Általános szabályok és tanácsok az önálló laborhoz
Előadások beosztása
A laborban több aktív kutatási területeken lehet önálló labor, szakdolgozat, és diplomaterv témát választani. Ezeknek a területeknek a leírása található alább. Ha valamelyik tématerület érdekel, keresd meg a tématerületért felelős kollégánkat, és beszéljetek lehetséges konkrét feladatokról a területen belül. Ne feledjétek, hogy az önálló labor keretében egy-egy feladaton kisebb csoportban (team-ben) is lehet dolgozni. Az témáink a következő területekhez kapcsolódnak:
All,
Embedded-Systems, Internet-of-Things, Malware, Machine-Learning, Software-Security, Security-Analysis, ICS/SCADA, Attack generation, Privacy, Security, Federated-Learning, Game-Theory, Economics
Kategória: Privacy, Machine-Learning
The word privacy is derived from the Latin word "privatus" which means set apart from what is public, personal and belonging to oneself, and not to the state. There are multiple angles of privacy and multiple techniques to improve them to varying extent. Students can work on the following topics:
Required skills: none
Preferred skills: basic programming skills (e.g., python)
Létszám: 6 hallgató
Kapcsolat: Gergely Ács (CrySyS Lab), Balázs Pejó (CrySyS Lab)
Kategória: Privacy, Security, Machine-Learning
Machine Learning (Artificial Intelligence) has become undisputedly popular in recent years. The number of security critical applications of machine learning has been steadily increasing over the years (self-driving cars, user authentication, decision support, profiling, risk assessment, etc.). However, there are still many open security problems of machine learning. Students can work on the following topics:
Required skills: none
Preferred skills: basic programming skills (e.g., python), machine learning (not required)
Létszám: 6 hallgató
Kapcsolat: Gergely Ács (CrySyS Lab), Balázs Pejó (CrySyS Lab)
Kategória: Privacy, Security, Federated-Learning, Game-Theory
Federated learning enables multiple actors to build a common, robust machine learning model without sharing data, thus allowing to address critical issues such as data privacy, data security, data access rights and access to heterogeneous data. Its applications are spread over a number of industries including defense, telecommunications, IoT, and pharmaceutics. Students can work on the following topics:
Required skills: none
Preferred skills: basic programming skills (e.g., python), machine learning (not required)
Létszám: 6 hallgató
Kapcsolat: Gergely Ács (CrySyS Lab), Balázs Pejó (CrySyS Lab), Gergely Biczók (CrySyS Lab)
Kategória: Economics, Privacy, Security, Game-Theory, Machine-Learning
As evidenced in the last 10-15 years, cybersecurity is not a purely technical discipline. Decision-makers, whether sitting at security providers (IT companies), security demanders (everyone using IT) or the security industry, are mostly driven by economic incentives. Understanding these incentives are vital for designing systems that are secure in real-life scenarios. Parallel to this, data privacy has also shown the same characteristics: proper economic incentives and controls are needed to design systems where sharing data is beneficial to both data subject and data controller. An extreme example to a flawed attempt at such a design is the Cambridge Analytica case.
The prospective student will identify a cybersecurity or data privacy economics problem, and use elements of game theory and other domain-specific techniques and software tools to transform the problem into a model and propose a solution. Potential topics include:
Required skills: model thinking, good command of English
Preferred skills: basic knowledge of game theory, basic programming skills (e.g., python, matlab, NetLogo)
Létszám: 6 hallgató
Kapcsolat: Gergely Biczók (CrySyS Lab), Balázs Pejó (CrySyS Lab)