4 research outputs found
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Denial of Service Detection for IoT Networks Using Machine Learning
The Internet of Things (IoT) is considered one of the trending technologies today. IoT affects a variety of industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyberattacks and breaches are rapidly targeting networks equipped with IoT devices. Due to the resource-constrained nature of the IoT devices, one of the Internet security issues impacting IoT devices is the Denial-of-Service (DoS). This encourages the development of new techniques for automatically detecting DoS in IoT networks. In this paper, we test the performance of the following Machine Learning (ML) algorithms in detecting IoT DoS attacks using packet analysis at regular time intervals: Neural Networks (NN), Gaussian Naive Bayes (NB), Decision Trees (DT), and Support Vector Machine (SVM). We were able to achieve 98% accuracy in intrusion detection for IoT devices. We have created a novel way of detecting the attacks using only six attributes, which significantly reduces the time to train the ML Models by 58% on average. This research is based on data collected from actual IoT attacks on IoT networks. This paper shows that using the DT or NN; we can detect attacks on IoT devices. Furthermore, it shows that NB and SVM are poor in detecting IoT attacks. In addition, it proves that middle boxes embedded with ML Models can be utilized to detect attacks in places such as houses, manufactures, and plants
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Anomaly detection for IoT networks using machine learning
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning.
This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets.
This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%.
This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature