Bio-inspired network security for 5G-enabled IoT applications
- Saleem, Kashif, Alabduljabbar, Ghadah, Alrowais, Nouf, Al-Muhtadi, Jalal, Imran, Muhammad, Rodrigues, Joel
- Authors: Saleem, Kashif , Alabduljabbar, Ghadah , Alrowais, Nouf , Al-Muhtadi, Jalal , Imran, Muhammad , Rodrigues, Joel
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE access Vol. 8, no. (2020), p. 1-1
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- Description: Every IPv6-enabled device connected and communicating over the Internet forms the Internet of things (IoT) that is prevalent in society and is used in daily life. This IoT platform will quickly grow to be populated with billions or more objects by making every electrical appliance, car, and even items of furniture smart and connected. The 5th generation (5G) and beyond networks will further boost these IoT systems. The massive utilization of these systems over gigabits per second generates numerous issues. Owing to the huge complexity in large-scale deployment of IoT, data privacy and security are the most prominent challenges, especially for critical applications such as Industry 4.0, e-healthcare, and military. Threat agents persistently strive to find new vulnerabilities and exploit them. Therefore, including promising security measures to support the running systems, not to harm or collapse them, is essential. Nature-inspired algorithms have the capability to provide autonomous and sustainable defense and healing mechanisms. This paper first surveys the 5G network layer security for IoT applications and lists the network layer security vulnerabilities and requirements in wireless sensor networks, IoT, and 5G-enabled IoT. Second, a detailed literature review is conducted with the current network layer security methods and the bio-inspired techniques for IoT applications exchanging data packets over 5G. Finally, the bio-inspired algorithms are analyzed in the context of providing a secure network layer for IoT applications connected over 5G and beyond networks.
- Authors: Saleem, Kashif , Alabduljabbar, Ghadah , Alrowais, Nouf , Al-Muhtadi, Jalal , Imran, Muhammad , Rodrigues, Joel
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE access Vol. 8, no. (2020), p. 1-1
- Full Text:
- Reviewed:
- Description: Every IPv6-enabled device connected and communicating over the Internet forms the Internet of things (IoT) that is prevalent in society and is used in daily life. This IoT platform will quickly grow to be populated with billions or more objects by making every electrical appliance, car, and even items of furniture smart and connected. The 5th generation (5G) and beyond networks will further boost these IoT systems. The massive utilization of these systems over gigabits per second generates numerous issues. Owing to the huge complexity in large-scale deployment of IoT, data privacy and security are the most prominent challenges, especially for critical applications such as Industry 4.0, e-healthcare, and military. Threat agents persistently strive to find new vulnerabilities and exploit them. Therefore, including promising security measures to support the running systems, not to harm or collapse them, is essential. Nature-inspired algorithms have the capability to provide autonomous and sustainable defense and healing mechanisms. This paper first surveys the 5G network layer security for IoT applications and lists the network layer security vulnerabilities and requirements in wireless sensor networks, IoT, and 5G-enabled IoT. Second, a detailed literature review is conducted with the current network layer security methods and the bio-inspired techniques for IoT applications exchanging data packets over 5G. Finally, the bio-inspired algorithms are analyzed in the context of providing a secure network layer for IoT applications connected over 5G and beyond networks.
Real-time dissemination of emergency warning messages in 5G enabled selfish vehicular social networks
- Ullah, Noor, Kong, Xiangjie, Lin, Limei, Alrashoud, Mubarak, Tolba, Amr, Xia, Feng
- Authors: Ullah, Noor , Kong, Xiangjie , Lin, Limei , Alrashoud, Mubarak , Tolba, Amr , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Computer Networks Vol. 182, no. (2020), p.
