A Method to Secure IoT Devices Against Botnet Attacks

Shah, Trusit; Venkatesan, S.

doi:10.1007/978-3-030-23357-0_3

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…Shah and Venkatesan proposed a mechanism to alter the intricacy of the puzzle after every login try. This mechanism ensures that, if all the IoT devices have utilised the login puzzle, then mirai attack will require two months to be affected [18]. The results showed some progress.…”

Section: A Rq1:what Are the Contributions Of The Primary Studies?mentioning

confidence: 92%

Systematic Literature Review on IoT-Based Botnet Attack

et al. 2020

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The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its capability to provide a better service. This technology has been successfully implemented on various devices. The growth of IoT devices is massive at present. However, security is becoming a major challenge with this growth. Attacks, such as IoT-based botnet attacks, are becoming frequent and have become popular amongst attackers.IoT has a resource constraint and heterogeneous environments, such as low computational power and memory. Hence, these constraints create problems in implementing a security solution in IoT devices. Therefore, various kind of attacks are possible due to this vulnerability, with IoT-based botnet attack being one of the most popular.In this study, we conducted a comprehensive systematic literature review on IoT-based botnet attacks. Existing state of the art in the area of study was presented and discussed in detail. A systematic methodology was adopted to ensure the coverage of all important studies. This methodology was detailed and repeatable. The review outlined the existing proposed contributions, datasets utilised, network forensic methods utilised and research focus of the primary selected studies. The demographic characteristics of primary studies were also outlined.The result of this review revealed that research in this domain is gaining momentum, particularly in the last 3 years (2018-2020). Nine key contributions were also identified, with Evaluation, System, and Model being the most conducted.

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Section: A Rq1:what Are the Contributions Of The Primary Studies?mentioning

confidence: 92%

Systematic Literature Review on IoT-Based Botnet Attack

et al. 2020

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“…Different investigations have proposed several approaches to secure systems. The study [40] proposed solutions for IoT devices and propose a riddling way to combat the Mirai attack. Mirai malware had infected roughly a million IoT devices using a brute‐forcing solution.…”

Section: Botnet Background and Literaturementioning

confidence: 99%

Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles

Javed¹,

Jalil

Moqurrab

et al. 2020

Trans Emerging Tel Tech

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The key characteristic of smart cities (ie, connectivity and intelligence) has enabled connected vehicles to work together to accomplish complex jobs that they are unable to perform individually. Connectivity not only being an inevitable blessing but also poses growing cybersecurity challenges for connected vehicles. The overall risk of connected vehicles is wide as the cybercriminals are nowadays applying versatile approaches (botnets, phishing, zero-days, rootkits, etc) to disrupt their communication. The botnet, capable of launching distributed denial of service attack, is a potential threat for any connected ecosystem due to its excessive capabilities of using maximum compromised devices. Hence, the detection of botnet attacks has become a pinnacle point for cybersecurity analysts. Existing studies lack in detecting botnet at the earliest and accurately. This article presents an approach to detect the botnet attacks at an early stage and accurately utilizing typical network traffic and temporal features. The proposed approach use machine learning algorithms for the detection of botnet attack(s) and present a detailed comparison of the decision tree, probabilistic neural network, sequential minimal optimization, and Adaboost classifiers, as well as comparison of the proposed approach with existing studies in this domain, is also done. Furthermore, it investigates the role of temporal features for botnet detection. The proposed approach accomplishes the promising true positive rate of 99.7%. Results show that our methodology is efficient compared with the existing studies. INTRODUCTIONThe concept of a smart city is primarily composed of information and communication technologies (ICT), to advance the quality of services such as connectivity, energy, transportation, and others. Especially in transportation the continuous vehicle to infrastructure (V2I), vehicle-to-sensors (V2S), vehicle-to-pedestrians, vehicle-to-vehicle (V2V) and the vehicle-to-cloud (V2C) services in smart cities are becoming essential for safety and nonsafety applications. Smart cities provide a channel to the driver, vehicle, infrastructure, and cloud to connect. 1,2 Smart cities produce a huge amount of data collected through IoT devices for further analysis that brings security issues with regard to communicating entities. [3][4][5][6][7] IoT devices (ie, smartphones, smartwatches, smart TV) have become a need of our daily lives. People around the globe are connected using personal smart devices and use them to perform many routine tasks daily. [8][9][10] Security in this current era of the connected world is most important as a single cyberattack can result in loss of important Abdul Rehman Javed and Zunera Jalil contributed equally to this study.

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RC-Security Mechanism to Prevent Attacks on IoT Smart Switch

Makhija

Narayanan

2021

Communications in Computer and Information Science

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A Method to Secure IoT Devices Against Botnet Attacks

Cited by 11 publications

References 13 publications

Systematic Literature Review on IoT-Based Botnet Attack

Systematic Literature Review on IoT-Based Botnet Attack

Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles

RC-Security Mechanism to Prevent Attacks on IoT Smart Switch

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