Australian Institute for Machine Learning (AIML), University of Adelaide My research is motivated from a passion for discovering the “why?” behind “how?” with respect to core problems in Artificial Intelligence, Computer Vision and Machine Learning. I am currently leading the CI2CV laboratory, newly re-located to the Robotics Institute, Carnegie Mellon University in Pittsburgh, PA, USA where we […]
The rise of deepfakes could enhance the effectiveness of disinformation efforts by states, political parties and adversarial actors. How rapidly is this technology advancing, and who in reality might adopt it for malicious ends? This report offers a comprehensive deepfake threat assessment grounded in the latest machine learning research on generative models. This paper examines
Deepfakes: A Grounded Threat Assessment Read More »
How Automation, Analytics, and Machine Learning Improve and Accelerate Threat Analysis The cybersecurity industry is increasingly producing enormous amounts of raw threat data. The sheer volume of information threat researchers must sift through makes it difficult to collect, analyze, and research that data in a timely manner. This in turn limits their ability to understand
Data Analytics and Cyber Threat Intelligence Read More »
ISACA’s The Future of IT Audit: Research Brief reveals the opportunities and challenges that
await the IT audit professional and sheds light on the IT audit profession in general. In this paper,
ISACA presents survey findings related to the involvement of auditors in technology projects
within their enterprise, the technology challenges faced by auditors, the perceived impact of
automation and artificial intelligence (AI), and the IT audit staffing implications and workforce
development issues caused by new technologies.
The Future of IT Audit Read More »
The Report shows how cyberattackers are raising the stakes in ransomware, increasing stealth in malicious Android apps, exploiting misconfiguration in the cloud, and abusing machine learning.
SophosLabs 2020 Threat Report Read More »
This short paper introduces an approach to producing explanations or justifications of decisions made in some artificial intelligence and machine learning (AI/ML) systems, using methods derived from those for fault location in combinatorial testing.
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