List of Titles

Maintenance and asset management practices of industrial assets : importance of tribological practices and digital tools

- Pai, Raghuvir, Chattopadhyay, Gopinath, Karmakar, Gour

Authors: Pai, Raghuvir , Chattopadhyay, Gopinath , Karmakar, Gour
Date: 2023
Type: Text , Journal article
Relation: International Journal of Process Management and Benchmarking Vol. 13, no. 2 (2023), p. 233-256
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Description: There are a large number of rotating and sliding parts in industrial assets. Tribological behaviour plays a significant role in influencing friction and wear, and in turn, the life of these parts. There are issues and challenges in understanding the tribological aspects and behaviour of machine components by maintenance professionals so that informed decisions can taken to improve performance and productivity. An understanding of tribology helps in developing and applying the tools and techniques necessary for better maintenance. In recent years, remote performance monitoring (RPM), internet of things (IoT), machine learning, artificial intelligence and data analytics have made a significant contribution to maintenance and asset management. This paper reviews the tribological aspects related to maintenance, reliability and asset management. The findings of this study will be useful to engineers and managers to understand and appreciate the relationship between tribology, maintenance, reliability and availability for better asset management. Copyright © 2023 Inderscience Enterprises Ltd.

Secure passive keyless entry and start system using machine learning

- Ahmad, Usman, Song, Hong, Bilal, Awais, Alazab, Mamoun, Jolfaei, Alireza

Authors: Ahmad, Usman , Song, Hong , Bilal, Awais , Alazab, Mamoun , Jolfaei, Alireza
Date: 2018
Type: Text , Conference proceedings
Relation: 11th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2018; Melbourne, Australia; 11th-13th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11342 LNCS, p. 304-313
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Description: Despite the benefits of the passive keyless entry and start (PKES) system in improving the locking and starting capabilities, it is vulnerable to relay attacks even though the communication is protected using strong cryptographic techniques. In this paper, we propose a data-intensive solution based on machine learning to mitigate relay attacks on PKES Systems. The main contribution of the paper, beyond the novelty of the solution in using machine learning, is in (1) the use of a set of security features that accurately profiles the PKES system, (2) identifying abnormalities in PKES regular behavior, and (3) proposing a countermeasure that guarantees a desired probability of detection with a fixed false alarm rate by trading off the training time and accuracy. We evaluated our method using the last three months log of a PKES system using the Decision Tree, SVM, KNN and ANN and provide the comparative analysis of the relay attack detection results. Our proposed framework leverages the accuracy of supervised learning on known classes with the adaptability of k-fold cross-validation technique for identifying malicious and suspicious activities. Our test results confirm the effectiveness of the proposed solution in distinguishing relayed messages from legitimate transactions.
Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A critical review of intrusion detection systems in the internet of things : techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

- Khraisat, Ansam, Alazab, Ammar

Authors: Khraisat, Ansam , Alazab, Ammar
Date: 2021
Type: Text , Journal article
Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
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Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s).

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A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing

- Nasser, Nidal, Emad-ul-Haq, Qazi, Imran, Muhammad, Ali, Asmaa, Razzak, Imran, Al-Helali, Abdulaziz

Authors: Nasser, Nidal , Emad-ul-Haq, Qazi , Imran, Muhammad , Ali, Asmaa , Razzak, Imran , Al-Helali, Abdulaziz
Date: 2023
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 35, no. 19 (2023), p. 13775-13789
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Description: Coronavirus (COVID-19) is a very contagious infection that has drawn the world’s attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data’s intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system’s robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications

- Rashid, Md Mamunur, Kamruzzaman, Joarder, Mehedi Hassan, Mohammad, Imam, Tasadduq, Wibowo, Santoso, Gordon, Steven, Fortino, Giancarlo

Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Mehedi Hassan, Mohammad , Imam, Tasadduq , Wibowo, Santoso , Gordon, Steven , Fortino, Giancarlo
Date: 2022
Type: Text , Journal article
Relation: Computers and Security Vol. 120, no. (2022), p.
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Description: Intrusion Detection Systems (IDS) based on deep learning models can identify and mitigate cyberattacks in IoT applications in a resilient and systematic manner. These models, which support the IDS's decision, could be vulnerable to a cyberattack known as adversarial attack. In this type of attack, attackers create adversarial samples by introducing small perturbations to attack samples to trick a trained model into misclassifying them as benign applications. These attacks can cause substantial damage to IoT-based smart city models in terms of device malfunction, data leakage, operational outage and financial loss. To our knowledge, the impact of and defence against adversarial attacks on IDS models in relation to smart city applications have not been investigated yet. To address this research gap, in this work, we explore the effect of adversarial attacks on the deep learning and shallow machine learning models by using a recent IoT dataset and propose a method using adversarial retraining that can significantly improve IDS performance when confronting adversarial attacks. Simulation results demonstrate that the presence of adversarial samples deteriorates the detection accuracy significantly by above 70% while our proposed model can deliver detection accuracy above 99% against all types of attacks including adversarial attacks. This makes an IDS robust in protecting IoT-based smart city services. © 2022 Elsevier Ltd

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Multi-source cyber-attacks detection using machine learning

- Taheri, Sona, Gondal, Iqbal, Bagirov, Adil, Harkness, Greg, Brown, Simon, Chi, Chihung

Authors: Taheri, Sona , Gondal, Iqbal , Bagirov, Adil , Harkness, Greg , Brown, Simon , Chi, Chihung
Date: 2019
Type: Text , Conference proceedings , Conference paper
Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1167-1172
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Description: The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
Description: Proceedings of the IEEE International Conference on Industrial Technology

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