The arrival of autonomous vehicles (AVs) promises many great benefits, including increased safety and reduced energy consumption, pollution, and congestion. However, these engines have many security and privacy issues that could undermine the expected benefits if not addressed. AVs will provide new opportunities for hackers to carry out malicious attacks, posing a great threat to the future of mobility and data protection. The research trend in this field indicates that combining Blockchain and AI could bring strong protection for AVs against malicious attacks. Blockchain and AI have different working paradigms, but when merged, they can empower each other, and solve many security and privacy issues of AVs. AI can optimise the construction of the Blockchain to make it more efficient, secure and energy-saving, where Blockchain provides data immutability and trust mechanism for AI-based solutions and makes them more transparent, trustful, and explainable. Although some research is being conducted on this area, the topic of applying Blockchain and AI for securing AVs is not deeply investigated. In this paper, we explore the possible application of an amalgamation of Blockchain and AI solutions for securing AVs. We first introduce a classification of security and privacy threats that may arise from the application of AVs. Then, we provide an overview of recent literature regarding Blockchain and AI usage for securing AVs. Finally, we highlight limitations and challenges that may face the integration of Blockchain and AI with AVs based on our systemic review and suggest potential future directions for research in this field.

Cyberattacks have been rapidly increasing over the years, resulting to big financial losses to businesses for recovery, regulatory sanctions, as well as collateral damages, such as reputation and trust. In this respect, the maritime sector, which until now was considered safe due to the lack of Internet connectivity and the isolated nature of ships in the sea, is showing a 900% increase in cybersecurity breaches on operational technology as it enters the digital era. Although some research is being conducted in this area, maritime cybersecurity has not been deeply investigated. Hence, this paper provides a close investigation of the landscape of cybersecurity in the maritime sector with the aim of highlighting security problems and challenges. First, it explores the systems available on ships that could be targeted by attackers, their possible vulnerabilities that an attacker could exploit, the consequences if the system is accessed, and actual incidents. Then, it describes and analyses possible mitigation actions that can be utilised in advance to prevent such attacks. Finally, several challenges and open problems are discussed for future research.

The rapidly growing use of cloud computing raises security concerns. This study paper seeks to examine cloud security frameworks, addressing cloud-associated issues and suggesting solutions. This research provides greater knowledge of the various frameworks, assisting in making educated decisions about selecting and implementing suitable security measures for cloud-based systems. The study begins with introducing cloud technology, its issues and frameworks to secure infrastructure, and an examination of the various cloud security frameworks available in the industry. A full comparison is performed to assess the framework’s focus, scope, approach, strength, limitations, implementation steps and tools required in the implementation process. The frameworks focused on in the paper are COBIT5, NIST (National Institute of Standards and Technology), ISO (International Organization for Standardization), CSA (Cloud Security Alliance) STAR and AWS (Amazon Web Services) well-architected framework. Later, the study digs into identifying and analyzing prevalent cloud security issues. This contains attack vectors that are inherent in cloud settings. Plus, this part includes the risk factor of top cloud security threats and their effect on cloud platforms. Also, it presents ideas and countermeasures to reduce the observed difficulties.

The sharing of cyber-threat intelligence is an essential part of multi-layered tools used to protect systems and organisations from various threats. Structured standards, such as STIX, TAXII and CybOX, were introduced to provide a common means of sharing cyber-threat intelligence and have been subsequently much-heralded as the de facto industry standards. In this paper, we investigate the landscape of the available formats and languages, along with the publicly available sources of threat feeds, how these are implemented and their suitability for providing rich cyber-threat intelligence. We also analyse at a sample of cyber-threat intelligence feeds, the type of data they provide and the issues found in aggregating and sharing the data. Moreover, the type of data supported by various formats and languages is correlated with the data needs for several use cases related to typical security operations. The main conclusions drawn by our analysis suggest that many of the standards have a poor level of adoption and implementation, with providers opting for custom or traditional simple formats.

With the increase of IoT devices and technologies coming into service, Malware has risen as a challenging threat with increased infection rates and levels of sophistication. Without strong security mechanisms, a huge amount of sensitive data are exposed to vulnerabilities, and therefore, easily abused by cybercriminals to perform several illegal activities. Thus, advanced network security mechanisms that are able of performing a real-time traffic analysis and mitigation of malicious traffic are required. To address this challenge, we are proposing a novel IoT malware traffic analysis approach using deep learning and visual representation for faster detection and classification of new malware (zero-day malware). The detection of malicious network traffic in the proposed approach works at the package level, reducing significantly the time of detection with promising results due to the deep learning technologies used. To evaluate our proposed method performance, a dataset is constructed which consists of 1000 pcap files of normal and malware traffic that are collected from different network traffic sources. The experimental results of Residual Neural Network (ResNet50) are very promising, providing a 94.50% accuracy rate for detection of malware traffic.