Using Ensemble Learning Technique for Detecting Botnet on IoT

Rezaei, Amirhossein

doi:10.1007/s42979-021-00585-w

Cited by 16 publications

(11 citation statements)

References 49 publications

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“…Benign and malicious traffic for the IoT camera was captured. HCRL lab [ 26 ] generated an academic purpose dataset, where benign dataset was captured for smart home devices, such as smart speakers. A malicious dataset was also collected after infecting the devices with IoT botnet binaries, such as Mirai.…”

Section: Results and Analysismentioning

confidence: 99%

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Lightweight Internet of Things Botnet Detection Using One-Class Classification

Malik

Rehman

Maqsood

et al. 2022

Sensors

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Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botnets in a heterogeneous environment. The proposed one-class classifier, which is based on one-class KNN, can detect the IoT botnets at the early stage with high accuracy. The proposed machine-learning-based model is a lightweight solution that works by selecting the best features leveraging well-known filter and wrapper methods for feature selection. The proposed strategy is evaluated over different datasets collected from varying network scenarios. The experimental results reveal that the proposed technique shows improved performance, consistent across three different datasets used for evaluation.

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Section: Results and Analysismentioning

confidence: 99%

“…To optimize the detection accuracy of IoT botnets, ensemble learning was introduced by the researchers in Ref. [ 26 ]. The algorithms from supervised, unsupervised and regression learning were chosen to enhance accuracy and reduce the number of features at the time of training.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Internet of Things Botnet Detection Using One-Class Classification

Malik

Rehman

Maqsood

et al. 2022

Sensors

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“…Botnet detection techniques for the IoT are either network-based or host-based [16][17][18][19][20]. However, the host-based approach is less realistic.…”

Section: Related Workmentioning

confidence: 99%

“…However, their best empirical results were recorded for the best logistic regression model, which scored 99.4% and 99.7%, registered for accuracy and F-score with low detection overhead. In [19][20][21][22], the authors' studies barely used the latest techniques, such as autoencoders, which are a part of our proposed technique in ensemble learning-known as N-BaIoT. This means that IoT attacks often remained undetected and unnoticed due to a lack of modern security defense mechanisms.…”

Section: Related Workmentioning

confidence: 99%

ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

Al‐Haija

Al-Dala’ien

2022

JSAN

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Due to the prompt expansion and development of intelligent systems and autonomous, energy-aware sensing devices, the Internet of Things (IoT) has remarkably grown and obstructed nearly all applications in our daily life. However, constraints in computation, storage, and communication capabilities of IoT devices has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for a lightweight and anomaly-based detection system that can build profiles for normal and malicious activities over IoT networks. In this paper, we propose an ensemble learning model for botnet attack detection in IoT networks called ELBA-IoT that profiles behavior features of IoT networks and uses ensemble learning to identify anomalous network traffic from compromised IoT devices. In addition, our IoT-based botnet detection approach characterizes the evaluation of three different machine learning techniques that belong to decision tree techniques (AdaBoosted, RUSBoosted, and bagged). To evaluate ELBA-IoT, we used the N-BaIoT-2021 dataset, which comprises records of both normal IoT network traffic and botnet attack traffic of infected IoT devices. The experimental results demonstrate that our proposed ELBA-IoT can detect the botnet attacks launched from the compromised IoT devices with high detection accuracy (99.6%) and low inference overhead (40 µ-seconds). We also contrast ELBA-IoT results with other state-of-the-art results and demonstrate that ELBA-IoT is superior.

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“…Through a new ML algorithm consisting of a combination of ANN and DT, Rezaei [145], has obtained a detection accuracy of 100%. The technique has a noticeable 11.36 s duration detection time using 20 features to detect botnets in IoT.…”

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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Using Ensemble Learning Technique for Detecting Botnet on IoT

Cited by 16 publications

References 49 publications

Lightweight Internet of Things Botnet Detection Using One-Class Classification

Lightweight Internet of Things Botnet Detection Using One-Class Classification

ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

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

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