Analysis of Epidemic Model for Performance Evaluation of Rechargeable Wireless Sensor Network

Wireless sensor network (WSN) is a decentralized network system which consists of sensor nodes, and these nodes are connected through wireless link. Due to decentralized network system and resource constraint WSN faces security threat. Malware (malicious signals, worm, Trajan horse, virus etc.) attacks on the sensor node of WSN and make them paralyze and steal information from the network. Malware attack also increases the energy consumption of Sensor nodes of WSN. It just begins to spread from an infected node, and spread across the entire WSN with the help of neighboring nodes. Therefore, security of WSN against attack of malware is an inescapable need. On the basis of earlier works and consideration of charging mechanism of sensor nodes, and considering the effect of coverage and connectivity, proposed a SILRD (Susceptible - Infectious – Low Energy – Recovered –Dead) model with vital dynamics. The propose model investigates the dynamics of malware propagation in WSN and also explain sensor node’s energy consumption. The system’s stability has analyzed in terms of local and global of malware-free and endemic equilibrium. For the investigation of system dynamics, the expression of basic reproduction number has computed, which is also utilized to analyze state of malware in WSN. The effect of charging, coverage and connectivity is explained in this paper.

Investigating Malware Propagation and Behaviour Using System and Network Pixel-Based Visualisation

Malicious software, known as malware, is a perpetual game of cat and mouse between malicious software developers and security professionals. Recent years have seen many high profile cyber attacks, including the WannaCry and NotPetya ransomware attacks that resulted in major financial damages to many businesses and institutions. Understanding the characteristics of such malware, including how malware can propagate and interact between systems and networks is key for mitigating these threats and containing the infection to avoid further damage. In this study, we present visualisation techniques for understanding the propagation characteristics in dynamic malware analysis. We propose the use of pixel-based visualisations to convey large-scale complex information about network hosts in a scalable and informative manner. We demonstrate our approach using a virtualised network environment, whereby we can deploy malware variants and observe their propagation behaviours. As a novel form of visualising system and network activity data across a complex environment, we can begin to understand visual signatures that can help analysts identify key characteristics of the malicious behaviours, and, therefore, provoke response and mitigation against such attacks.

Modeling and Analysis of the Spread of Malware with the Influence of User Awareness

By incorporating the security awareness of computer users into the susceptible-infected-susceptible (SIS) model, this study proposes a new malware propagation model, named the SID model, where D compartment denotes the group of nodes with user awareness. Through qualitative analysis, the basic reproductive number R 0 is given. Furthermore, it is proved that the virus-free equilibrium is globally asymptotically stable if R 0 is less than one, whereas the viral equilibrium is globally asymptotically stable if R 0 is greater than one. Then, some numerical examples are given to demonstrate the analytical results. Finally, we put forward some efficient control measures according to the theoretical and experimental analysis.

Malware propagation model for cluster-based wireless sensor networks using epidemiological theory

Due to limited resources, wireless sensor network (WSN) nodes generally possess weak defense capabilities and are often the target of malware attacks. Attackers can capture or infect specific sensor nodes and propagate malware to other sensor nodes in WSNs through node communication. This can eventually infect an entire network system and even cause paralysis. Based on epidemiological theory, the present study proposes a malware propagation model suitable for cluster-based WSNs to analyze the propagation dynamic of malware. The model focuses on the data-transmission characteristics between different nodes in a cluster-based network and considers the actual application parameters of WSNs, such as node communication radius, node distributed density, and node death rate. In addition, an attack and defense game between malware and defending systems is also established, and the infection and recovery rates of malware propagation under the mixed strategy Nash equilibrium condition are given. In particular, the basic reproductive number, equilibrium point, and stability of the model are derived. These studies revealed that a basic reproductive number of less than 1 leads to eventual disappearance of malware, which provides significant insight into the design of defense strategies against malware threats. Numerical experiments were conducted to validate the theory proposed, and the influence of WSN parameters on malware propagation was examined.

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.

Identifying the Author Group of Malwares through Graph Embedding and Human-in-the-Loop Classification

Malware are developed for various types of malicious attacks, e.g., to gain access to a user’s private information or control of the computer system. The identification and classification of malware has been extensively studied in academic societies and many companies. Beyond the traditional research areas in this field, including malware detection, malware propagation analysis, and malware family clustering, this paper focuses on identifying the “author group” of a given malware as a means of effective detection and prevention of further malware threats, along with providing evidence for proper legal action. Our framework consists of a malware-feature bipartite graph construction, malware embedding based on DeepWalk, and classification of the target malware based on the k-nearest neighbors (KNN) classification. However, our KNN classifier often faced ambiguous cases, where it should say “I don’t know” rather than attempting to predict something with a high risk of misclassification. Therefore, our framework allows human experts to intervene in the process of classification for the final decision. We also developed a graphical user interface that provides the points of ambiguity for helping human experts to effectively determine the author group of the target malware. We demonstrated the effectiveness of our human-in-the-loop classification framework via extensive experiments using real-world malware data.

A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks

A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.