Back close

Lightweight, Privacy-Preserving and Usable Security Solutions for Internet of Things

Project Incharge:Dr. Nimmy K.
Co-Project Incharge:Dr. Prasad Calyam, University of Missouri, USA

The Internet of Things (IoT) is already pervasive and has completely transformed the world by giving us access to real-time information. However, IoT security concerns have attracted much interest from academia and industry. IoT devices are more vulnerable due to weak password settings, lack of encryption and incorrect access control, thus needing robust security measures. However, existing IoT devices do not sufficiently handle the increased security requirements posed by such vulnerabilities. Furthermore, many recent IoT device attacks have shown the need for robust security solutions to protect the developing IoT infrastructure. This dissertation develops lightweight, privacy-preserving, and usable security solutions to safeguard the IoT. Remote user and IoT device authentication being the primary concerns, authentication protocols are proposed and analyzed for IoT and its applications in Smart Home and industrial settings. Moreover, attack detection in IoT devices can also be an effective solution to secure IoT devices from cyberattacks. Further, we propose an Anomaly Detection System (ADS) to track abnormal activities and detect zero-day attacks. 

Outcome

Related Projects

40 Plus- A Paper Based Microfluidic Chip for the Monitoring of Women Health After 40
40 Plus- A Paper Based Microfluidic Chip for the Monitoring of Women Health After 40
Modelling the cerebellar information code in large-scale realistic circuits – Towards pharmacological predictions and robotic abstractions
Modelling the cerebellar information code in large-scale realistic circuits – Towards pharmacological predictions and robotic abstractions
Mitigation of dam induced flood disaster due to hydrological extremes (CoPI)
Mitigation of dam induced flood disaster due to hydrological extremes (CoPI)
NUF – 500 Filtration Systems
NUF – 500 Filtration Systems
Cerebellum Inspired Approach for Pattern Classification in Robots
Cerebellum Inspired Approach for Pattern Classification in Robots
Admissions Apply Now