[RU2/22] Hassan CHAITOU : Optimization of security risk for learning on heterogeneous data
On Oct 17, 2022, our PhD student Hassan CHAITOU presented her latest results regarding “Optimization of security risk for learning on heterogeneous data“. This presentation took place during the research update fall 2022 event of the chair Cybersecurity for Critical Networked Infrastructures (cyberCNI.fr) at the SNCF Head Office. You find more infos on our website https://cyberCNI.fr/
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To see the video, click on this link : https://youtu.be/BI4r3xikugI
Abstract
A classifier is a component used in the automation of “decision-making” or complex data abstraction: intruder detection, speed limitation extraction. For an efficient classifier, the training must be on a large volume of data and be renewed over time by integrating or revoking certain learning data. From a security point of view, this process represents a risk since it offers the attacker various ways of degrading classifier performance (either by forcing classifications mischievous, either by randomly degrading its performance). These two types of attacks require more or less effort from the attacker. This risk is exacerbated when data comes from sources (network equipment, organizations) corresponding to heterogeneous trust levels. This thesis aims at controlling the risk associated with this update via game theory in the case where the confidence in the learning data is not homogeneous.
About Hassan CHAITOU
I have been working in programming, data science, and IT technologies since 2010. My experience in programming, software analysis, and IT technical management helped me to master several essential technologies such as developing information systems, software analysis, and design, virtualization, and storage techniques in addition to Microsoft server technologies. I have obtained a B.Sc.degree in applied mathematics from the Lebanese university. The first M.Sc.degree in computer science from the American University of culture and education and a second M.Sc. degree in Cybersecurity from Lebanese university. Moreover, I enrolled in a research internship program entitled “Predictive analysis of political discourses, a machine learning approach” at University de Versailles Saint- Quentin-en-Yvelines, which is in the field of data science and cybersecurity. Currently, I am enrolling in the Ph.D. program, entitled “Optimization of security risk for learning on heterogeneous data” at Télécom Paris.
About Research Update
The cyberCNI.fr Research Updates happen once per semester. They are our big status event where our PhD students, PostDocs, and Engineers present their progress, current works, and next challenges. The research update is the perfect opportunity for getting an overview on and discussing what is going on at the chair.
About the chair Cybersecurity of Critical Networked Infrastructures (cyberCNI.fr)
The Cyber CNI Chair at IMT Atlantique does research, innovation, and teaching in the field of cybersecurity for critical networked infrastructures. Such infrastructures include industrial processes, financial systems, building automation, energy networks, water treatment plants, or transportation.
The chair covers the full stack from sensors and actuators and their signals over industrial control systems, distributed services at the edge or cloud, to user interfaces with collaborative Mixed Reality, and security policies. The chair currently hosts 9 PhD students, 4 PostDocs, 11 Professors, 1 engineer, and 1 internship student.
The chair runs a large testbed that enables applied research together with the industry partners. The industry partners of the current third funding round are Airbus, BNP Paribas, EDF, and SNCF. The chaire is located in Brittany, France. Brittany is the cybersecurity region number 1 in France. The chair Cyber CNI is strongly embedded in the cybersecurity ecosystem through its partnerships with the Pôle d’Excellence Cyber (PEC) and the Brittany Region.
The chair provides a unique environment for cybersecurity research with lots of development possibilities.
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