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- Description: This paper addresses the issues of selfishness, limited network resources, and their adverse effects on real-time dissemination of Emergency Warning Messages (EWMs) in modern Autonomous Moving Platforms (AMPs) such as Vehicular Social Networks (VSNs). For this purpose, we propose a social intelligence based identification mechanism to differentiate between a selfish and a cooperative node in the network. Therefore, we devise a crowdsensing based mechanism to calculate a tie-strength value based on several social metrics. Moreover, we design a recursive evolutionary algorithm for each node's reputation calculation and update. Given that, then we estimate each node's state-transition probability to select a super-spreader for rapid dissemination. In order to ensure a seamless and reliable dissemination process, we incorporate 5G network structure instead of conventional short range communication which is used in most vehicular networks at present. Finally, we design a real-time dissemination algorithm for EWMs and evaluate its performance in terms of network parameters such as delivery-ratio, delay, hop-count, and message-overhead for varying values of vehicular density, speed, and selfish nodes’ density based on realistic vehicular mobility traces. In addition, we present a comparative analysis of the performance of the proposed scheme with state-of-the-art dissemination schemes in VSNs. © 2020 Elsevier B.V.
- Authors: Ullah, Noor , Kong, Xiangjie , Lin, Limei , Alrashoud, Mubarak , Tolba, Amr , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Computer Networks Vol. 182, no. (2020), p.
- Full Text:
- Reviewed:
- Description: This paper addresses the issues of selfishness, limited network resources, and their adverse effects on real-time dissemination of Emergency Warning Messages (EWMs) in modern Autonomous Moving Platforms (AMPs) such as Vehicular Social Networks (VSNs). For this purpose, we propose a social intelligence based identification mechanism to differentiate between a selfish and a cooperative node in the network. Therefore, we devise a crowdsensing based mechanism to calculate a tie-strength value based on several social metrics. Moreover, we design a recursive evolutionary algorithm for each node's reputation calculation and update. Given that, then we estimate each node's state-transition probability to select a super-spreader for rapid dissemination. In order to ensure a seamless and reliable dissemination process, we incorporate 5G network structure instead of conventional short range communication which is used in most vehicular networks at present. Finally, we design a real-time dissemination algorithm for EWMs and evaluate its performance in terms of network parameters such as delivery-ratio, delay, hop-count, and message-overhead for varying values of vehicular density, speed, and selfish nodes’ density based on realistic vehicular mobility traces. In addition, we present a comparative analysis of the performance of the proposed scheme with state-of-the-art dissemination schemes in VSNs. © 2020 Elsevier B.V.
Consumer perspectives on the use of artificial intelligence technology and automation in crisis support services : mixed methods study
- Ma, Jennifer, O’Riordan, Megan, Mazzer, Kelly, Batterham, Philip, Bradford, Sally, Kõlves, Kairi, Titov, Nickolai, Klein, Britt, Rickwood, Debra
- Authors: Ma, Jennifer , O’Riordan, Megan , Mazzer, Kelly , Batterham, Philip , Bradford, Sally , Kõlves, Kairi , Titov, Nickolai , Klein, Britt , Rickwood, Debra
- Date: 2022
- Type: Text , Journal article
- Relation: JMIR Human Factors Vol. 9, no. 3 (2022), p.
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- Description: Background: Emerging technologies, such as artificial intelligence (AI), have the potential to enhance service responsiveness and quality, improve reach to underserved groups, and help address the lack of workforce capacity in health and mental health care. However, little research has been conducted on the acceptability of AI, particularly in mental health and crisis support, and how this may inform the development of responsible and responsive innovation in the area. Objective: This study aims to explore the level of support for the use of technology and automation, such as AI, in Lifeline’s crisis support services in Australia; the likelihood of service use if technology and automation were implemented; the impact of demographic characteristics on the level of support and likelihood of service use; and reasons for not using Lifeline’s crisis support services if technology and automation were implemented in the future. Methods: A mixed methods study involving a computer-assisted telephone interview and a web-based survey was undertaken from 2019 to 2020 to explore expectations and anticipated outcomes of Lifeline’s crisis support services in a nationally representative community sample (n=1300) and a Lifeline help-seeker sample (n=553). Participants were aged between 18 and 93 years. Quantitative descriptive analysis, binary logistic regression models, and qualitative thematic analysis were conducted to address the research objectives. Results: One-third of the community and help-seeker participants did not support the collection of information about service users through technology and automation (ie, via AI), and approximately half of the participants reported that they would be less likely to use the service if automation was introduced. Significant demographic differences were observed between the community and help-seeker samples. Of the demographics, only older age predicted being less likely to endorse technology and automation to tailor Lifeline’s crisis support service and use such services (odds ratio 1.48-1.66, 99% CI 1.03-2.38; P<.001 to P=.005). The most common reason for reluctance, reported by both samples, was that respondents wanted to speak to a real person, assuming that human counselors would be replaced by automated robots or machine services. Conclusions: Although Lifeline plans to always have a real person providing crisis support, help-seekers automatically fear this will not be the case if new technology and automation such as AI are introduced. Consequently, incorporating innovative use of technology to improve help-seeker outcomes in such services will require careful messaging and assurance that the human connection will continue. © Jennifer S Ma, Megan O’Riordan, Kelly Mazzer, Philip J Batterham, Sally Bradford, Kairi Kõlves, Nickolai Titov, Britt Klein, Debra J Rickwood.
