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Security has become one of the cornerstones in the design of computer systems and networks. My long-term research goal is to support the security of emerging computing environments against sophisticated threats, which has proven to require a high level of innovation in both theoretical and applied cyber defense.
Secure Systems and Networks for the Internet of Things
The Internet of Things (IoT) has brought new service opportunities across various sectors such as digitized health, autonomous transportation, and intelligent manufacturing, and has played a crucial role in building smart cities. IoT involves smart devices that can upload data to the Internet and control the decisions of cyber-physical processes. Also, the Industrial IoT (IIoT) paradigm combines automated machines and advanced data analytics techniques to improve productivity and efficiency. However, IoT has shown to raise many security vulnerabilities that can lead to cyber and cyber-physical attacks with potentially catastrophic impact on public safety as well as the economy. Hence, with more critical infrastructures becoming intrinsically reliant on IoT technologies, securing IoT systems and networks becomes of utmost importance.
My ongoing research focuses on the design, analysis, and deployment of secure and resilient large-scale IoT and cyber-physical systems, with particular emphasis on critical infrastructures. It explores intelligence-driven offensive and defensive paradigms, rethinking real-time interactions among heterogeneous system modules—ranging from networked cyber components to constrained physical nodes—to enable systems that can strategically anticipate, withstand, and recover from sophisticated cyber compromises. This research adopts innovative security engineering concepts, including the intrinsic design of resilience against zero-day threats, adaptive hardening mechanisms, and the integration of autonomous cyber defense agents operating under uncertainty and adversarial dynamics. By leveraging recent advances in deep learning, federated learning, and trustworthy AI, my work also seeks to develop robust, privacy-preserving intrusion detection and prevention frameworks suitable for decentralized IoT computing infrastructures. A significant focus is placed on combating botnet-enabled cyber attacks, enhancing situational awareness across distributed environments, and ensuring that IoT ecosystems evolve toward self-healing, self-learning, and verifiably dependable operational states. Together, these contributions aim to bridge fundamental research and practical deployment, shaping resilient-by-design IoT architectures capable of supporting secure digital transformation at scale.
"An ounce of prevention is worth a pound of cure." - Benjamin Franklin
The research also focuses on investigating novel security and resilience solutions against sophisticated cyber attacks on sensors and actuators in industrial Cyber-Physical Systems (CPS). These are particularly vulnerable to Advanced Persistent Threats (APTs) that stealthily undermine system and network operations to cause a long-term impact on reliability and safety.
One of the key challenges is that security in IoT must be optimized due to the many functional and architectural requirements and constraints dictated by the IoT environment such as large scale, resource limitation, and real-time communications. Hence, the research objectives also include the design of cooperative attack prevention and mitigation schemes in decentralized IoT and edge computing systems while emphasizing strategic and resource-aware security deployment. More specifically, my team and I focus on leveraging the power of game theory to devise optimal and adaptive defense strategies under incomplete threat information in adversarial settings. Game theory has been extensively used to optimize the design and deployment of cyber systems, and has become one of the fundamental tools for security risk assessment and management in vulnerable systems and networks.
"The Best for the Group comes when everyone in the group does what's best for himself AND the group." - John Nash
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