Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions.

Introduction: The rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress.

Beyond Access to Sanitary Pads: A Comprehensive Analysis of Menstrual Health Scheme Impact Among Rural Girls in Northeast India.

Menstrual hygiene management among girls in rural India poses a substantial challenge for public health, education, and quality of life, exacerbated by limited access and affordability of menstrual products. In response to these issues, the Government of India initiated the Menstrual Hygiene Scheme (MHS) to enhance access and awareness. This study evaluates the impact of the MHS in Assam and Tripura designated" treatment states" with consistent pad supply from 2017 to 2021 compared to neighboring" control states" with negligible pad distribution.

MemberShield: A framework for federated learning with membership privacy.

Federated Learning (FL) allows multiple data owners to build high-quality deep learning models collaboratively, by sharing only model updates and keeping data on their premises. Even though FL offers privacy-by-design, it is vulnerable to membership inference attacks (MIA), where an adversary tries to determine whether a sample was included in the training data. Existing defenses against MIA cannot offer meaningful privacy protection without significantly hampering the model's utility and causing a non-negligible training overhead.

Development and validation of a survey instrument for reusable sanitary products toward sustainable menstrual hygiene.

The predominant use of disposable, non-organic menstrual products has significant environmental impacts due to waste and resource depletion. Concerns over the environmental, economic, and health implications of menstrual hygiene management (MHM) have highlighted the need to explore sustainable options like reusable sanitary pads (RSPs). Despite their benefits, the adoption of RSPs is limited by a lack of awareness, availability, cost, and research.

Green Intrusion Detection Systems: A Comprehensive Review and Directions.

Intrusion detection systems have proliferated with varying capabilities for data generation and learning towards detecting abnormal behavior. The goal of green intrusion detection systems is to design intrusion detection systems for energy efficiency, taking into account the resource constraints of embedded devices and analyzing energy-performance-security trade-offs. Towards this goal, we provide a comprehensive survey of existing green intrusion detection systems and analyze their effectiveness in terms of performance, overhead, and energy consumption for a wide variety of low-power embedded systems such as the Internet of Things (IoT) and cyber physical systems.

Fake news research trends, linkages to generative artificial intelligence and sustainable development goals.

In the digital age, where information is a cornerstone for decision-making, social media's not-so-regulated environment has intensified the prevalence of fake news, with significant implications for both individuals and societies. This study employs a bibliometric analysis of a large corpus of 9678 publications spanning 2013-2022 to scrutinize the evolution of fake news research, identifying leading authors, institutions, and nations. Three thematic clusters emerge: Disinformation in social media, COVID-19-induced infodemics, and techno-scientific advancements in auto-detection.

Darkweb research: Past, present, and future trends and mapping to sustainable development goals.

The Darkweb, part of the deep web, can be accessed only through specialized computer software and used for illegal activities such as cybercrime, drug trafficking, and exploitation. Technological advancements like Tor, bitcoin, and cryptocurrencies allow criminals to carry out these activities anonymously, leading to increased use of the Darkweb. At the same time, computers have become an integral part of our daily lives, shaping our behavior, and influencing how we interact with each other and the world.

Blockchain and PUF-based secure key establishment protocol for cross-domain digital twins in industrial Internet of Things architecture.

Introduction: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical and digital domains is crucial for unlocking the full potential of the IIoT, and digital twins can facilitate this integration by providing a virtual representation of real-world entities.

Objectives: By combining digital twins with the IIoT, industries can simulate, predict, and control physical behaviors, enabling them to achieve broader value and support industry 4.

Fortifying Smart Home Security: A Robust and Efficient User-Authentication Scheme to Counter Node Capture Attacks.

In smart home environments, the interaction between a remote user and devices commonly occurs through a gateway, necessitating the need for robust user authentication. Despite numerous state-of-the-art user-authentication schemes proposed over the years, these schemes still suffer from security vulnerabilities exploited by the attackers. One severe physical attack is the node capture attack, which allows adversaries to compromise the security of the entire scheme.

Exploring the Clinical Translation of Generative Models Like ChatGPT: Promise and Pitfalls in Radiology, From Patients to Population Health.

Generative artificial intelligence (AI) tools such as GPT-4, and the chatbot interface ChatGPT, show promise for a variety of applications in radiology and health care. However, like other AI tools, ChatGPT has limitations and potential pitfalls that must be considered before adopting it for teaching, clinical practice, and beyond. We summarize five major emerging use cases for ChatGPT and generative AI in radiology across the levels of increasing data complexity, along with pitfalls associated with each.

