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

Churcher, Andrew; Ullah, Rehmat; Ahmad, Jawad; Rehman, Sadaqat Ur; Masood, Fawad; Gogate, Mandar; Alqahtani, Fehaid; Nour, Boubakr; Buchanan, William J.

doi:10.3390/s21020446

Cited by 160 publications

(90 citation statements)

References 41 publications

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“…Authors in [8] reported the advancements of remote sensor organization (WSN), correspondence innovation, and IoT innovation. Authors in [9] used ML techniques such as KNN, SVM, DT, Naïve Bayes, neural networks, and RF which can be applied in IDS. The authors compared ML models for multi and binary class combinations on the data set of Bot-IoT.…”

Section: Related Workmentioning

confidence: 99%

Attack Detection in IoT using Machine Learning

Anwer

Khan

Farooq

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

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Section: Related Workmentioning

confidence: 99%

Attack Detection in IoT using Machine Learning

Anwer

Khan

Farooq

et al. 2021

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

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“…Besides, users ca be notified about malicious activities. There is a challenge associated with traffic classif cation in the IoT domain because there are very few platforms that analyzes this issue [16 A limited research is done on IDS using ML deployed on IoT networks [17,18]. The pr mary reasons are the lack of datasets and also real hardware deployed for all dataset consisting of simulated IoT devices [19].…”

Section: Intrusion Detection Systems (Ids)mentioning

confidence: 99%

A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

et al. 2021

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In recent years, different variants of the botnet are targeting government, private organizations and there is a crucial need to develop a robust framework for securing the IoT (Internet of Things) network. In this paper, a Hadoop based framework is proposed to identify the malicious IoT traffic using a modified Tomek-link under-sampling integrated with automated Hyper-parameter tuning of machine learning classifiers. The novelty of this paper is to utilize a big data platform for benchmark IoT datasets to minimize computational time. The IoT benchmark datasets are loaded in the Hadoop Distributed File System (HDFS) environment. Three machine learning approaches namely naive Bayes (NB), K-nearest neighbor (KNN), and support vector machine (SVM) are used for categorizing IoT traffic. Artificial immune network optimization is deployed during cross-validation to obtain the best classifier parameters. Experimental analysis is performed on the Hadoop platform. The average accuracy of 99% and 90% is obtained for BoT_IoT and ToN_IoT datasets. The accuracy difference in ToN-IoT dataset is due to the huge number of data samples captured at the edge layer and fog layer. However, in BoT-IoT dataset only 5% of the training and test samples from the complete dataset are considered for experimental analysis as released by the dataset developers. The overall accuracy is improved by 19% in comparison with state-of-the-art techniques. The computational times for the huge datasets are reduced by 3–4 hours through Map Reduce in HDFS.

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“…Recently, deep-machine-learning-based automated feature engineering and classification has been shown to outperform state-of-the-art, manual-feature-engineering-based shallow classification. However, both deep and shallow machine learning algorithms have widely been applied to English Corpora, with little work being carried out to develop deep learning models for Persian sentiment analysis [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Dashtipour

Gogate

Adeel

et al. 2021

Entropy

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Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.

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

Cited by 160 publications

References 41 publications

Attack Detection in IoT using Machine Learning

Attack Detection in IoT using Machine Learning

A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

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