Project Abstract

Our project focuses on improving intrusion detection systems (IDSs) due to the increasing security risks in computer networks and the internet. We’re using the CICIDS2017 dataset, which provides a comprehensive look at both normal and attack network traffic, filling a gap in existing datasets. Our main aim is to assess the effectiveness of various machine learning techniques in detecting anomalies in automotive cybersecurity. By doing this, we hope to enhance the capability to identify and thwart cyber threats, ultimately bolstering the security of computer networks and critical systems. Through our research, we aim to contribute valuable insights and methodologies for researchers and practitioners in the field of cybersecurity. We believe that by better understanding how to detect and respond to cyber threats, we can make significant strides towards safeguarding sensitive information and infrastructure from malicious actors. Our work seeks to address a crucial need in the field of cybersecurity and contribute to the ongoing efforts to create more resilient and secure networks and systems in today’s digital age.

Keywords: Cybersecurity, Network security, Intrusion detection systems

 Conference Details

Session: Presentation Stream 28 at Presentation Slot 7

Location: GH001 at Wednesday 8th 13:30 – 17:00

Markers: Neil Carter, Gary Tam

Course: MSc Advanced Computer Science, Masters PG

Future Plans: I’m looking for work