Research projects in Information Technology

This project will explore the security and privacy challenges inherent in the tokenization of real-world assets (RWAs) using the blockchain technology. As industries increasingly adopt tokenization to digitize and trade assets like real estate, commodities, and fine art, ensuring the security and privacy of these transactions becomes critical. The research will focus on identifying vulnerabilities in existing tokenization frameworks, analyzing potential risks, and developing novel security protocols to protect sensitive data and ensure the integrity of tokenized assets.

This project focuses on developing algorithms capable of automatically identifying and categorizing mobile ringtones. This involves leveraging machine learning techniques to analyze audio signals from mobile devices and classify them into different categories or types of ringtones. The activities of the project include gathering a diverse dataset of audio samples representing various types of mobile ringtones.

In recent years, the rise in cybercrimes has significantly increased the vulnerability of the open internet to various threats and cyber-attacks. Among these, phishing stands out as one of the most perilous crimes worldwide. In a phishing attack, perpetrators create fraudulent websites that mimic legitimate ones (such as fake bank websites). These deceptive sites lure users into disclosing sensitive financial, personal, and confidential information.

The detection of human activities is crucial for effective monitoring purposes. The challenge lies in accurately and promptly identifying various types of activities from videos and images captured in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task.

Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where the target person appears to be performing actions or speaking as the source person. In a broader context, deepfakes encompass other categories such as lip-sync and puppet-master. Lip-sync deepfakes alter videos to synchronize mouth movements with a provided audio track.

This project aims to develop robust algorithms capable of identifying and analyzing fingerprints extracted from both static images and video footage. Machine learning techniques, particularly computer vision and pattern recognition methods, will be utilized to automate the process of fingerprint detection. These methods will be trained to learn patterns from fingerprint features and detect them using object detection approaches. A dataset of fingerprint images and videos, annotated with ground truth information will be collected.

Mis/disinformation (also known as fake news), in the era of digital communication, poses a significant challenge to society, affecting public opinion, decision-making processes, and even democratic systems. We still know little about the features of this communication, the manipulation techniques employed, and the types of people who are more susceptible to believing this information.

This project extends upon Prof Whitty's work in this field to address one of the issues above.

Online fraud, also referred to as cyberscams, is increasingly becoming a cybersecurity problem that technical cybersecurity specialists are unable to effectively detect. Given the difficulty in the automatic detection of scams, the onus is often pushed back to humans to detect. Gamification and awareness campaigns are regularly researched and implemented in workplaces to prevent people from being tricked by scams, which may lead to identity theft or conning individuals out of money.

The energy transition to net zero is in full swing! We at Monash University's Faculty of Information Technology (FIT) are in the unique position that we support the transition across an immensely broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data crunching to make decisions automatically and to let humans make informed decisions, too. 

Tired of time series anomalies slipping through the cracks due to scarce labeled data? Look no further! Our groundbreaking research project [1,2], is set to transform the landscape of anomaly detection.

[1] https://arxiv.org/pdf/2308.09296

[2] https://arxiv.org/pdf/2211.05244