The Use of AI in Managing Big Data Analysis Demands: Status and Future updates Directions

In the modern age, the context of health, energy, environment, climate crisis, and global Covid-19 pandemic, managing Big Data demands via Sustainable Development Goals and disease mitigation supported by Artificial Intelligence, present significant challenges for a given territory or national boundaries' policies, legal systems, energy infrastructure, societal cohesion, internal and external national security. We look at policy, technical, and legal applications alongside ramifications of relevant policies and practices to highlight key challenges from a global and societal context. This review contributes to developing further awareness of the complexity and demands on policy and technology. In the long term due to these significant changes, inferences of multifaceted policy and data acquisition could present additional compounding challenges regarding civil liberties, data privacy law, and equitable health outcomes, whilst implementing continually evolving policies, practices, and techniques that can weaken infrastructure and present cyber-attack vulnerabilities. As a consequence of local, regional, and international paradigm shifts, Blockchain and Smart Contracts are suggested as part of a solution in providing data protection, transparency, and validity with transactional data to enable further trust between private and public sectors during times of crisis and technological transition processes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Correction to: Privacy and security challenges and opportunities for IoT technologies during and beyond COVID-19

The book was inadvertently published with an incorrect name information for one of the Chapter author as "M. Yousef", whereas it should be "M. Yousif" in the front matter and Chapter 3. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. All rights reserved.

Contemporary Issues in Child Protection: Police Use of Artificial Intelligence for Online Child Protection in the UK

This chapter investigates Artificial Intelligence (AI) inspired approaches used by the police in protecting children online. The reviewed approaches are successful in most of the situations but have their own weaknesses. As such consideration is required for all stakeholders within the child protection arena. The utmost duty to protect children lies with all, irrespective of whether the abuse occurred on or offline. The reporting and intervention on child abuse cases were based on the community, as this was mostly offline perpetrated by parents or caregivers. However, with the advent of technology and the increasing use of the internet by children for several reasons, it has shifted most abuses from offline to online. The law enforcement authorities such as police plays a vital role in protecting children online and can apply different approaches compared to other agencies such as Social Services, Health, and Education. However, Government recommendations for a joint working response mean that all child-protected agencies need to work together in the process of protecting children (HM Government in Working together to safeguard children: a guide to inter-agency working to safeguard and promote the welfare of children, Department for Children, Schools, and Families, London, 2010). However, with the emergence of COVID-19 and the high reliance on the internet by children, it meant that the police must adapt to the changes and rely on advanced technologies such as AI. The UK Police force is stretched due to a lack of financial and human resources, which means that alternative intervention methods are applied in monitoring and attacking online child abuse. This chapter challenges the use of AI unilaterally in predicting and identifying online abuse as opposed to face-to-face investigation and intervention. Though AI can be helpful, it has limitations that can impact on protecting children online as discussed in this chapter. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Minimization of Cyber Security Threats Caused by COVID-19 Pandemic

Whilst the world is preoccupied in its struggle with the Coronavirus pandemic, cyber-criminals are busy every day, spreading their own viruses, by phishing emails, data breaches, frauds, denials of service, and taking advantage of the vulnerabilities created by this crisis. In many ways, we, as a nation, are handing over our data without realizing it, without fully thinking it through or even being aware of cyber threats, which will ultimately have a tremendous impact on the governments and citizens both personally and at work. The goal of this paper is to investigate the correlation between the cyberattacks before the coronavirus and during the coronavirus in order to build an understanding of what is happening. To optimize cyber security and provide effective ways to tackle cyber security attacks during COVID-19 or something similar, we need to consider extra precautions and take a more secure approach to protection. To minimize the universal risks of data breaches and other cyber incidents, we need to enforce practical steps to deal with and if possible limit those risks. This requires not only thoughtful consideration, but also a good understanding of the opportunities that COVID-19 provides to cyber-criminals. The aim of this research paper is to investigate the growth of and reasons for the increase of cyberattacks during the COVID-19 pandemic. In order to make better cyber security decisions, we need to address and maximize the level of cyber security awareness and precaution taken during COVID-19. A set of practical steps to minimize the risk of cyber-attack is provided to compensate for the vulnerabilities associated with COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Cost benefits of using machine learning features in NIDS for cyber security in UK small medium enterprises (SME)

Cyber security has made an impact and has challenged Small and Medium Enterprises (SMEs) in their approaches towards how they protect and secure data. With an increase in more wired and wireless connections and devices on SME networks, unpredictable malicious activities and interruptions have risen. Finding the harmony between the advancement of technology and costs has always been a balancing act particularly in convincing the finance directors of these SMEs to invest in capital towards their IT infrastructure. This paper looks at various devices that currently are in the market to detect intrusions and look at how these devices handle prevention strategies for SMEs in their working environment both at home and in the office, in terms of their credibility in handling zero-day attacks against the costs of achieving so. The experiment was set up during the 2020 pandemic referred to as COVID-19 when the world experienced an unprecedented event of large scale. The operational working environment of SMEs reflected the context when the UK went into lockdown. Pre-pandemic would have seen this experiment take full control within an operational office environment;however, COVID-19 times has pushed us into a corner to evaluate every aspect of cybersecurity from the office and keeping the data safe within the home environment. The devices chosen for this experiment were OpenSource such as SNORT and pfSense to detect activities within the home environment, and Cisco, a commercial device, set up within an SME network. All three devices operated in a live environment within the SME network structure with employees being both at home and in the office. All three devices were observed from the rules they displayed, their costs and machine learning techniques integrated within them. The results revealed these aspects to be important in how they identified zero-day attacks. The findings showed that OpenSource devices whilst free to download, required a high level of expertise in personnel to implement and embed machine learning rules into the business solution even for staff working from home. However, when using Cisco, the price reflected the buy-in into this expertise and Cisco’s mainframe network, to give up-to-date information on cyber-attacks. The requirements of the UK General Data Protection Regulations Act (GDPR) were also acknowledged as part of the broader framework of the study. Machine learning techniques such as anomaly-based intrusions did show better detection through a commercially subscription-based model for support from Cisco compared to that of the OpenSource model which required internal expertise in machine learning. A cost model was used to compare the outcome of SMEs’ decision making, in getting the right framework in place in securing their data. In conclusion, finding a balance between IT expertise and costs of products that are able to help SMEs protect and secure their data will benefit the SMEs from using a more intelligent controlled environment with applied machine learning techniques, and not compromising on costs. © 2021 by the authors.

Security and Privacy Issues Associated with Coronavirus Diagnosis and Prognosis

The urgency of the need to manage and find a cure for the COVID-19 has made it necessary to share information. However, sharing information involves potential risks that are inevitably likely to infringe individual privacy. Therefore, whether permissible under extenuation circumstances or not, sharing and handling of information for medical diagnosis and prognosis need consideration without ignoring the need to protect privacy. This makes it important to strike a balance between protecting individual privacy and collecting information to combat the virus, the responsibility for doing so rests with the state. However, circumstances in which the COVID-19 pandemic appears to be accelerating, the medical professionals and the government seem to be focusing more on collecting information that could be used to limit the extent of the outbreak and mitigate the risks. Such a strategy overrides perception of the need to protect personal privacy. This paper discusses the security and privacy challenges associated with SARS-CoV-2 diagnosis and prognosis using case studies from different countries. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.