RIoT: A Rapid Exploit Delivery Mechanism against IoT Devices Using Vehicular Botnets

Garip, Mevlut Turker; Reiher, Peter; Gerla, Mário

doi:10.1109/vtcfall.2019.8891228

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…This additional communication makes VANETs a likely target for malicious attackers. Reference [198] introduces novel attack VANETs and propose a honeypot approach to notify nearby vehicles to ignore messages stemming from vehicles infected by botnets. The paper also proposes the use of localisation mechanisms to limit the exposure to far-away botnets.…”

Section: Vehicle Networkmentioning

confidence: 99%

“…2 out 18 papers listed. [81,198] Remotely disrupting the controls of an autonomous vehicle. 1 out of 18 papers listed.…”

Section: Trend Topics Within Trend Relative Interest Papersmentioning

confidence: 99%

“…The most common trend and challenge within botnet research between 2013 and 2021 is the continued spread of botnets anchored in pervasive computing devices. With the increase in computational power within normally benign devices, such as home appliances [31,201], mobile phones [222,223] and (non)-autonomous vehicles [81,198], a higher potential of malicious activity within these devices becomes viable. As devices are allowed more computational headroom, botnets' ability to perform increasingly effective obfuscation techniques to mask their existence within pervasive devices grows ever more concerning.…”

Section: Proactive Botnet Mitigationmentioning

confidence: 99%

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

Stege

El-Habr

et al. 2021

Future Internet

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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.

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Section: Vehicle Networkmentioning

confidence: 99%

“…2 out 18 papers listed. [81,198] Remotely disrupting the controls of an autonomous vehicle. 1 out of 18 papers listed.…”

Section: Trend Topics Within Trend Relative Interest Papersmentioning

confidence: 99%

Section: Proactive Botnet Mitigationmentioning

confidence: 99%

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

Stege

El-Habr

et al. 2021

Future Internet

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“…An exploit is a bit of software, i.e chunk of data or vulnerability to generate unintended or a series of commands that take benefit of a bug or unanticipated action to occur on computer software or hardware [24]. Such behavior usually involves things like obtaining control of a computer system.…”

Section: • Exploits Attackmentioning

confidence: 99%

IoT-Botnet Detection using Long Short-Term Memory Recurrent Neural Network

Costa¹,

Dessai²,

Gaonkar³

et al. 2020

IJERT

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IoT (Internet of Things) provides unique identifiers and the facility to convey data across a network without requiring human-to-computer or human-to-human interaction. It is one of the most fast-evolving technologies nowadays. The positive influence of the IoT toward governments, citizens, and businesses is being significant. IoT comes with significant security concerns that need to be addressed. One of the serious security threats in network security is IoT-Bot. In past years many techniques are being practiced to detect IoT-Bot in a network. This paper explains the detection of IoT-botnet using a deep learning-based LSTM RNN (Long Short-Term Memory Recurrent Neural Network) model. The accuracy of this model is then compared with the SVM (Support Vector Machine), LR (Linear Regression), and KNN (K-Nearest Neighbors) model. UNSW-NB15 dataset is used for training and testing the model. The dataset contains 9 types of attack categories. The accuracy of this proposed model is very high. This can further be extended to work in real-time botnet detention. Wireshark can be used to collect real-time network traffic and detect IoT-Bot in a network.

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