Professor Marios Angelides
Divisional Lead / Professor - Computing
Howell Building 230
- Email: email@example.com
- Tel: +44 (0)1895 265990
The rapid growth of Internet of Things over the last few years and the working from home culture over the last 12 months as a result of Covid-19 have both contributed to the booming of the cybereconomy. People, however, have become too dependent on cyber information with often disastrous consequences as on the cyberspace neither the information is necessarily genuine nor is the source reliable. The banking sector has been leading the field with reporting the exponential growth in all forms of digital crime across the entire world that ranges from hacking, identity theft, malware, monetary extortion, spamming just to name a few. And despite the best advice and protection available, people keep falling victim to cybercriminals as their habits shift to 24/7 online banking, shopping, services and IoT apps. The proliferation of the dark web is also helping cybercrime and the underground economy flourish as a new form of industry. Targets include cars, smart phones, personal computers and any IoT device that is connected and markets range from mass markets like general shopping and banking to niche markets such as healthcare, public sector services and education. As with the software industry, the malware industry uses programs that can circumvent new detection techniques and will use social engineering through phishing, pharming, pop-ups or fake websites for financial gain In order to avoid tracking the cybercriminals use cryptocurrencies such as Bitcoin, Litecoin, Ethereum or Zcash which in turn is helping the underground economy thrive. The primary motivation behind this research is to address monetization that results from cyberattacks by developing a smart middleware app that will minimize the risks from a cyberattack such as phishing and pharming and in turn will also learn from the process through the application of machine learning. The secondary motivation is to develop a framework of best practice in minimizing the risks and impact from cyberattacks.
Wearable technology comes with the promise of improving one’s lifestyles thru data mining of their physiological condition. The potential to generate a change in daily or routine habits thru these devices leaves little doubt. Whilst the hardware capabilities of wearables have evolved rapidly, software apps that interpret and present the physiological data and make recommendations in a simple, clear and meaningful way have not followed a similar pattern of evolution. Existing fitness apps provide routinely some information to the wearer by mining personal data but the subsequent analysis is limited to supporting ad hoc personal goals. The information and recommendations presented are often either not entirely relevant or incomplete and often not easy to interpret by the wearer. The primary motivation behind this research is to address this wearable technology software challenge by developing a middleware mobile app that is supported by data analytics and machine learning to assist with interpretation of wearer data and with making of personal lifestyle improvement recommendations on the go which may then be used to feedback to the wearer’s daily goals and activities. The secondary motivation is to correlate and compare with trends in the wearer’s peer community.
If you are interested in applying for the above PhD topics to pursue under my supervision please:
- Contact me at firstname.lastname@example.org to discuss your interest. I will advise you in developing the PhD research proposal, which will form part of your application.
- Click Apply here and you will be taken to the relevant PhD course page, where you can apply using an online application.
- Complete the online application nominating me as your selected supervisor and include the PhD research proposal you have developed.
These are self-funded topics.