Research at CSIT targets the development of secure solutions to a number of particularly modern problems, including the protection of mobile phone networks, guaranteeing privacy over unsecure networks for connected healthcare and the creation of secure "corridors" for the seamless and rapid transit of people. CSIT has evolved from a number of well established, technology driven research clusters in signal, video, data, network and security processing systems at Queen’s University Belfast.
Contact information
Address
Centre for Secure Information Technologies (CSIT)
ECIT Institute
Queens Road
Belfast
BT3 9DT
United Kingdom
Telephone: 028 9097 1794
Email: p.mills@ecit.qub.ac.uk
Website: www.csit.qub.ac.uk
Press Releases
CSIT joins Network Intelligence Alliance
Newly published cyber security report identifies key research priorities for safeguarding the Internet of tomorrow
WCSTRS Report.pdf 1.86 MB
Products
Autonomous Surveillance Platform
Traditional surveillance methods rely on security personnel to observe and interpret surveillance data in real-time. Inevitably such methods do not scale well for busy environments, cannot merge data from disparate sources, introduce opportunity for human error and ultimately fail to provide adequate protection for modern transport corridors.
CSIT has developed a suite of technologies to deliver real-time situational awareness. Real-time analytic techniques are applied to video and RF data to detect and track human subjects through secure zones. Detections are combined with data acquired from traditional physical security infrastructure such as ticket verification and access control to detect and record events of significance and identify abnormal or inconsistent behaviours.
CB2-ASP - Autonomous Surveillance Platform.pdf 1.37 MB
Evidential Reasoning Framework
Many applications rely on accurate and frequent readings acquired from physical and logical sensors. However for many deployments the sensor data cannot be deemed accurate and it can be characterised by uncertainty or incompleteness. This ‘noise’ is often introduced into analytic sensing where imperfect analytic algorithms cannot deliver dependable accuracy in all cases (consider for example video analytics). Nevertheless when the level of uncertainty can be quantified, significant benefits result from reasoning with this uncertainty over extended time periods.
Researchers at CSIT have extended traditional Artificial Intelligence reasoning techniques and incorporated state-of-the-art uncertainty and conflict handling to create a powerful Evidential Reasoning Framework (ERF) for use across a broad range of applications. Application opportunities are numerous but within CSIT the framework has been applied to traditionally difficult problems such as autonomous surveillance.
CB3-ERF - Evidential Reasoning Framework.pdf 1.26 MB
Physical Unclonable Function
Counterfeit products are estimated to cost the global economy many billions of pounds each year. Physical Unclonable Functions (PUFs) are the next generation of anti-counterfeiting technology. A PUF is a physical object that can take inputs and generate unpredictable outputs; it is unclonable in that the input/output behaviour of a physical copy of one PUF will differ from that of the original one due to some uncontrollable randomness in the copying process.
This concept can be utilised in conjunction with RFID technology to provide an RFID tag with a unique identifier. Lightweight PUF designs have been developed at CSIT that can distinguish between inconsistencies in tags - ‘digital fingerprints’ - that occur during fabrication. These ‘digital fingerprints’ can be used to compute a unique tag response upon receiving a challenge from an RFID reader.
A PUF cannot be copied or simulated ensuring that each challenge/response pair is unique to a tag. A backend database stores the challenge/response pairs for each tag and a reader verifies the authenticity of a tag by checking its responses to given challenges.
CB4-PUF - Physical Unclonable Function.pdf 1.31 MB
Side Channel Attacks & Countermeasures
Side-channel attacks (SCA) can be used to reveal the security key stored in electronic cryptographic devices by monitoring physical characteristics such as power consumption & electromagnetic (EM) emanations. For example, the attack complexity
of the Advanced Encryption Standard (AES) algorithm can be reduced from 2128 (bruteforce attack) to 28 using Differential
Power Analysis (DPA), which is the most effective SCA technique. Countermeasures aim to eliminate the dependencies between
the cryptographic key-dependent operations and their associated power consumption and EM emissions.
CSIT researchers have developed an SCA measurement platform for EM & Power analysis. CSIT were the first to show successful
power attacks of SHACAL-2, Camellia & CAST-128. CSIT have also developed new attack techniques and lightweight DPA countermeasures. A novel solution designed for AES hardware implementations offers significant performance improvements
over previous work by combining two lightweight countermeasures, random inversion masking and improved random register renaming.
CB5-SCA - Side Channel Attacks & Countermeasures.pdf 1.26 MB
