Forthcoming and Online First Articles

International Journal of Internet of Things and Cyber-Assurance

International Journal of Internet of Things and Cyber-Assurance (IJITCA)

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International Journal of Internet of Things and Cyber-Assurance (5 papers in press)

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  • A Hybrid Machine Learning Method in detecting anomalies in IoT at the fog layer   Order a copy of this article
    by Believe Ayodele, Michaela Tromans Jones 
    Abstract: With the rapid growth and utilisation 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 analyse the network traffic and categorise 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. The proposed model was tested and evaluated on a dataset, demonstrating that such a model is both highly effective and efficient in detecting IoT network traffic anomalies.
    Keywords: internet of things; IoT; artificial neural network; ANN; machine learning; ML; binary classifiers; anomaly detection.

  • Composition Analysis of the Bot-IoT Dataset   Order a copy of this article
    by Jared M. Peterson, Taghi Khoshgoftaar, Joffrey L. Leevy 
    Abstract: As machine learning continues to be a promising tool for cyber security, industry and researchers have continued to develop datasets for research. These datasets often contain multiple emulated exemplars for common attacks seen in real-world networks. The datasets provide researchers with the necessary samples to train and test the detection capabilities of their machine learning models. This paper contains an in-depth analysis of the composition of one of the newest datasets, Bot-IoT. The full dataset contains about 73 million instances (big data), three dependent features, 26 independent features, and four primary attack categories. The purpose of this paper is to provide researchers with an understanding of the environment used to create Bot-IoT and how that environment effected its composition. A detailed analysis of the datasets composition can provide additional insight into the datasets suitability for machine learning.
    Keywords: Bot-IoT; machine learning; destination port; internet of things; IoT; intrusion detection; denial of service; DoS; distributed denial of service; DDoS; information theft; reconnaissance.
    DOI: 10.1504/IJITCA.2023.10048061
  • Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach   Order a copy of this article
    by Sayan Chatterjee, Manjesh Kumar Hanawal 
    Abstract: Critical role of the internet of things (IoT) in various domains like smart city, healthcare, supply chain, and transportation has made them the target of malicious attacks. Past works in this area focused on centralised intrusion detection system (IDS), assuming a central entity to perform data analysis and identify threats. However, such IDS may not always be feasible, mainly due to the spread of data across multiple sources, and gathering at a central node can be costly. In this paper, we first present an architecture for IDS based on a hybrid ensemble model named PHEC, which gives improved performance compared to state-of-the-art architectures. We then adapt this model to a federated learning framework. Next, we propose noise-tolerant PHEC to address the label-noise problem. Experimental results on four benchmark datasets drawn from various security attacks show that our model achieves high TPR while keeping FPR low on noisy and clean data.
    Keywords: IoT security; ensemble learning; federated learning; noise robust classification.
    DOI: 10.1504/IJITCA.2023.10048062
  • IoT Attack Prediction Using Big Bot-IoT Data   Order a copy of this article
    by Joffrey L. Leevy, Taghi Khoshgoftaar, John Hancock 
    Abstract: Bot-IoT is a recent and publicly available dataset that depicts attack traffic launched by BotNets against internet of things (IoT) networks. Normal (non-attack) traffic is represented by over 9,000 of the approximately 73,000,000 instances of big data that constitute this dataset. We present an easy-to-learn Bot-IoT approach, centred on the use of a minimum number of dataset features and a simple machine learning algorithm. Our contribution is defined by decision tree models built from derived Bot-IoT datasets with no more than three features. As per our definition of easy-to-learn, we require that predictive models have area under the receiver operating characteristic curve (AUC) mean scores greater than 0.99. According to our results, all the derived datasets produce easy-to-learn models. To the best of our knowledge, this work, in terms of its simplicity, interpretability, and performance, is an improvement over Bot-IoT classification approaches in existing literature.
    Keywords: Bot-IoT; decision tree; easy-to-learn; intrusion detection; IoT; machine learning; denial-of-service; DoS; distributed denial-of-service; DDoS; information theft; reconnaissance.
    DOI: 10.1504/IJITCA.2023.10048063
  • Feature Evaluation for IoT Botnet Traffic Classification   Order a copy of this article
    by Joffrey L. Leevy, Taghi Khoshgoftaar, John Hancock 
    Abstract: Researchers must often decide whether to use destination port as an input feature when building predictive models for intrusion detection systems. To evaluate this feature, we use the Bot-IoT dataset with three different sets of input features. The first and second set of input features comprise all Bot-IoT features (26 variables) and all Bot-IoT features excluding destination port (25 variables), respectively, while the third includes destination port as the only feature. Our results show that classification models trained on the first (26 variables) and second (25 variables) set of input features generally yield favourable results. We note that several destination port values are associated with disproportionate label distributions. Hence, it is possible in some cases, that the classifiers have been trained to closely correlate specific attack types with specific values of destination port. To the best of our knowledge, this is the first Bot-IoT study based on the destination port feature.
    Keywords: Bot-IoT; intrusion detection; internet of things; IoT; machine learning; ensemble classifiers; big data; destination port; CatBoost; LightGBM; random forest; XGBoost.
    DOI: 10.1504/IJITCA.2023.10048087