Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning

Kim, Jiyeon; Shim, Minsun; Hong, Seungah; Shin, Yulim; Choi, Eun‐Jung

doi:10.3390/app10197009

Cited by 61 publications

(26 citation statements)

References 33 publications

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“…In comparison to other work in the same eld, (Rajadurai and Gandhi 2020) obtained a highest Recall of 97.75% compared to the model presented in this work where a Recall of 100% was achieved for most of the malware types especially the ANN-DT, ANN-SVM and ANN-kNN model. (Kim et al 2020) in one of the ML models using LR as standalone model performed poorly in detecting Botnet, however, in this proposed hybrid model, ANN-LR performed excellently well in detecting anomalies in all malware types and most importantly in Gagfyt (BASHLITE) and Mirai which was the Botnet used by (Kim et al 2020). (Hodo et al 2016) achieved an accuracy of 99.4% with detecting DDoS in IoT using ANN.…”

Section: Discussionmentioning

confidence: 89%

“…The type of attacks that IoT devices are susceptible to include -but are not limited to -Denial of Service, Botnets, Man-in-the-middle attacks, data theft etc. (Aris et al 2015;Cekerevac et al 2017;Kim et al 2020). Despite these risks, the popularity of IoT devices will scale up at an exponential rate, and as such it is extremely important for service providers, organizations and individuals to protect their infrastructure from various potential cyber-attacks to any of their devices.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Machine Learning Method in detecting anomalies in IoT at the fog layer

Ayodele

Jones

2021

Preprint

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With the rapid growth and utilization of IoT devices around the world, attacks on these devices are also increasing thereby posing a security and privacy issue for industry providers and end-users alike. A common way to detect anomaly behaviour is to analyze the network traffic and categorize the outcome into benign and malignant traffic. With an increase in network traffic and sophistication of attacking techniques daily, there is a need for a state-of-the-art pattern recognition technique that can handle this ever increasing and ever-changing traffic and can also improve over time as attacks become more sophisticated. This research paper proposes a hybrid model for anomaly detection at the IoT fog layer using an ANN as a base model and several binary classifiers (which served as meta-classifiers) connected in series. The proposed model was tested and evaluated on a dataset of ‘x’ observations, demonstrating that such a model is both highly effective and efficient in detecting IoT network traffic anomalies.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A Hybrid Machine Learning Method in detecting anomalies in IoT at the fog layer

Ayodele

Jones

2021

Preprint

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“…In [ 27 ], the authors made use of machine learning and deep learning techniques for detection. They concentrated on botnets affecting different IoT devices and developed ML-based models for each type of device.…”

Section: Literature Reviewmentioning

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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“…For the purpose of clarification, Table 5 below details a number of papers that goes over machine learning-based detection: Neural network is not the only method to use the N-BaIoT dataset, as seen in [97], where Bashlite and Mirai found their way into various IoT devices. These included doorbells, baby monitors, security cameras and a webcam.…”

Section: Machine Learning and Network-based Detection Mechanismsmentioning

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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Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning

Cited by 61 publications

References 33 publications

A Hybrid Machine Learning Method in detecting anomalies in IoT at the fog layer

A Hybrid Machine Learning Method in detecting anomalies in IoT at the fog layer

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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