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Mrs. Gabriela Ahmadi-Assalemi
University of Wolverhampton

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0 CNI
0 Cyber-Physical Systems
0 Digital Forensics
0 Incident Response
0 Smart Cities

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Review
Published: 13 August 2020 in Smart Cities
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The world is experiencing a rapid growth of smart cities accelerated by Industry 4.0, including the Internet of Things (IoT), and enhanced by the application of emerging innovative technologies which in turn create highly fragile and complex cyber–physical–natural ecosystems. This paper systematically identifies peer-reviewed literature and explicitly investigates empirical primary studies that address cyber resilience and digital forensic incident response (DFIR) aspects of cyber–physical systems (CPSs) in smart cities. Our findings show that CPSs addressing cyber resilience and support for modern DFIR are a recent paradigm. Most of the primary studies are focused on a subset of the incident response process, the “detection and analysis” phase whilst attempts to address other parts of the DFIR process remain limited. Further analysis shows that research focused on smart healthcare and smart citizen were addressed only by a small number of primary studies. Additionally, our findings identify a lack of available real CPS-generated datasets limiting the experiments to mostly testbed type environments or in some cases authors relied on simulation software. Therefore, contributing this systematic literature review (SLR), we used a search protocol providing an evidence-based summary of the key themes and main focus domains investigating cyber resilience and DFIR addressed by CPS frameworks and systems. This SLR also provides scientific evidence of the gaps in the literature for possible future directions for research within the CPS cybersecurity realm. In total, 600 papers were surveyed from which 52 primary studies were included and analysed.

ACS Style

Gabriela Ahmadi-Assalemi; Haider Al-Khateeb; Gregory Epiphaniou; Carsten Maple. Cyber Resilience and Incident Response in Smart Cities: A Systematic Literature Review. Smart Cities 2020, 3, 894 -927.

AMA Style

Gabriela Ahmadi-Assalemi, Haider Al-Khateeb, Gregory Epiphaniou, Carsten Maple. Cyber Resilience and Incident Response in Smart Cities: A Systematic Literature Review. Smart Cities. 2020; 3 (3):894-927.

Chicago/Turabian Style

Gabriela Ahmadi-Assalemi; Haider Al-Khateeb; Gregory Epiphaniou; Carsten Maple. 2020. "Cyber Resilience and Incident Response in Smart Cities: A Systematic Literature Review." Smart Cities 3, no. 3: 894-927.

Chapter
Published: 07 April 2020 in Advanced Sciences and Technologies for Security Applications
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Precision healthcare is an emerging concept that will see technology-driven digital transformation of the health service. It enables customised patient outcomes via the development of novel, targeted medical approaches with a focus on intelligent, data-centric smart healthcare models. Currently, precision healthcare is seen as a challenging model to apply due to the complexity of the healthcare ecosystem, which is a multi-level and multifaceted environment with high real-time interactions among disciplines, practitioners, patients and discrete computer systems. Digital Twins (DT) pairs individual physical artefacts with digital models reflecting their status in real-time. Creating a live-model for healthcare services introduces new opportunities for patient care including better risk assessment and evaluation without disturbing daily activities. In this article, to address design and management in this complexity, we examine recent work in Digital Twins (DT) to investigate the goals of precision healthcare at a patient and healthcare system levels. We further discuss the role of DT to achieve precision healthcare, proposed frameworks, the value of active participation and continuous monitoring, and the cyber-security challenges and ethical implications for this emerging paradigm.

ACS Style

Gabriela Ahmadi-Assalemi; Haider Al-Khateeb; Carsten Maple; Gregory Epiphaniou; Zhraa A. Alhaboby; Sultan Alkaabi; Doaa Alhaboby. Digital Twins for Precision Healthcare. Advanced Sciences and Technologies for Security Applications 2020, 133 -158.

AMA Style

Gabriela Ahmadi-Assalemi, Haider Al-Khateeb, Carsten Maple, Gregory Epiphaniou, Zhraa A. Alhaboby, Sultan Alkaabi, Doaa Alhaboby. Digital Twins for Precision Healthcare. Advanced Sciences and Technologies for Security Applications. 2020; ():133-158.

Chicago/Turabian Style

Gabriela Ahmadi-Assalemi; Haider Al-Khateeb; Carsten Maple; Gregory Epiphaniou; Zhraa A. Alhaboby; Sultan Alkaabi; Doaa Alhaboby. 2020. "Digital Twins for Precision Healthcare." Advanced Sciences and Technologies for Security Applications , no. : 133-158.

Review
Published: 07 April 2020 in Cyberspace
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Forensic age estimation is usually requested by courts, but applications can go beyond the legal requirement to enforce policies or offer age-sensitive services. Various biological features such as the face, bones, skeletal and dental structures can be utilised to estimate age. This article will cover how modern technology has developed to provide new methods and algorithms to digitalise this process for the medical community and beyond. The scientific study of Machine Learning (ML) have introduced statistical models without relying on explicit instructions, instead, these models rely on patterns and inference. Furthermore, the large-scale availability of relevant data (medical images) and computational power facilitated by the availability of powerful Graphics Processing Units (GPUs) and Cloud Computing services have accelerated this transformation in age estimation. Magnetic Resonant Imaging (MRI) and X-ray are examples of imaging techniques used to document bones and dental structures with attention to detail making them suitable for age estimation. We discuss how Convolutional Neural Network (CNN) can be used for this purpose and the advantage of using deep CNNs over traditional methods. The article also aims to evaluate various databases and algorithms used for age estimation using facial images and dental images.

ACS Style

Sultan Alkaabi; Salman Yussof; Haider Al-Khateeb; Gabriela Ahmadi-Assalemi; Gregory Epiphaniou. Deep Convolutional Neural Networks for Forensic Age Estimation: A Review. Cyberspace 2020, 375 -395.

AMA Style

Sultan Alkaabi, Salman Yussof, Haider Al-Khateeb, Gabriela Ahmadi-Assalemi, Gregory Epiphaniou. Deep Convolutional Neural Networks for Forensic Age Estimation: A Review. Cyberspace. 2020; ():375-395.

Chicago/Turabian Style

Sultan Alkaabi; Salman Yussof; Haider Al-Khateeb; Gabriela Ahmadi-Assalemi; Gregory Epiphaniou. 2020. "Deep Convolutional Neural Networks for Forensic Age Estimation: A Review." Cyberspace , no. : 375-395.