Modelling Botnet Propagation in Networks with Layered Defences

Acarali, Dilara; Rajarajan, Muttukrishnan; Komninos, Nikos

doi:10.1109/isncc.2018.8530934

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Whilst this provides a sufficient baseline understanding of the relationships between key model parameters, it may be con- sidered unrealistic as communication and dependency between systems will depend heavily on the services they provide. Options to improve this include a sub-division of the overall c and p populations by grid sub-systems as defined in [6], or the use of dual directed graphs with cross-graph connectivity as defined in [7]. Such expansions would make S-A-C more applicable to both individual sub-systems and to the grid as a whole.…”

Section: Discussionmentioning

confidence: 99%

Modelling DoS Attacks & Interoperability in the Smart Grid

Acarali

Rajarajan

Chema

et al. 2020

2020 29th International Conference on Computer Communications and Networks (ICCCN)

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Smart grids perform the crucial role of delivering electricity to millions of people and driving today's industries. However, the integration of physical operational technology (OT) with IT systems introduces many security challenges. Denial-of-Service (DoS) is a well-known IT attack with a large potential for damage within the smart grid. Whilst DoS is relatively well-understood in IT networks, the unique characteristics and requirements of smart grids bring up new challenges. In this paper, we examine this relationship and propose the OT impact chain to capture possible sequences of events resulting from an IT-side DoS attack. We then apply epidemic principles to explore the same dynamics using the proposed S-A-C model.

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Section: Discussionmentioning

confidence: 99%

Modelling DoS Attacks & Interoperability in the Smart Grid

Acarali

Rajarajan

Chema

et al. 2020

2020 29th International Conference on Computer Communications and Networks (ICCCN)

Self Cite

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“…Additional states, namely Recovered (R), Exposed (E), Connected (C), Quarantine (Q) and Vaccinated (V), were introduced with different variations of the model in many studies subsequently. Different variations with different combinations of states (e.g., SIS (Acarali et al, 2019), SIR (Kermack and McKendrick, 1927), SEIR (Acarali et al, 2018), SEIRS (Signes-Pont et al, 2018;Gardner et al, 2017), SIC (Khosroshahy et al, 2013), SEIRS-QV (Hosseini and Azgomi, 2018), etc.) can depict different behaviours of virus/malware propagation.…”

Section: Epidemic Modelling (Em)mentioning

confidence: 99%

IoTSecSim: A Framework for Modelling and Simulation of Security in Internet of Things

Chee¹,

Ge²,

Bai³

et al. 2023

Preprint

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The project is focusing on building a framework of IoT security. Our main goals are: <ol><li>to model IoT attacks and defences in an emulated IoT networks; and</li><li>to evaluate the impact of different defence techniques on IoT attacks by using multiple security metrics.</li></ol> This framework can create IoT networks with different settings and configurations on IoT devices and network topologies, model malware attacks against the emulated IoT networks and evaluate relevant defences. The simulation software is built with the aim to offer flexibility and adaptability to users. Also, it can be extended with new features and functionalities to simulate recent discovered malware attacks and defences.

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“…34 Acarali propose a novel use of a probabilistic adaptation of the SEIR model applied to defence-in-depth networks. 35 Yin et al propose a non-Markovian spread dynamics model. 36 These cases are working to eliminate the effects of botnets.…”

Section: Related Workmentioning

confidence: 99%

A botnets control strategy based on variable forgetting rate of control commands

Wang

Elahi

et al. 2020

Concurrency and Computation

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Botnets take advantage of the vulnerabilities in the device-to-device (D2D) communication in the Internet of Things (IoT) systems. The infect devices may become part of the command and control mechanism of Botnets. These infected devices may forget control commands to remain undetected. State-of-the-art control models for botnets based on how devices are infected and patched in IoT systems while dealing with Botnets assume that the rate at which IoT devices forget the control commands (forgetting rate) is constant and known. These control models ignore the possible variability of the forgetting rate, which may enable bots to operate without being detected. This research fills this gap by proposing a modeling-based strategy that considers the changes in equipment states and repair speed when the forgetting rate is variable. In this regard, first, based on the state-of-the-art botnets control model, the effect of variation in the forgetting rate is analyzed. After that, an improved strategy is proposed for the botnets control model when the forgetting rate is unknown. Finally, the experimental evaluation of the proposed strategy is performed that proves the correctness of the proposed control strategy.

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Modelling Botnet Propagation in Networks with Layered Defences

Cited by 5 publications

References 17 publications

Modelling DoS Attacks & Interoperability in the Smart Grid

Modelling DoS Attacks & Interoperability in the Smart Grid

IoTSecSim: A Framework for Modelling and Simulation of Security in Internet of Things

A botnets control strategy based on variable forgetting rate of control commands

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