Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Soe, Yan Naung; Feng, Yaokai; Santosa, Paulus Insap; Hartanto, Rudy; Sakurai, Kouichi

doi:10.3390/s20164372

Cited by 144 publications

(64 citation statements)

References 26 publications

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“…Recently, several efforts have been devoted for ML in IoT network [ 32 , 53 , 54 , 55 ]. However, in most of the existing works, the performance are checked for specific types of ML algorithms, such as ANN, J48 DT, and NB without detailed performance evaluation.…”

Section: Background and Related Workmentioning

confidence: 99%

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Churcher

Ullah

Ahmad

et al. 2021

Sensors

159

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In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

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Section: Background and Related Workmentioning

confidence: 99%

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Churcher

Ullah

Ahmad

et al. 2021

Sensors

159

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“…A framework of machine learning-based botnet attack detection with sequential detection architecture for IDS has been introduced in [9]. Demand of processing resources reduced by adopting relevant feature selection method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to the statistics, the damages caused by the cyber attacks are expected to reach up to 3 Trillion by the year 2021 [8]. According to the Symantec report, on an average of IoT devices were attacked once after every two minutes [9]. Another analysis in [10] shows drastic increase in cyberattacks incidents by approximately 2000% in just 6 years.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional approaches examine network packet by matching it with a predefined database where all types of attacks and signature patterns are already saved. The system was not sufficient for evaluating the traffic on the basis of this database, due to arrival of possible new zero-day, which will be distinct from signatures found in the file [9]. Existing IDS has shown inefficiency in detecting various attacks including zero-day attacks and reducing the false alarm rates (FARs) [17].…”

Section: Introductionmentioning

confidence: 99%

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A New Ensemble-Based Intrusion Detection System for Internet of Things

et al. 2021

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The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

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“…The authors generated their dataset by creating a virtual network and recorded the traffic into pcap files. Soe et al [ 6 ] proposed a sequential attack detection architecture that uses three machine learning models for IoT networks. The authors used the N-BaIoT dataset and achieved 99% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Explainable Security in SDN-Based IoT Networks

Sarica

Angin

2020

Sensors

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The significant advances in wireless networks in the past decade have made a variety of Internet of Things (IoT) use cases possible, greatly facilitating many operations in our daily lives. IoT is only expected to grow with 5G and beyond networks, which will primarily rely on software-defined networking (SDN) and network functions virtualization for achieving the promised quality of service. The prevalence of IoT and the large attack surface that it has created calls for SDN-based intelligent security solutions that achieve real-time, automated intrusion detection and mitigation. In this paper, we propose a real-time intrusion detection and mitigation solution for SDN, which aims to provide autonomous security in the high-traffic IoT networks of the 5G and beyond era, while achieving a high degree of interpretability by human experts. The proposed approach is built upon automated flow feature extraction and classification of flows while using random forest classifiers at the SDN application layer. We present an SDN-specific dataset that we generated for IoT and provide results on the accuracy of intrusion detection in addition to performance results in the presence and absence of our proposed security mechanism. The experimental results demonstrate that the proposed security approach is promising for achieving real-time, highly accurate detection and mitigation of attacks in SDN-managed IoT networks.

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Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Cited by 144 publications

References 26 publications

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

A New Ensemble-Based Intrusion Detection System for Internet of Things

Explainable Security in SDN-Based IoT Networks

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