Leveraging Deep Learning for Enhancing Cyber Defence Against Pegasus Spyware Attack

Advanced cyber threats such as Pegasus Spyware show us why cybersecurity should be unbreachable. This paper attempts to provide an insight into how such cyber defences can be improved using deep learning techniques with help of Pegasus Spyware. In this paper, we provided an investigation on the threat landscape and Pegasus advanced capabilities which leads to proposing a Deep learning-based solution for identifying & neutralizing such sophisticated attacks. Basically, you need neural networks to empower an intrusion detection and prevention system that can withstand threats like Pegasus as well. This effort is critical to advance cybersecurity in support of cyber warfare blending the best practices and processes around digital ecosystems. New digital devices and advancements in technology have led to an increase of cyber threats. Pegasus Spyware is surveillance cyber weapons used for the conduct of targeted attacks and state-sponsored espionage. Pegasus, a military-grade spyware developed by Israeli company NSO Group can be used to monitor remotely on smartphones. It takes advantage of Android and iOS security issues, via which, it is infecting devices. After being installed, Pegasus sideloads onto a device and can collect call records, SMS messages, emails social media activity as well as access the microphone and camera. It can monitor location, intercept encrypted messages and exfiltrate any data. Malware is tough to find due obfuscation, encryption and zero-day exploits. Pegasus used to be marketed just to law enforcement, but now it has been employed against journalists and opposition activists (as well as political dissidents), leaving questions over its morality. Pegasus spyware illustrates the advanced functionality, potential for abuse, and continuous evolution of cyber weapons, highlighting the critical need for measures to protect internet and personal data.