Neighbor Discovery Protocol (NDP) is a network protocol used in IPv6 networks to manage communication between neighboring devices. NDP is responsible for mapping IPv6 addresses to MAC addresses and discovering the availability of neighboring devices on the network. The main risk of deploying NDP on public networks is the potential for hackers or attackers to launch various types of attacks, such as address spoofing attacks, denial-of-service attacks, and man-in-the-middle attacks. Although Secure Neighbor Discovery (SEND) is implemented to secure NDP, its complexity and cost hinder its widespread deployment. This research emphasizes the potential hazard of deploying IPv6 networks in public spaces, such as airports, without protecting NDP messages. These risks have the potential to crash the entire local network. To demonstrate these risks, the GNS3 testbed environment is used to generate NDP attacks and capture the resulting packets using Wireshark for analysis. The analysis results reveal that with just a few commands, attackers can execute various NDP attacks. This highlights the need to protect against the potential issues that come with deploying IPv6 on widely accessible public networks. In addition, the analysis result shows that NDP attacks have behavior that can be used to define various NDP attacks.

Autonomous vehicles and intelligent traffic transportation are widely investigated for road networks. Context-aware traffic light scheduling algorithms determine signal phases by analyzing the real-time characteristics and contextual information of competing traffic flows. The context of traffic flows mainly considers the existence of regular, emergency, or heavy vehicles. This is an important factor in setting the phases of the traffic light schedule and assigning a high priority for emergency vehicles to pass through the signalized intersection first. VANET technology, through its communication capabilities and the exchange of data packets among moving vehicles, is utilized to collect real-time traffic information for the analyzed road scenarios. This introduces an attractive environment for hackers, intruders, and criminals to deceive drivers and intelligent infrastructure by manipulating the transmitted packets. This consequently leads to the deployment of less efficient traffic light scheduling algorithms. Therefore, ensuring secure communications between traveling vehicles and verifying the integrity of transmitted data are crucial. In this work, we investigate the possible attacks on the integrity of transferred messages and vehicles’ identities and their effects on the traffic light schedules. Then, a new secure context-aware traffic light scheduling system is proposed that guarantees the integrity of transmitted messages and verifies the vehicles’ identities. Finally, a comprehensive series of experiments were performed to assess the proposed secure system in comparison to the absence of security mechanisms within a simulated road intersection. We can infer from the experimental study that attacks on the integrity of vehicles have different effects on the efficiency of the scheduling algorithm. The throughput of the signalized intersection and the waiting delay time of traveling vehicles are highly affected parameters.

The Kingdom of Saudi Arabia’s ambitious Vision 2030 initiative aims to revolutionize its economy through technological innovation, with artificial intelligence (AI) playing a pivotal role. This transformation, while promising enhanced efficiency, sustainability, and economic diversification, also introduces significant cybersecurity risks. These include threats to data privacy, potential biases in AI algorithms, and vulnerabilities to cyberattacks. Addressing these challenges is critical for ensuring the secure and ethical deployment of AI technologies. This paper provides an in-depth analysis of Saudi Arabia’s legal and regulatory frameworks governing AI adoption and cybersecurity. It explores the strategic roles of the Saudi Data and Artificial Intelligence Authority (SDAIA) and the National Cybersecurity Authority (NCA) in crafting policies that safeguard against cyber threats while fostering innovation. Additionally, the study examines data protection laws, encryption standards, incident response protocols, and liability frameworks that collectively support a resilient cyber environment. Through a comprehensive review, the paper highlights the importance of robust legal structures, cross-sector collaboration, and proactive cybersecurity measures in realizing the full potential of AI in alignment with Vision 2030’s goals, thus offering insights into balancing technological advancement with security imperatives in a rapidly evolving digital landscape.