D. Bourne’s research while affiliated with United Arab Emirates University and other places

The significance of virtual private networks (VPN) has increased rapidly over time. Enterprises utilize IPsec and other VPN technologies to provide a secure connection to mission-critical resources over the internet. The various sub-protocols of IPsec i.e., Encapsulation Security Payload (ESP) and Authentication Header (AH) provide confidentiality and integrity through the encryption mechanisms. Unfortunately, recent studies indicate that the traditional encryption techniques employed in IPsec could face a potential threat from the progress of quantum computing. To address this issue, this research proposes an improved Encapsulation Security Payload for Quantum Safety (ESPQ) which can serve as a post-quantum encryption and authentication algorithm for VPN networks. The proposed algorithm incorporates modern quantum-safe algorithms into its framework. Through extensive research and analysis of performance metrics, it has become evident that ESPQ surpasses the conventional methodology of ESP both in terms of heightened security safeguards and reduced system load requirements.

With the emergence of new technologies, the threat of cyberattacks is becoming more prominent. Apart from direct attacks, hackers also try to steal personal data through indirect methods, i.e., social engineering. This threatens not only single individuals but enterprises as well. As a form of protection, it’s important to raise awareness about cybercrime and social engineering. However, sometimes humans can be tricked by smart social engineering attacks. This calls for the need to improve the defense through the implementation of other methods such as Data Loss Prevention (DLP) or new customized monitoring systems that would further mitigate the risk of personal information leakage. Therefore, this paper implements a network traffic monitoring system at the network edge that inspects packets and classifies personal information based on an ML model trained on synthetic data.