Our Research

Our Talk: Long-term Cloud Service Selection using Short-term Trials. By Dr. Fattah

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Our Research

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Tools and Techniques for Secure-by-Design DLT System

This area of research concerns with techniques and tools for facilitating secure-by-design of DLT. We focus on a holistic perspective that considers not just the ledger networks but the interfaces between the ledger and other system components.

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Socio-technical factors in secure software engineering methodologies and practices

Software development is a highly cognitive process, requiring the intellectual work of individuals and groups. And the development of secure software systems is highly dependent on the decisions of the software developers and other relevant stakeholders. Due to this reason, it is a field that is affected by errors in human judgment and a range of other socio-technical factors. Therefore, how do we account for this “human factor”, which plays a major role in this process?

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Security of Machine Learning Models

Machine learning approaches are widely adopted in software engineering and cybersecurity application to readily deal with volumes of data proactively.

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Secure Mobile Health Application Engineering

Smartphone ownership has surpassed three billion, and it is expected to increase by several hundred million in the next few years. Healthcare becomes one of the primary domains that strive to utilise mobile applications (apps) to empower and innovate health services.

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Security, Architecture and Platforms for Distributed Autonomous Systems

TBA.

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Security Orchestration and Automation

At CREST researchers are leveraging existing software engineering, analytical reasoning, natural language processing and machine learning tools and techniques to develop a secure and integrated platform. Our aim is to help build a secure and integrated platform that is easy to use and evolve with the changing threat landscape and increase the operation efficiency of the cybersecurity team.

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Mining Open-source Repositories for Predictive Security Analytics

The focus of CREST researchers is to utilize data available in open-source repositories to develop high-performing and robust AI-enabled prediction models to automate the detection, characterization, and fixing prioritization of vulnerabilities in time.

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Real-time Big Data Analytics

CREST researchers leverage state-of-the-art techniques (e.g., AI and search-based optimization) to design, implement, deploy, and evaluate big data systems for optimally collecting, storing, analyzing, and visualizing a large volume of data in real-time. CREST research particularly focuses on the evaluation of big data storage solutions (e.g., Cassandra and MongDB) and big data analytical solutions (e.g., Spark and Flink) as deployed on private, public, and hybrid clouds.

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Cyber Common Operating Pictures (CCOP)

The goal of this research is to build and rigorously evaluate novel approaches, metrics, and technological infrastructure for providing a highly configurable Platform for CCOP that will enhance Cyber Situational Awareness in an organisation and allow them to redefine how they manage cyber security.

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Automated Container Security

Researchers at CREST leverage various artificial intelligence approaches to develop tools and techniques to better understand the developers’ viewpoint on security while adopting container technologies in industry. We study a large volume of container-related data in open source software repositories (GitHub), Question/Answering Platforms (Stack Overflow, Docker Forum) as well as container image repositories (Docker Hub) to find and analyse the security issues.

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Techniques and Frameworks for DLT System Engineering

This area of research concerns with the development, cataloguing, and evaluating techniques and frameworks for architecting and engineering DLT systems

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