This paper presents the results of a research on using voice assistants to enable financial and commercial operations which require authentication with increased level of security. Several prototypes of authentication service with two-factor authentication for the purpose of voice-controlled digital banking and online payments have been developed at the Cyber Security Research Centre of London Metropolitan University. They utilize the power of the cloud technology and the attractiveness of the devices equipped with voice assistant software to provide secure method for two-factor authentication, which meets the recent standards for security of the operations in digital banking.

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats.

Although authentication of users of digital voice-based systems has been addressed by much research and many commercially available products, there are very few that perform well in terms of both usability and security in the audio domain. In addition, the use of voice biometrics has been shown to have limitations and relatively poor performance when compared to other authentication methods. We propose using audio steganography as a method of placing authentication key material into sound, such that an authentication factor can be achieved within an audio channel to supplement other methods, thus providing a multi factor authentication opportunity that retains the usability associated with voice channels. In this research we outline the challenges and threats to audio and voice-based systems in the form of an original threat model focusing on audio and voice-based systems, we outline a novel architectural model that utilises audio steganography to mitigate the threats in various authentication scenarios and finally, we conduct experimentation into hiding authentication materials into an audible sound. The experimentation focused on creating and testing a new steganographic technique which is robust to noise, resilient to steganalysis and has sufficient capacity to hold cryptographic material such as a 2048 bit RSA key in a short audio music clip of just a few seconds achieving a signal to noise ratio of over 70 dB in some scenarios. The method developed was seen to be very robust using digital transmission which has applications beyond this research. With acoustic transmission, despite the progress demonstrated in this research some challenges remain to ensure the approach achieves its full potential in noisy real-world applications and therefore the future research direction required is outlined and discussed.