The Study on the Botnet and its Prevention Policies in the Internet of Things

Ji, Yimu; Lu, Yao; Liu, Shangdong; Yao, Haichang; Ye, Qing; Wang, Ruchuan

doi:10.1109/cscwd.2018.8465280

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…Ji et al [19] focused their work on Mirai's architecture to study its propagation patterns. They based their propagation model on the SIR format, with N defined as the total IoT population, S as IoT nodes with weak logins, I as infected nodes, and R as immune nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Wang et al [14] focus specifically on the mobile actuator scenario, demonstrating how the IoT space may present unique vulnerabilities and exploitation opportunities. Ji et al [19] provide a specialised model for the Mirai botnet, driven by a need to understand the propagation of this prevalent threat. Singh et al [20] and Feng et al [23] focus on defining 0 -based thresholds, using the epidemic-based metric to determine the limits of spread.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed model aims to capture this and to explore the surrounding factors. The works of Ji et al [19] and Gardner et al [21] are botnet-focused, but they concentrate exclusively on Mirai, using empirical measurements of this malware to build models which characterise it specifically. In contrast, the proposed model considers a wider range of behaviours by incorporating observations from Mirai's descendants as well.…”

Section: Related Workmentioning

confidence: 99%

“…These papers also have vague or abstract definitions of scanning behaviour, assuming a standard approach across all bot instances. An exception to this is Ji et al [19], who consider the attack surface to be defined based on the infection rate and omitted portions of the IP address space. Meanwhile, the IoT-SIS model defines 3 separate and clearly defined scanning behaviours, along with the corresponding attack surfaces that become available with each method.…”

Section: Related Workmentioning

confidence: 99%

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Modelling the Spread of Botnet Malware in IoT-Based Wireless Sensor Networks

Acarali

Rajarajan

Komninos

et al. 2019

Security and Communication Networks

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The propagation approach of a botnet largely dictates its formation, establishing a foundation of bots for future exploitation. The chosen propagation method determines the attack surface and, consequently, the degree of network penetration, as well as the overall size and the eventual attack potency. It is therefore essential to understand propagation behaviours and influential factors in order to better secure vulnerable systems. Whilst botnet propagation is generally well studied, newer technologies like IoT have unique characteristics which are yet to be thoroughly explored. In this paper, we apply the principles of epidemic modelling to IoT networks consisting of wireless sensor nodes. We build IoT-SIS, a novel propagation model which considers the impact of IoT-specific characteristics like limited processing power, energy restrictions, and node density on the formation of a botnet. Focusing on worm-based propagation, this model is used to explore the dynamics of spread using numerical simulations and the Monte Carlo method to discuss the real-life implications of our findings.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Modelling the Spread of Botnet Malware in IoT-Based Wireless Sensor Networks

Acarali

Rajarajan

Komninos

et al. 2019

Security and Communication Networks

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show abstract

“…Taking derivatives of F and V with respect to I and E, F and V can be respectively transformed in equation (10) and (11)…”

Section: Determination Of Basic Reproductive Number R0mentioning

confidence: 99%

Modelling and Mitigation Strategy of IoT Botnet Propagation

Ibrahim¹,

Abdullah²,

Abdullah³

et al. 2019

Preprint

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Nodes in wireless sensor networks (WSN) are characterized particularly by their limited power and memory capabilities. Limited memory is an important parameter as it defines the size of the operating system and the processing code. As established previously, energy and memory efficiency is the most important evaluation factors of WSNs as they are directly related to data loss and network lifetime. However, based on our simulation results, memory efficiency determines the selection or abandon of nodes by the botmaster for the propagation of bots in an IoT infrastructure. Consequently, the node’s memory efficiency determined the spread of bots in the network and provides defense actors with an insight of the botmaster behavior for mitigation of the attack. Conventional botnet propagation and mitigation models did not consider the impact of node’s memory efficiency in the IoT platform. To address this gap, we build IoT-SIEF, a novel propagation model with forensic capability that will analyze command and control propagation behavior based on the perspective of the node’s memory efficiency. IoT-SIEF model used to explore the dynamics of propagation using numerical simulation with more than 50% outperform other models in mitigating the number of secondary bots. Consequently, it can serve as a basis for assisting the planning, design, and defense of such networks from the investigator's point of view.

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IoT Botnet: The Largest Threat to the IoT Network

Dange¹,

Chatterjee²

2019

Advances in Intelligent Systems and Computing

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The Study on the Botnet and its Prevention Policies in the Internet of Things

Cited by 17 publications

References 8 publications

Modelling the Spread of Botnet Malware in IoT-Based Wireless Sensor Networks

Modelling the Spread of Botnet Malware in IoT-Based Wireless Sensor Networks

Modelling and Mitigation Strategy of IoT Botnet Propagation

IoT Botnet: The Largest Threat to the IoT Network

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