Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection

The introduction sets the stage for exploring collaborative approaches to bolstering smart vehicle cybersecurity through AI-driven threat detection. As the automotive industry increasingly adopts connected and automated vehicles (CAVs), the need for robust cybersecurity measures becomes paramount. With the emergence of new vulnerabilities and security requirements, the integration of advanced technologies such as 5G networks, blockchain, and quantum computing presents promising avenues for enhancing CAV cybersecurity . Additionally, the roadmap for cybersecurity in autonomous vehicles emphasizes the importance of efficient intrusion detection systems and AI-based techniques, along with the integration of secure hardware, software stacks, and advanced threat intelligence to address cybersecurity challenges in future autonomous vehicles.

Enhancing Wireless Sensor Network Security through Integration with the ServiceNow Cloud Platform

Wireless Sensor Networks (WSNs) continue to experience rapid developments and integration into modern-day applications. Overall, WSNs collect and process relevant data through sensors or nodes and communicate with different networks for superior information management. Nevertheless, a primary concern relative to WSNs is security. Considering the high constraints on throughput, battery, processing power, and memory, typical security procedures present limitations for application in WSNs. This research focuses on the integration of WSNs with the cloud platform, specifically to address these security risks. The cloud platform also adopts a security-driven approach and has attracted many applications across various sectors globally. This research specifically explores how cloud computing could be exploited to impede Denial of Service attacks from endangering WSNs. WSNs are now deployed in various low-powered applications, including disaster management, homeland security, battlefield surveillance, agriculture, and the healthcare industry. WSNs are distinguished from traditional networks by the numerous wireless connected sensors being deployed to conduct an assigned task. In testing scenarios, the size of WSNs ranges from a few to several thousand. The overarching requirements of WSNs include rapid processing of collected data, low-cost installation and maintenance, and low latency in network operations. Given that a substantial amount of WSN applications are used in high-risk and volatile environments, they must effectively address security concerns. This includes the secure movement, storage, and communication of data through networks, an environment in which WSNs are notably vulnerable. The limitations of WSNs have meant that they are predominantly used in unsecured applications despite positive advancements. This study explores methods for integrating the WSN with the cloud.