Cyber Security Challenges for Internet of Things and Core Networks
As the threat of data breaches increases, detecting wireless/wired network and malware attacks becomes increasingly important.
Cybersecurity is an integral element of computer communication networks. Since the explosion of Internet of Things and the adoption of contemporary computer communication protocols into emerging technological domains (e.g. autonomous vehicles, Industry 4.0), there is an expectation that built-in security is present throughout the whole system, architecture and design.
In collaboration with the King Saud University in Saudi Arabia, this project aims to:
1. develop lightweight unsupervised machine learning algorithms for network traffic anomaly detection
2. use stochastic processes to model the sequential occurrence of individual stages of complex, multi-stage attack campaigns
3. use signal processing techniques, exploiting network traffic dynamics manifested within the control and data planes, to identify network faults and anomalies
4. address the issue of imbalanced ratios between benign and malicious software by using convolutional neural network (CNN) classification
Governmental bodies, organisations and individuals are all invested in secure and resilient communication devices and networks. Therefore, efforts for a more secure cyber environment will further support and
enhance the global digital economy.
The project leverages data fusion, stochastic sequence learning, classification and clustering machine learning techniques and statistical approaches to address cyber security challenges in wireless and wired networks. To this end, specialised software is used to collect and store data in our high-performance servers, which we access locally or remotely to evaluate the performance of our developed algorithms.
We have already developed lightweight Intrusion Detection Systems that identify anomalies in the network traffic such as: Rogue Access Points, Port scanning, Man-in-the-Middle attacks in WiFi. In addition, our signal processing techniques identify Denial-of-Service attacks, which we have demonstrated using network traffic datasets from a real University campus. Finally, our work with CNN will provide better techniques on training against skewed datasets to successfully identify mutating malware software.
Our publications can be publicly accessed from Loughborough University’s repository.