MALAGA · Applying Machine Learning to Cyber Risk Analysis and Mitigation
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2019-09-01 → 2021-10-06
- EU contribution
- €196,591
- Participants
- 1
- Scheme
- MSCA-IF
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Results in brief
Applying Machine Learning to Cyber Risk Analysis and Mitigation
The main problem of the research was to find out a prediction model that could predict new cyber risks in the emerging area of connected autonomous vehicles. The movement towards smart vehicles in the vehicle industry has changed the traditional transportation environment risks. Regarding the hardware and software embedded in connected and smart vehicles, they can face any cyber risks in the cybersecurity domain. For example, the Android/iOS entertainment systems in the new smart cars mean that any cyber risk predicted for these operating systems is now a cyber risk for connected and smart vehicles. In this regard, we can consider that a general cyber risk prediction system can also predict the cyber risks of smart vehicles. There are also some cyber risks that are specially for smart vehicles. Considering this, the cyber risk prediction models should be examined to see if they can be trained to gain higher performance regarding the connected and autonomous vehicles cyber risks. In our MSCA-IF research project, we addressed the problem of finding both general cyber risk classification and prediction models and also connected and autonomous vehicles cyber risk prediction models. Our presented models facilitate the entry of self-driving cars into the market by predicting their cyber risks. It is clear that no car can enter the transportation cycle without insurance. Insurers will not be able to insure new vehicles without recognizing the risks these vehicles may face. In one of the models we have presented and published in a reputable peer-reviewed journal, we identify cyber risks and quantify them. This helps the insurer to be able to insure the car and pave the way for it to enter the transportation cycle. Ultimately, self-driving cars will contribute to the sustainable development of societies by facilitating easier and cheaper transportation for all and reducing the number of accidents, as well as by helping to optimize time and fuel consumption in travels by creating intelligent public fleets. Our project is in line with the United Nations Sustainable Development Plan. The overall objective of the research done was to develop cyber risk prediction models using machine learning algorithms. In the research path, the first objective was to detect hazards, threats, top events, and consequences. Then another objective was to classify different risks and quantify them so they could be used in insurance underwriting. The second objective was to build a full data-driven machine learning prediction model. After achieving these objectives and by investigating the predicted cyber risks, we concentrated on Phishing attacks, and another objective was defined to build a machine learning based model to discover phishing attacks.
Data: CORDIS, © European Union
Project objective
Increasing connectivity and automation presents many opportunities and challenges for society. Emerging technology can benefit all citizens with better communication, increased environmental sustainability, autonomous transport, safer roads,, the list is almost inexhaustible. These emerging technologies will disrupt existing business models including underwriting and risk transfer. This disruption can stifle venture capital, innovation and risk taking in key emerging technologies and can inhibit regulatory development and societal acceptance.My research will examine Connected and Autonomous Vehicles (CAV) cybersecurity risks and mitigation using Machine Learning (ML) techniques to predict future risks, price insurance policies and and thereby foster innovation and entrepreneurial activity in Europe. My research will go beyond the SoA and implement models in ML like ensemble models and deep learning to forecast the risks of CAV technology. A network model of interactions will be trained and evaluated to study cascading of risks and threats in the CAV environment.My host team at the University of Limerick have members with machine learning skills, actuarial skills, ethical skills and underwriting experience. I will have access to staff development programmes, training courses, workshops, online courses and internal meetings. My host team are directly connected to a large variety of colleagues in other EU locations in both academic and industry positions. I will work with my host and partners to develop my research and increase my skillsets.My research directly contributes to several UN sustainable development goals. On a personal level, the impact of my fellowship and collaborations will expand my set of skills, both research-related and transferable ones, leading to greatly improved career prospects both in and outside academia. My new abilities will include enhanced machine learning capabilities, cyber risk expertise and risk engineering skills.
Original text from CORDIS.
Participants
- UNIVERSITY OF LIMERICK · LimerickCoordinatorIreland
Links
Data: CORDIS, © European Union