Epidemic Models of Malicious-Code Propagation and Control in Wireless Sensor Networks: An Indepth Review

Nwokoye, ChukwuNonso H.; Madhusudanan, V.

doi:10.1007/s11277-022-09636-8

Cited by 14 publications

(5 citation statements)

References 83 publications

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“…Each network node consists of a singleboard computer Raspberry Pi version 3 or 2, connected sensors (temperature, pressure, humidity, etc.) and a Digi XBee communication module [28,29]. The main characteristics of the nodes are presented in Table 7.…”

Section: Experiments and Discussionmentioning

confidence: 99%

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The Modeling and Detection of Attacks in Role-Based Self-Organized Decentralized Wireless Sensor Networks

Meleshko,

Desnitsky

2024

Telecom

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This article discusses the modeling and detection of attacks in self-organizing decentralized wireless sensor networks (WSNs) that can be applied to various critical scenarios in practice. Security issues in this type of network have previously been studied to a rather poor extent. In particular, existing attack detection approaches and algorithms do not rely on the properties of self-organization and decentralization, which an attacker is able to exploit to compromise the network and its services. We propose, first, a model of a self-organizing decentralized wireless sensor network; second, a model of the attacks on such networks; third, algorithms for data collection and attack detection; and, finally, a technique for their application. The WSN model represents a formal specification of this type of network, defining the conditions and limitations of network self-organization and decentralization. The model is characterized by a proposed underlying role-based operation of network nodes and a set of their functional states. The proposed attack model covers the possible types of attacks that are relevant to a given type of WSN and are based on the exploitation of the self-organization and decentralization of the network. The developed algorithm for collecting data for attack detection presents specific types of data and their sources. The developed combined attack detection algorithm is formed of actions that detect relevant attacks on self-organizing decentralized WSNs using machine learning methods. The distinctive element of this algorithm is a set of highly specific features that are obtained by analyzing the data collected in the WSN and used to detect attacks. The proposed technique combines the constructed models and algorithms for the sake of tuning and deploying the attack detection module and the effective detection of attacks in practice. This technique specifies the main steps for the joint use of the models and algorithms and the assignment of data collection and detection parameters. The results of the experiments confirm the correctness of the constructed models, algorithms and technique due to the high values of the attack detection quality indicators. Therefore, the practical application of the proposed apparatus will facilitate improvements in the security of self-organizing decentralized WSNs. Experimental research has confirmed the practical applicability of our proposed solutions. In particular, it has shown that the proposed algorithms and the detection technique can detect both attacks implemented through the exploitation of the network’s properties of decentralization/self-organization and common variations in these attacks (i.e., without exploiting the decentralization property). In general, the experimental results expose a high quality of detection, with an f1-score equal to 0.99.

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Section: Experiments and Discussionmentioning

confidence: 99%

“…A malicious code injection attack is a type of attack that exploits errors in software and injects malicious code into the host control program, for example, by injecting code into a data packet [28,29]. The goal is to disrupt the operation algorithms of the node.…”

Section: Attack Modelmentioning

confidence: 99%

The Modeling and Detection of Attacks in Role-Based Self-Organized Decentralized Wireless Sensor Networks

Meleshko,

Desnitsky

2024

Telecom

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“…A critical evaluation of the above-given models shows that besides considering the deterministic SLB model with their delay analysis, this work presents its SLB's stochastic version, wherein the impact of white noise is studied. Our model also includes the patch (P) compartment that prevents the inundation [26] of the computer network as a result of virus infestation.…”

Section: Related Literaturementioning

confidence: 99%

An Epidemic Patch-Enabled Delayed Model for Virus Propagation: Towards Evaluating Bifurcation and White Noise

Liu

Madhusudanan²,

Srinivas

et al. 2022

Mathematical Problems in Engineering

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The massive disruptions caused by malware, such as a virus in computer networks and other aspects of information and communication technology, have generated attention, making it a hot research topic. While antivirus and firewalls can be effective, there is also a need to understand the spread patterns of viral infection using epidemic models to curb its incidences. Many previous research attempts have produced analytical models for computer viruses under various infectiousness situations. As a result, we suggested the SLBS model, which considers infection latency and transient immunity in patched nodes. Under certain conditions, the local stability of all equilibrium points is investigated. By setting the delay parameter, we established the occurrence of a Hopf bifurcation (HB) as it crossed a crucial point by several analyses. We also used the centre manifold theorem and normal form theory to examine the attributes of the HB. While the former was used to study the time delay and direction of Hopf bifurcation, the latter was used to investigate external noise and its intensities. Finally, numerical simulations two dimensional and three-dimensional graphs were used to depict the perturbations of the model, thus bolstering the essentiality of the study.

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“…Particularly interesting are those dedicated to study the propagation of malicious code on wireless sensor networks (WSNs), which constitute the foundation for the development and implementation of IoT networks. The vast majority of these models are of a global nature (see, for example, [10][11][12][13][14] and the references therein), and their dynamics are described by continuous mathematical techniques, such as systems of ordinary differential equations. These are compartmental models (that is, the device population is divided into different classes or compartments: susceptible, infectious, recovered, etc.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of an Individual-Based Model for Malware Propagation on IoT Networks

Martín del Rey

2023

Mathematics

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The main goal of this work is to propose a novel compartmental SEA (Susceptible–Exposed–Attacked) model to simulate malware spreading on an IoT (Internet of Things) network. This is a deterministic and individual-based model, whose main novelty compared to others lies in the used of continuous mathematical techniques, such as ordinary differential equations, in the description of local transition rules that define the changes of the states of the devices. These states are given by probability vectors representing the probabilities of being susceptible, exposed and attacked at each step of time. The qualitative study of the model is presented, and several simulations are performed.

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Epidemic Models of Malicious-Code Propagation and Control in Wireless Sensor Networks: An Indepth Review

Cited by 14 publications

References 83 publications

The Modeling and Detection of Attacks in Role-Based Self-Organized Decentralized Wireless Sensor Networks

The Modeling and Detection of Attacks in Role-Based Self-Organized Decentralized Wireless Sensor Networks

An Epidemic Patch-Enabled Delayed Model for Virus Propagation: Towards Evaluating Bifurcation and White Noise

Design and Analysis of an Individual-Based Model for Malware Propagation on IoT Networks

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