Legacy systems in the aviation and military sectors present unique challenges when it comes to ensuring cybersecurity. According to De Otto at Wind River, using vulnerability scanners, static code analysis, and threat analysis methods like MITRE can help identify potential vulnerabilities in current developments. However, difficulties arise when dealing with legacy code written in languages like FORTRAN, which may require manual analysis that can be impractical, time-consuming, or expensive. Mitigating security risks in legacy systems can also be challenging, as mechanisms for updates may not be readily available. De Otto emphasizes that addressing security in legacy systems typically requires a case-by-case approach.
As the industry explores the potential of artificial intelligence (AI) and machine learning (ML), the adoption of these technologies in secure systems used in aviation and military applications may lag behind until their safety can be assured. De Otto notes that while AI is already being used in software development for security scanning, its deployment in operational systems is limited due to concerns about AI security and the challenges of protecting AI from external attacks. As AI continues to play a larger role, it will be crucial to incorporate it within the framework of proper regulations.
ML, particularly in the form of Generative Artificial Intelligence (GenAI), shows promise in enhancing intrusion detection and response capabilities. Jaenicke at Green Hills highlights the potential of ML for pattern matching and improving the efficiency of security measures. FPGAs are identified as ideal for high-performance, real-time computation in intrusion detection systems, offering enhanced security features for CPU systems. While GenAI is still in its early stages, it has the potential to provide valuable insights to security analysts regarding detected threats and recommended responses.
Edge computing, which involves processing data closer to the source rather than relying solely on centralized data centers, presents unique security challenges. The decentralized architecture of edge computing increases the attack surface, making it more difficult to protect the entire system. Data processed at the edge may be sensitive, requiring robust encryption to ensure data integrity and confidentiality. Edge devices, such as IoT sensors and mobile devices, often lack the security measures of traditional data center equipment, necessitating strong security measures like secure boot and firmware updates.
Wind River’s edge products, including VxWorks, Wind River Linux, and the Helix Virtualization Platform, offer the latest security technologies for customers to implement in their applications. De Otto emphasizes Wind River’s focus on security throughout the Software Development Life Cycle, aligning with established methodologies like NIST SP800-53 and NIST SP 800-218. By following secure development principles and responding to vulnerabilities, Wind River aims to enhance the security of edge computing systems and protect sensitive data processed at the edge.