Our research group works in 3 domains within the field of security and privacy: (1) security of cyber-physical systems, (2) security and privacy problems in machine learning-based systems, and (3) economics of security and privacy. In domain (1), we work on the security of industrial automation and control systems, security of modern vehicles and intelligent transport systems, and security of IoT systems and applications. The common in these topics is that attacks originating from cyberspace may have physical consequences, resulting in equipment or environmental damage, or potentially even loss of human life, and therefore, security is an important requirement. In domain (2), we study how machine learning can be used to solve security and privacy problems, and also how machine learning–based systems can be exploited maliciously. More specifically, we focus on the security of federated learning algorithms and the problem of adversarial examples (eg, in machine learning-based malware detection). In domain (3), we apply game theoretic models to study the incentive structures in different systems, and the cause of security and privacy problems. Besides the domains mentioned above, we have strong competence in applied cryptography, privacy enhancing technologies, malware analysis, reverse engineering, and secure operation of networks and network-based systems, including IT infrastructure automation.
-In the SETIT project, we developed an efficient anti-virus solution for resource-constrained IoT devices, which detects malware with more than 90% accuracy. We developed many other security enhancing solutions for IoT devices. We constructed new attacks on the vehicle CAN bus, and proposed multiple attack detection mechanisms. We analyzed what information can be extracted from recorded CAN traffic, and showed that it is possible to identify the driver solely from the raw CAN data. We designed and implemented honeypots for ICS/SCADA systems. We achieved pioneering results in the field of interdependent privacy, which gave the theoretical explanations for the Cambridge Analytica scandal. We proposed new privacy-preserving federated machine learning algorithms
IEEE Transactions you Dependable and Secure Systems,
IEEE Transactions on Mobile Computing,
IEEE Transactions you Knowledge and Data Engineering,
IEEE Communications Magazine,
ACM Computing Surveys,
Elsevier Computer Networks,
Elsevier Computer Communications,
Elsevier Computers & Security
IEEE Security and Privacy Workshops (SPW),
Information Security and Cryptology (ICISC),
Security and Safety Interplay of Intelligent Software Systems (CSITS),
Emerging Technologies for Authorization and Authentication,
International Conference on Software, Telecommunications and Computer Networks (SoftCOM),
IEEE / IFIP Network Operations and Management Symposium,
IEEE / IFIP Workshop on Security for Emerging Distributed Network Technologies (DISSECT),
International Conference on Availability, Reliability and Security (ARES),
IEEE Intelligent Transportation Systems Conference (ITSC),
International Conference on Data Mining ( ICDM),
IEEE Conference on Network Softwareization (NetSoft),
International Conference on Distributed Computing in Sensor Systems (DCOSS),
ACM SIGSAC Conference on Computer and Communications Security,
Privacy Enhancing Technologies Symposium (PETS),
International Workshop on Privacy Engineering (IWPE),
ACM International Workshop is Security in the Cloud Computing,
The workshop is Cyber Security for Intelligent Transportation Systems,
European Symposium on Research in Computer Security (ESORICS),
ACM Conference on Computer and Communications Security (CCS),
Virus Bulletin,
IEEE International Conference you Communications (ICC),
IEEE Global Communications Conference (GLOBECOM),
International Conference on Decision and Game Theory for Security (GameSec),
International Conference on Game Theory for Networks (GameNets),
Financial Cryptography & Data Security,
IEEE International Conference on Big Data,
International Conference on Internet of Things, Big Data, and Security (IoTBDS),