Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

Wazzan, Majda; Algazzawi, Daniyal M.; Bamasaq, Omaima; Albeshri, Aiiad; Cheng, Li

doi:10.3390/app11125713

Cited by 49 publications

(37 citation statements)

References 105 publications

(186 reference statements)

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“…With the popularity of artificial intelligence (AI), the detection mechanisms of malicious network attacks are keen to use AI technology [13]. Nagisetty and Gupta [14] used four different deep learning models to construct a framework in the IoT backbone network to identify malicious network traffic.…”

Section: Previous Workmentioning

confidence: 99%

Machine Learning White-Hat Worm Launcher for Tactical Response by Zoning in Botnet Defense System

Pan

Yamaguchi

2022

Sensors

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Malicious botnets such as Mirai are a major threat to IoT networks regarding cyber security. The Botnet Defense System (BDS) is a network security system based on the concept of “fight fire with fire”, and it uses white-hat botnets to fight against malicious botnets. However, the existing white-hat Worm Launcher of the BDS decides the number of white-hat worms, but it does not consider the white-hat worms’ placement. This paper proposes a novel machine learning (ML)-based white-hat Worm Launcher for tactical response by zoning in the BDS. The concept of zoning is introduced to grasp the malicious botnet spread with bias over the IoT network. This enables the Launcher to divide the network into zones and make tactical responses for each zone. Three tactics for tactical responses for each zone are also proposed. Then, the BDS with the Launcher is modeled by using agent-oriented Petri nets, and the effect of the proposed Launcher is evaluated. The result shows that the proposed Launcher can reduce the number of infected IoT devices by about 30%.

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Section: Previous Workmentioning

confidence: 99%

Machine Learning White-Hat Worm Launcher for Tactical Response by Zoning in Botnet Defense System

Pan

Yamaguchi

2022

Sensors

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“…Recently, various articles have surveyed the literature on IoT botnet detection. The authors in [ 10 ] presented a thorough analysis of experimental works related to the detection of IoT botnets. They provided a systematic literature review (SLR) by applying an effective method for assembling and critically examining research papers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…IoT botnets carry out their activities in at least two main stages, the early stage and late stage (see Figure 1 ), and each stage has different malicious activities. The researchers in [ 10 ] explained these activities in detail. Generally, the two stages are as illustrated below.…”

Section: Introductionmentioning

confidence: 99%

Cross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet

Wazzan

Alghazzawi

Albeshri

et al. 2022

Sensors

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In recent times, organisations in a variety of businesses, such as healthcare, education, and others, have been using the Internet of Things (IoT) to produce more competent and improved services. The widespread use of IoT devices makes our lives easier. On the other hand, the IoT devices that we use suffer vulnerabilities that may impact our lives. These unsafe devices accelerate and ease cybersecurity attacks, specifically when using a botnet. Moreover, restrictions on IoT device resources, such as limitations in power consumption and the central processing unit and memory, intensify this issue because they limit the security techniques that can be used to protect IoT devices. Fortunately, botnets go through different stages before they can start attacks, and they can be detected in the early stage. This research paper proposes a framework focusing on detecting an IoT botnet in the early stage. An empirical experiment was conducted to investigate the behaviour of the early stage of the botnet, and then a baseline machine learning model was implemented for early detection. Furthermore, the authors developed an effective detection method, namely, Cross CNN_LSTM, to detect the IoT botnet based on using fusion deep learning models of a convolutional neural network (CNN) and long short-term memory (LSTM). According to the conducted experiments, the results show that the suggested model is accurate and outperforms some of the state-of-the-art methods, and it achieves 99.7 accuracy. Finally, the authors developed a kill chain model to prevent IoT botnet attacks in the early stage.

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“…Describes potential attack threats of botnets while also covering detection mechanisms. [31,32] Mentions only IoT-based botnets. ( [31]-36 references from 2010 to 2019), ([32]- 122,[2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016][2017][2018][2019][2020][2021].…”

Section: Paper Main Contribution and Reference Metricsmentioning

confidence: 99%

“…Reference [24] endorses convolutional neural network (CNN) as being one of the best-performing techniques for detecting botnets in IoT devices. While a newer systematic review [32] answers the questions of how IoT botnets are formed, what kind of communication and scenarios involve IoT botnets, and which methods currently exist to detect IoT botnets.…”

Section: Paper Main Contribution and Reference Metricsmentioning

confidence: 99%

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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Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

Cited by 49 publications

References 105 publications

Machine Learning White-Hat Worm Launcher for Tactical Response by Zoning in Botnet Defense System

Machine Learning White-Hat Worm Launcher for Tactical Response by Zoning in Botnet Defense System

Cross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet

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

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