Cloud computing provides the flexible architecture where data and resources are dispersed at various locations and are accessible from various industrial environments. Cloud computing has changed the using, storing, and sharing of resources such as data, services, and applications for industrial applications. During the last decade, industries have rapidly switched to cloud computing for having more comprehensive access, reduced cost, and increased performance. In addition, significant improvement has been observed in the internet of things (IoT) with the integration of cloud computing. However, this rapid transition into the cloud raised various security issues and concerns. Traditional security solutions are not directly applicable and sometimes ineffective for cloud-based systems. Cloud platforms’ challenges and security concerns have been addressed during the last three years, despite the successive use and proliferation of multifaceted cyber weapons. The rapid evolution of deep learning (DL) in the artificial intelligence (AI) domain has brought many benefits that can be utilized to address industrial security issues in the cloud. The findings of the proposed research include the following: we present a comprehensive survey of enabling cloud-based IoT architecture, services, configurations, and security models; the classification of cloud security concerns in IoT into four major categories (data, network and service, applications, and people-related security issues), which are discussed in detail; we identify and inspect the latest advancements in cloud-based IoT attacks; we identify, discuss, and analyze significant security issues in each category and present the limitations from a general, artificial intelligence and deep learning perspective; we provide the technological challenges identified in the literature and then identify significant research gaps in the IoT-based cloud infrastructure to highlight future research directions to blend cybersecurity in cloud.

The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities.

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.

With the alarmingly increasing rate of cybercrimes worldwide, there is a dire need to combat cybercrimes timely and effectively. Cyberattacks on computing machines leave certain artifacts on target device storage that can reveal the identity and behavior of cyber-criminals if processed and analyzed intelligently. Forensic agencies and law enforcement departments use several digital forensic toolkits, both commercial and open-source, to examine digital evidence. The proposed research survey focuses on identifying the current state-of-the-art digital forensics concepts in existing research, sheds light on research gaps, presents a detailed introduction of different computer forensic domains and forensic toolkits used for computer forensics in the current era. The proposed survey also presents a comparative analysis based on the tool’s characteristics to facilitate investigators in tool selection during the forensics process. Finally, the proposed survey identifies and derives current challenges and future research directions in computer forensics.

Artificial intelligence (AI) transforms aviation, driving new, safe, and secure solutions. In this review, we dive into the transformative role that machine learning and deep learning play in critical aviation applications, such as predictive maintenance, anomaly detection, flight path optimization, and pilot assistance. AI-driven systems use real-time data to predict equipment failures and speed deviation from normal flight behavior and to optimize resource management to improve operational efficiency. AI helps strengthen cybersecurity in aviation security, enhances the accuracy of passenger screening, and boosts air traffic management with predictive analytics. There has been a lot of progress already, but data quality, scalability, governing frameworks, and ethical concerns are some factors that make widespread adoption a challenge. Further advances are anticipated in the coming years due to emerging trends like explainable AI, quantum computing, and collaborative AI. The lesson that this study illustrates is that there remains much work to be done to ensure that the full potential of AI can be used to deliver safer, more secure, and faster air travel.

Automatic Dependent Surveillance-Broadcast (ADS-B) is a vital communication protocol within air traffic control (ATC) systems. Unlike traditional technologies, ADS-B utilizes the Global Positioning System (GPS) to deliver more accurate and precise location data while reducing operational and deployment costs. It enhances radar coverage and serves as a standalone solution in areas lacking radar services. Despite these advantages, ADS-B faces significant security vulnerabilities due to its open design and the absence of built-in security features. Given its critical role, developing an advanced security framework to classify ADS-B messages and identify various attack types is essential to safeguard the system. This research makes several key contributions to address these challenges. First, it presents a comprehensive review of state-of-the-art machine learning and deep learning techniques, critically analyzing existing methodologies for ADS-B intrusion detection. Second, a detailed attack model is developed, categorizing potential threats and aligning them with key security requirements, including confidentiality, integrity, availability, and authentication. Third, the study proposes a robust and accurate Intrusion Detection System (IDS) using three advanced deep learning models—TabNet, Neural Oblivious Decision Ensembles (NODE), and DeepGBM—to classify ADS-B messages and detect specific attack types. The models are evaluated using standard metrics, including accuracy, precision, recall, and F1-score. Among the tested models, DeepGBM achieves the highest accuracy at 98%, outperforming TabNet (92%) and NODE (96%). The findings offer valuable insights into ADS-B security and define essential requirements for a future security framework, contributing actionable recommendations for mitigating threats in this critical communication protocol.