- Authors: Ma, Jennifer , O’Riordan, Megan , Mazzer, Kelly , Batterham, Philip , Bradford, Sally , Kõlves, Kairi , Titov, Nickolai , Klein, Britt , Rickwood, Debra
- Date: 2022
- Type: Text , Journal article
- Relation: JMIR Human Factors Vol. 9, no. 3 (2022), p.
- Full Text:
- Reviewed:
- Description: Background: Emerging technologies, such as artificial intelligence (AI), have the potential to enhance service responsiveness and quality, improve reach to underserved groups, and help address the lack of workforce capacity in health and mental health care. However, little research has been conducted on the acceptability of AI, particularly in mental health and crisis support, and how this may inform the development of responsible and responsive innovation in the area. Objective: This study aims to explore the level of support for the use of technology and automation, such as AI, in Lifeline’s crisis support services in Australia; the likelihood of service use if technology and automation were implemented; the impact of demographic characteristics on the level of support and likelihood of service use; and reasons for not using Lifeline’s crisis support services if technology and automation were implemented in the future. Methods: A mixed methods study involving a computer-assisted telephone interview and a web-based survey was undertaken from 2019 to 2020 to explore expectations and anticipated outcomes of Lifeline’s crisis support services in a nationally representative community sample (n=1300) and a Lifeline help-seeker sample (n=553). Participants were aged between 18 and 93 years. Quantitative descriptive analysis, binary logistic regression models, and qualitative thematic analysis were conducted to address the research objectives. Results: One-third of the community and help-seeker participants did not support the collection of information about service users through technology and automation (ie, via AI), and approximately half of the participants reported that they would be less likely to use the service if automation was introduced. Significant demographic differences were observed between the community and help-seeker samples. Of the demographics, only older age predicted being less likely to endorse technology and automation to tailor Lifeline’s crisis support service and use such services (odds ratio 1.48-1.66, 99% CI 1.03-2.38; P<.001 to P=.005). The most common reason for reluctance, reported by both samples, was that respondents wanted to speak to a real person, assuming that human counselors would be replaced by automated robots or machine services. Conclusions: Although Lifeline plans to always have a real person providing crisis support, help-seekers automatically fear this will not be the case if new technology and automation such as AI are introduced. Consequently, incorporating innovative use of technology to improve help-seeker outcomes in such services will require careful messaging and assurance that the human connection will continue. © Jennifer S Ma, Megan O’Riordan, Kelly Mazzer, Philip J Batterham, Sally Bradford, Kairi Kõlves, Nickolai Titov, Britt Klein, Debra J Rickwood.
