, the main factors influencing worm spreading in vehicular networks are discussed followed by a summary of countermeasure strategies designed to deal with these worms.

ERES: an extended regular expression signature for polymorphic worm detection

The quick spreading of modern sophisticated polymorphic worms poses a serious threat to the internet security. So far, several signature classes have been proposed to face this challenge. Although taking patterns such as 1-byte invariants and distance restrictions into signature classes are considered in the previous schemes, they do not consider the set of specific values, which are important in the successful execution of a worm. In this paper, we introduce a new signature type, called ERES (Extended Regular Expression Signature). By considering all the above specifications, along with the probability of being a worm, in the worm signature, ERES generates a more specific signature leading to a more accurate detection. In addition, to accelerate the signature extraction process, it combines token extraction with sequence alignment. Evaluations based on multiple polymorphic worms demonstrate that this approach is more acceptable in terms of speed, accuracy, and noise tolerance.