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558 results on '"Buchanan, William J."'

162. A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT

Author

Almas Khan, Muhammad, Khan, Muazzam A, Ullah Jan, Sana, Ahmad, Jawad, Jamal, Sajjad Shaukat, Shah, Awais Aziz, Pitropakis, Nikolaos, Buchanan, William J, Alonistioti, Nancy, Panagiotakis, Spyros, and Markakis, Evangelos K

Subjects
IoT, classification, Centre for Distributed Computing, Networking and Security, security, MQTT, IDS, Cyber-security, AI and Technologies
Abstract

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset.

Published
2021

176. Privacy-Preserving Passive DNS

Author

Papadopoulos, Pavlos, primary, Pitropakis, Nikolaos, additional, Buchanan, William J., additional, Lo, Owen, additional, and Katsikas, Sokratis, additional

Published
2020
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194. Open-source Data Analysis and Machine Learning for Asthma Hospitalisation Rates

Author

Hepburn, Leigh-Anne, Chute, Chaloner, Rooney, Laura, Buchanan, William J., and Smales, Adrian

Subjects
respiratory tract diseases
Abstract

Long-term conditions in Scotland account for 80%of all GP consultations; they also account for 60% of all deaths inScotland. Asthma and Chronic Obstructive Pulmonary Disease(COPD) are common long-term respiratory diseases [1]. Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation. It is defined by the history of respiratory symptoms such as wheeze, shortness of breath, chest tightness and cough that vary over time and in intensity, together with variable expiratory airflow limitation [2]. So far, we know that there are many different things – such as viruses, allergens, and pollution – that cause asthma or trigger attacks but not why or how they do it. This paper outlines how an open source dataset can be used to estimate asthma hospitalisation rates and uses machine learning to predict these rates, within±7.5%, and for an 86.67% success rate.

Published
2018

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