Project Abstract

In the digital age, computer systems are evolving and integrating into all aspects of our society. However, with that, cyber-criminals are evolving too, and are seeking new ways to attack our critical systems to steal our valuable information to earn ill-gotten gains. To address this, the project aims to explore different methods of protecting our data from the notorious keylogger and to create an application integrated with an immune system-inspired detection algorithm. The algorithm will provide a reliable method to detect keyloggers before they can steal more valuable information from a system. Taking inspiration from the dendritic cells in the immune system used to detect the presence of antigens, this project will mimic these cells and their method of antigen thresholding for detection. An algorithm was iteratively developed, taking inspiration from evolution, and integrated into an interactive application that can be used to save computer systems from financial loss. Keyloggers exhibit similar behaviours, so scanning for these can lead to correlations of suspicious activities to be identified. With these correlations, data can be normalised and placed into an antigen threshold that can be used to identify and locate a keylogger. This project?��s thresholding saw an impressive success rate of 67% for the detection of keyloggers. The findings from this project provide a dynamic way to detect keyloggers and highlight how the lines between immunology and cybersecurity are becoming blurred. Giving way to a new method of combating cybercriminals.

Keywords: Cyber Security, Algorithms, Data Science

 Conference Details

Session: Poster Session B at Poster Stand 67

Location: Sir Stanley Clarke Auditorium at Wednesday 8th 09:00 – 12:30

Markers: Solmaz Safari, Muneeb Ahmad

Course: BSc Computer Science, 3rd Year

Future Plans: I’m continuing studies