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<p>With the advent of new technologies, the world is moving very fast and interconnecting the various types of devices anywhere, any time, and any location. This interconnection of devices builds a network that is known as the Internet of Things (IoT). This is a dynamic and complex network system. The readers, RFID tags, actuators, and sensors are the components of IoT, which empower the linkage between the virtual and physical worlds. The increasing pervasiveness of IoT introduces numerous operational problems, one of which is malware (bot) attacks. Due to the attack of the bot, IoT infrastructure poses an existential threat. The number of security challenges present in the IoT network because of the absence of regulatory standards, inherent security weaknesses, and another reason is the lack of security knowledge amongst the customers. The botmasters exploit the vulnerabilities of IoT and install a malware on a node of the network; that node gets compromised. After that, the compromised node spreads malwares over the entire network. The malware in the IoT spreads from one host to another host, like the biological infectious diseases that spread in the vulnerable population. One of the factors that also influence the spreading ability of the bots in IoT is the availability of memory. Therefore, for the protection of IoT from the attack of malware, it is necessary to understand the spreading dynamics of malware in the IoT. For the study of dynamic spreading characteristics of malware in the IoT, a mathematical model is proposed. The proposed model overcomes the deficiencies of the existing IoT-SIAF and IoT-SIA models with the inclusion of the Exposed and Forensic states. The proposed model is named the IoT-Susceptible-Exposed-Infectious-Abandon-Forensic (IoT-SEIAF) model. The exposed state assists in the detection of abnormal behaviour of nodes of the IoT at an early stage when the malware attack occurs. The devices in the IoT network are memory-efficient, so the malware attack poses important apprehension for forensic analysis. The variability and data instability of IoT nodes pose challenges in forecasting the object of forensic interest (OOFI) in resource-limited devices, including sensor nodes. The diffusion of data among adjacent nodes presents significant challenges for forensic analysis. Incorporating the forensic state into the proposed model is essential because it aids in gathering the object of forensic interest at the investigation site and facilitates the recovery of digital evidence. The basic reproduction number of the proposed model is derived, serving as a crucial metric for analyzing malware propagation dynamics in IoT, and the local and endemic equilibrium points of the system are examined. The proposed model has been juxtaposed with the preceding paradigm. The mathematical and simulation results confirm that the proposed IoT-SEIAF model demonstrates an improved strategy for preventing malware propagation compared to the prior models. The proposed IoT-SEIAF model demonstrates an amelioration technique to stop malware transmission.</p>

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Impact of Abandon and Forensic State on the Stability of Internet of Things Against Malware Attacks

  • 期刊论文

  • 发表日期：

    2026年4月18日

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  • Om Prakash,
  • Avadhesh Kumar,
  • Rudra Pratap Ojha

摘要

With the advent of new technologies, the world is moving very fast and interconnecting the various types of devices anywhere, any time, and any location. This interconnection of devices builds a network that is known as the Internet of Things (IoT). This is a dynamic and complex network system. The readers, RFID tags, actuators, and sensors are the components of IoT, which empower the linkage between the virtual and physical worlds. The increasing pervasiveness of IoT introduces numerous operational problems, one of which is malware (bot) attacks. Due to the attack of the bot, IoT infrastructure poses an existential threat. The number of security challenges present in the IoT network because of the absence of regulatory standards, inherent security weaknesses, and another reason is the lack of security knowledge amongst the customers. The botmasters exploit the vulnerabilities of IoT and install a malware on a node of the network; that node gets compromised. After that, the compromised node spreads malwares over the entire network. The malware in the IoT spreads from one host to another host, like the biological infectious diseases that spread in the vulnerable population. One of the factors that also influence the spreading ability of the bots in IoT is the availability of memory. Therefore, for the protection of IoT from the attack of malware, it is necessary to understand the spreading dynamics of malware in the IoT. For the study of dynamic spreading characteristics of malware in the IoT, a mathematical model is proposed. The proposed model overcomes the deficiencies of the existing IoT-SIAF and IoT-SIA models with the inclusion of the Exposed and Forensic states. The proposed model is named the IoT-Susceptible-Exposed-Infectious-Abandon-Forensic (IoT-SEIAF) model. The exposed state assists in the detection of abnormal behaviour of nodes of the IoT at an early stage when the malware attack occurs. The devices in the IoT network are memory-efficient, so the malware attack poses important apprehension for forensic analysis. The variability and data instability of IoT nodes pose challenges in forecasting the object of forensic interest (OOFI) in resource-limited devices, including sensor nodes. The diffusion of data among adjacent nodes presents significant challenges for forensic analysis. Incorporating the forensic state into the proposed model is essential because it aids in gathering the object of forensic interest at the investigation site and facilitates the recovery of digital evidence. The basic reproduction number of the proposed model is derived, serving as a crucial metric for analyzing malware propagation dynamics in IoT, and the local and endemic equilibrium points of the system are examined. The proposed model has been juxtaposed with the preceding paradigm. The mathematical and simulation results confirm that the proposed IoT-SEIAF model demonstrates an improved strategy for preventing malware propagation compared to the prior models. The proposed IoT-SEIAF model demonstrates an amelioration technique to stop malware transmission.