Evaluating explanations of artificial intelligence decisions : the explanation quality rubric and survey
- Authors: Young, Charlotte
- Date: 2022
- Type: Text , Thesis , PhD
- Full Text:
- Description: The use of Artificial Intelligence (AI) algorithms is growing rapidly (Vilone & Longo, 2020). With this comes an increasing demand for reliable, robust explanations of AI decisions. There is a pressing need for a way to evaluate their quality. This thesis examines these research questions: What would a rigorous, empirically justified, human-centred scheme for evaluating AI-decision explanations look like? How can a rigorous, empirically justified, human-centred scheme for evaluating AI-decision explanations be created? Can a rigorous, empirically justified, human-centred scheme for evaluating AI-decision explanations be used to improve explanations? Current Explainable Artificial Intelligence (XAI) research lacks an accepted, widely employed method for evaluating AI explanations. This thesis offers a method for creating a rigorous, empirically justified, human-centred scheme for evaluating AI-decision explanations. It uses this to create an evaluation methodology, the XQ Rubric and XQ Survey. The XQ Rubric and Survey are then employed to improve explanations of AI decisions. The thesis asks what constitutes a good explanation in the context of XAI. It provides: 1. a model of good explanation for use in XAI research 2. a method of gathering non-expert evaluations of XAI explanations 3. an evaluation scheme for non-experts to employ in assessing XAI explanations (XQ Rubric and XQ Survey). The thesis begins with a literature review, primarily an exploration of previous attempts to evaluate XAI explanations formally. This is followed by an account of the development and iterative refinement of a solution to the problem, the eXplanation Quality Rubric (XQ Rubric). A Design Science methodology was used to guide the XQ Rubric and XQ Survey development. The thesis limits itself to XAI explanations appropriate for non-experts. It proposes and tests an evaluation rubric and survey method that is both stable and robust: that is, readily usable and consistently reliable in a variety of XAI-explanation tasks.
- Description: Doctor of Philosophy
- Authors: Young, Charlotte
- Date: 2022
- Type: Text , Thesis , PhD
- Full Text:
- Description: The use of Artificial Intelligence (AI) algorithms is growing rapidly (Vilone & Longo, 2020). With this comes an increasing demand for reliable, robust explanations of AI decisions. There is a pressing need for a way to evaluate their quality. This thesis examines these research questions: What would a rigorous, empirically justified, human-centred scheme for evaluating AI-decision explanations look like? How can a rigorous, empirically justified, human-centred scheme for evaluating AI-decision explanations be created? Can a rigorous, empirically justified, human-centred scheme for evaluating AI-decision explanations be used to improve explanations? Current Explainable Artificial Intelligence (XAI) research lacks an accepted, widely employed method for evaluating AI explanations. This thesis offers a method for creating a rigorous, empirically justified, human-centred scheme for evaluating AI-decision explanations. It uses this to create an evaluation methodology, the XQ Rubric and XQ Survey. The XQ Rubric and Survey are then employed to improve explanations of AI decisions. The thesis asks what constitutes a good explanation in the context of XAI. It provides: 1. a model of good explanation for use in XAI research 2. a method of gathering non-expert evaluations of XAI explanations 3. an evaluation scheme for non-experts to employ in assessing XAI explanations (XQ Rubric and XQ Survey). The thesis begins with a literature review, primarily an exploration of previous attempts to evaluate XAI explanations formally. This is followed by an account of the development and iterative refinement of a solution to the problem, the eXplanation Quality Rubric (XQ Rubric). A Design Science methodology was used to guide the XQ Rubric and XQ Survey development. The thesis limits itself to XAI explanations appropriate for non-experts. It proposes and tests an evaluation rubric and survey method that is both stable and robust: that is, readily usable and consistently reliable in a variety of XAI-explanation tasks.
- Description: Doctor of Philosophy
- Siddiqui, Mustafa, Kahandawa, Gayan, Hewawasam, Hasitha
- Authors: Siddiqui, Mustafa , Kahandawa, Gayan , Hewawasam, Hasitha
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Mechatronics, ICM 2023, Leicestershire UK, 15-17 March 2023, Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023
- Full Text: false
- Reviewed:
- Description: Industrial automation systems are excessively used in advanced manufacturing environments. These systems are always prone to failure which not only disturbs smooth manufacturing operations but can also cause injuries to operators. Therefore, in this research, a novel predictive maintenance algorithm is proposed that can be used to detect anomalies in the automation system to avoid asset failure. Artificial Intelligence enabled Digital Twin model was used to detect early anomalies to avoid catastrophic effects of equipment failure. Real-time sensor data were used to validate the proposed novel algorithm. The data were recorded via sensors mounted on the physical system. This paper presents the effectiveness of the proposed algorithm to detect anomalies in industrial automation systems under faulty conditions. © 2023 IEEE.
