Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.

The progress made in Information and Communication Technologies (ICT) has played a crucial role in turning the Internet of Things (IoT) into a reality. IoT is an emerging technology that refers to networks of interconnected and Internet-enabled objects equipped with sensors, processors, and actuators that interact with each other to create significant collaboration and interaction environments. The field of education is one of the areas where IoT can be applied. However, the implementation of IoT poses security and privacy risks, such as unauthorized access, denial-of-service (DoS) attacks, and interference with wireless signals where IoT devices collect a significant amount of data, including user’s personal information like identity, location, and daily behavior. Therefore, it is crucial to protect users’ privacy in IoT applications. The innovative Ubiquitous Learning Environments (ULEs) have been created by ubiquitous computing technologies (mobile, wireless, network), which provide learners with learning experiences beyond the traditional classroom in both the real and virtual worlds. Ubiquitous learning (U-learning) is an emerging technology as a result of the tremendous technological revolution of ICT. U-learning is a novel learner-centered approach that aims to enhance learning, motivation, and creativity by utilizing innovative technology and IoT. U-learning enables learners to access the appropriate learning content, collaborate with the right learning partners, and engage in self-learning at the right time and place in a ubiquitous computing environment. To support learners in developing their social skills, in this study a framework for implementing the ULE based on the Internet of Things is designed, which consists of three main layers: perception, network, and application. The article explores the effects of IoT on education and how U-learning, which incorporates IoT to enhance learning experiences, has the potential to replace traditional classroom learning. Furthermore, the article addresses privacy preservation measures for different layers within the IoT environment and ULE. A framework for implementing the ULE model is in progress, which is a part of our future work.

In decades, technology and artificial intelligence have significantly impacted aspects of life. One noteworthy development is ChatGPT, an AI-based model that has created a revolution and attracted attention from researchers, academia, and organizations in a short period of time. Experts predict that ChatGPT will continue advancing, bringing about a leap in artificial intelligence. It is believed that this technology holds the potential to address cybersecurity concerns, protect against threats and attacks, and overcome challenges associated with our increasing reliance on technology and the internet. This technology may change our lives in productive and helpful ways, from the interaction with other AI technologies to the potential for enhanced personalization and customization to the continuing improvement of language model performance. While these new developments have the potential to enhance our lives, it is our responsibility as a society to thoroughly examine and confront the ethical and societal impacts. This research delves into the state of ChatGPT and its developments in the fields of artificial intelligence and security. It also explores the challenges faced by ChatGPT regarding privacy, data security, and potential misuse. Furthermore, it highlights emerging trends that could influence the direction of ChatGPT's progress. This paper also offers insights into the implications of using ChatGPT in security contexts. Provides recommendations for addressing these issues. The goal is to leverage the capabilities of AI-powered conversational systems while mitigating any risks. Doi: 10.28991/HIJ-2024-05-01-010 Full Text: PDF

As cyber threats continue to increase in sophistication, the security of Operational Technology (OT) environments has become a paramount priority for organizations in various sectors. OT systems, including Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems, are vital in the functioning of critical infrastructure but often lack robust security due to legacy security weaknesses. This research discusses the implementation of Multi-Factor Authentication (MFA) as a baseline strategy for enhancing the security of such systems. We recognize the distinct cybersecurity issues of OT environments, especially the use of legacy hardware that does not have contemporary security mechanisms. By suggesting an extensive framework for implementing MFA, this research offers a multi-layered system that incorporates knowledge-based, possession-based, and biometric authentication techniques. We further stress the need for role-based access control, ongoing monitoring, and user training to enhance security mechanisms. Using case studies and real-world examples, we show how MFA can be used to counter unauthorized access and increase system resilience. We present actionable recommendations for organizations wishing to deploy MFA, including mitigation strategies that reduce identified vulnerabilities to acceptable levels for critical infrastructure as a foundation of their cybersecurity approach, with the ultimate goal of safeguarding critical infrastructure and sensitive information in a hyper-connected world. Our research not only adds to the body of knowledge but also acts as a guide to deploying stringent security controls in OT networks.