Research to help software communicate and operate seamlessly
The Cyber Security and Network Security research cluster is focussed on various aspects of network and computer security, spanning from theory to practice. In an increasingly interconnected world, where cyber attacks are increasing in frequency, we provide vital research with practical applications to help protect individuals and organisations from cyber threats.
The Network Security Research undertakes research primarily in the following areas:
- Applied Cryptography
- Cyber and Data Security
- Multimedia Security
- Sensor Networks Security
- Coding Techniques for Network Communications
- Network Security, Intrusion Detection and Wireless Security
- Information Assurance in e-Government Models
Body sensor networks
The availability of small, low-cost networked sensors combined with advanced signal processing and information extraction is driving a revolution in physiological monitoring and intervention. Body Sensor Networks (BSN) are enabling technologies for precision healthcare, enhanced sports and fitness training, novel life-style monitoring, and individualized security. Expected growth of elderly populations and the corresponding increase in healthcare costs mandate systems for automated monitoring of physiological conditions, triage, and remote diagnosis.
Semantics-Based Document Classification for Data Leakage Detection
The protection of confidential data from being leaked to the public is conventionally done through firewalls, virtual private networks and intrusion detection/prevention systems. However, these systems lack dedicated and proactive protection for confidential data when it travels through legitimate channels. An emerging technology in the field of information security called Data Leakage Prevention has been developed to overcome these problems. It deals with tools working under a central policy, which analyse networked environments to detect sensitive data, prevent unauthorized access to it and block channels associated with potential leak. This requires special data classification capabilities to distinguish between sensitive and normal data. Not only this task needs prior knowledge of the sensitive data, but also requires knowledge of potentially evolved and unknown data. Content statistical analysis deals with nebulous forms of data, while preserving exact data detection capabilities. It focuses on analysing frequencies and relationships of words “terms” within a large corpus, to construct a semantic weight for data. It also facilitates the use of machine learning algorithms and Bayesian analysis, in order to classify data. We evaluate the effectiveness of using content statistical analysis in constructing semantics of confidential data. Moreover, we propose a semantic-aware document classification model for data leakage detection.