Digital twin for risk and uncertainty analysis in complex industrial control and automation systems using artificial intelligence and machine learning
- Authors: Siddiqui, Muhammad
- Date: 2023
- Type: Text , Thesis , Masters
- Full Text:
- Description: Industrial control systems play a crucial role in enabling advanced manufacturing operations. However, these systems are inherently susceptible to failure. Detecting faults at an early stage is of paramount importance, as it can prevent the occurrence of fatal and catastrophic consequences resulting from equipment failures. Moreover, timely detection and resolution of faults can save significant costs and time for organizations. The failure of these systems not only poses risks to operators but can also lead to substantial delays in the advanced manufacturing process, imposing substantial financial burdens on organizations. Therefore, a methodology is needed that can be used to avoid the adverse effects of equipment failure of industrial control systems to achieve smooth advanced manufacturing operations. To achieve this, the methodology should be able to detect the abnormal behaviour of the system at very early stages for predictive maintenance. This methodology can be designed using an extremely popular concept known as the Digital Twin, which has gained significant importance in the era of Industry 4.0. In this research, artificial intelligence techniques will be employed to develop a highly accurate and detailed digital twin model. This model will serve as a valuable tool for predictive maintenance in complex industrial control systems, facilitating the achievement of smooth and uninterrupted advanced manufacturing processes. Also, the performance of the proposed Digital Twin model will be compared with state-of-the-art anomaly detection approaches. The digital twin, utilizing the proposed algorithms, will not only be able to detect anomalies but also quantify their severity, classifying them into different levels such as minor, severe, and faulty operations. Furthermore, the research addresses the generalization challenges faced by state-of-the-art approaches, showcasing the digital twin's ability to effectively classify unseen data as healthy or anomalous. The results obtained from the analysis and comparison of state-of-the-art approaches with the proposed algorithms clearly demonstrate the methodology's capability to detect anomalies, quantify their level, and classify them accurately and effectively in real-world data. This validation underscores the robustness and reliability of the developed methodology, further solidifying its potential as a valuable tool for predictive maintenance in complex industrial control systems.
- Description: Masters by Research
- Authors: Siddiqui, Muhammad
- Date: 2023
- Type: Text , Thesis , Masters
- Full Text:
- Description: Industrial control systems play a crucial role in enabling advanced manufacturing operations. However, these systems are inherently susceptible to failure. Detecting faults at an early stage is of paramount importance, as it can prevent the occurrence of fatal and catastrophic consequences resulting from equipment failures. Moreover, timely detection and resolution of faults can save significant costs and time for organizations. The failure of these systems not only poses risks to operators but can also lead to substantial delays in the advanced manufacturing process, imposing substantial financial burdens on organizations. Therefore, a methodology is needed that can be used to avoid the adverse effects of equipment failure of industrial control systems to achieve smooth advanced manufacturing operations. To achieve this, the methodology should be able to detect the abnormal behaviour of the system at very early stages for predictive maintenance. This methodology can be designed using an extremely popular concept known as the Digital Twin, which has gained significant importance in the era of Industry 4.0. In this research, artificial intelligence techniques will be employed to develop a highly accurate and detailed digital twin model. This model will serve as a valuable tool for predictive maintenance in complex industrial control systems, facilitating the achievement of smooth and uninterrupted advanced manufacturing processes. Also, the performance of the proposed Digital Twin model will be compared with state-of-the-art anomaly detection approaches. The digital twin, utilizing the proposed algorithms, will not only be able to detect anomalies but also quantify their severity, classifying them into different levels such as minor, severe, and faulty operations. Furthermore, the research addresses the generalization challenges faced by state-of-the-art approaches, showcasing the digital twin's ability to effectively classify unseen data as healthy or anomalous. The results obtained from the analysis and comparison of state-of-the-art approaches with the proposed algorithms clearly demonstrate the methodology's capability to detect anomalies, quantify their level, and classify them accurately and effectively in real-world data. This validation underscores the robustness and reliability of the developed methodology, further solidifying its potential as a valuable tool for predictive maintenance in complex industrial control systems.
- Description: Masters by Research
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