Enhancing Security and Privacy in Healthcare Systems Using a Lightweight RFID Protocol.

Exploiting Radio Frequency Identification (RFID) technology in healthcare systems has become a common practice, as it ensures better patient care and safety. However, these systems are prone to security vulnerabilities that can jeopardize patient privacy and the secure management of patient credentials. This paper aims to advance state-of-the-art approaches by developing more secure and private RFID-based healthcare systems.

Challenges in cybersecurity: Lessons from biological defense systems.

Defending against novel, repeated, or unpredictable attacks, while avoiding attacks on the 'self', are the central problems of both mammalian immune systems and computer systems. Both systems have been studied in great detail, but with little exchange of information across the different disciplines. Here, we present a conceptual framework for structured comparisons across the fields of biological immunity and cybersecurity, by framing the context of defense, considering different (combinations of) defensive strategies, and evaluating defensive performance.

Confirmatory and validation studies on experimental self-efficacy scale with applications to multiple scientific disciplines.

Laboratory education is essential for enhancing both the understanding of concepts and skills of students. A significant barrier to excelling in laboratory practices relates to a lack of self-efficacy. Being complementary to mainstream theoretical learning, the contribution of laboratory education to impart knowledge and hands-on proficiency is often under-represented.

A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations.

Background: Frailty is frequently used by clinicians to help determine surgical outcomes. The frailty index, which represents the frequency of frailty indicators present in an individual, is one method for evaluating patient frailty to predict surgical outcomes. However, the frailty index treats all indicators of frailty that are used in the index as equivalent.

Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues.

A significant study has been undertaken in the areas of health care and administration of cutting-edge artificial intelligence (AI) technologies throughout the previous decade. Healthcare professionals studied smart gadgets and other medical technologies, along with the AI-based Internet of Things (IoT) (AIoT). Connecting the two regions makes sense in terms of improving care for rural and isolated resident individuals.

Ali-Bey - an open collaborative georeferencing web application.

Background: Georeferencing preserved specimens represents a major effort at the Museu de Ciències Naturals de Barcelona (MCNB), given the available resources and limited staff that can be allocated to the task. Georeferencing is a labour-intensive and hard-to-automate task that requires software tools that can help in making it as efficient as possible. The tool we present, Ali-Bey, has been slowly developed over 15 years and its functionalities have been gradually built in a process of development, testing, use in production and refinement, rather than as a single development cycle out of a comprehensive specifications requirement document.

An Insight into the Machine-Learning-Based Fileless Malware Detection.

In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware. As file-based malware depends on files to spread itself, on the other hand, fileless malware does not require a traditional file system and uses benign processes to carry out its malicious intent.

Longitudinal study of teacher acceptance of mobile virtual labs.

Synthesizing the advancements in technology with classroom practices depends considerably on teachers acceptance of such internet and communication technology (ICT) tools. Adequate teacher training and upgrading of their IT skills are not prioritized in developing economies leading to poor adoption of emerging technology assisted pedagogic interventions. This paper investigated the underlying characteristics of teachers acceptance of mobile friendly virtual laboratories (M-VLs) as part of a longitudinal study conducted over 5 years covering both pre-pandemic and pandemic periods.

Getting Smarter about Smart Cities: Improving Data Security and Privacy through Compliance.

Smart cities assure the masses a higher quality of life through digital interconnectivity, leading to increased efficiency and accessibility in cities. In addition, a huge amount of data is being exchanged through smart devices, networks, cloud infrastructure, big data analysis and Internet of Things (IoT) applications in the various private and public sectors, such as critical infrastructures, financial sectors, healthcare, and Small and Medium Enterprises (SMEs). However, these sectors require maintaining certain security mechanisms to ensure the confidentiality and integrity of personal and critical information.

Theoretical perspectives of parental influence on adolescent cyber behaviour: A bi-national Instagram-based study.

An increase in adolescent social media use has exacerbated cyberbullying globally. Instagram has the highest percentage of adolescent users experiencing cybervictimisation. While past research has delved into self-driven or peer-driven motivations of cyberbullying, theory-driven research characterising external factors is integral to understanding the psyche of cyberbullies, victims, or bystanders.