The Internet of Medical Things (IoMT) is getting extreme attraction as it motivates unprecedented growth in the healthcare industry. Security breaches in IoMT can lead to threatening patients’ lives. For IoMT, existing medical remote attestation techniques (EMRATs) have limitations such as neglecting operational symptoms of compromised systems, like inconsistent medical sensor readings. Moreover, EMRATs do not enable medical-forensic-based attestation history and are inefficient for mutual attestation between a doctor network and a sensor network monitoring a patient. This mutual attestation guarantees safe remote surgeries. In this paper for IoMT, we present a novel remote attestation protocol, BDMFA (Blockchain-supported and Deep learning Medical Forensic-enabling Attestation), to overcome the limitations of EMRATs. BDMFA utilizes deep learning and Blockchain to learn from sensor readings and store attestation history. We prove that BDMFA is resilient to a higher number of attacks than that resisted by EMRATs. Moreover, we present a proof-of-concept implementation for BDMFA using SMART (Secure and Minimal Architecture for Root of Trust). We proved the practical feasibility of BDMFA by implementing it using Omnetpp equipped with Castalia. For a system with 50 patient-sensors and 25 doctor-terminals, BDMFA needed only 2.6 s to complete attestation and less communication cost than that needed for related state-of-the-art protocols by 28.4%. For larger systems, we carried comparative analysis confirming that our proposed protocol BDMFA requires less cost and is more scalable and efficient than related protocols.

With the rapid technological advancement in the Internet of Things (IoT) and Internet of Vehicles (IoV), we witness exponential growth of Connected and Autonomous Vehicles (CAVs). However, these integrations of IoV with other technologies make the IoV network and its interaction between different network components highly complex. Therefore, ensuring the correct functioning of the firmware and software running on these next-generation vehicles becomes an essential requirement. A feasible method to address the aforementioned security issues is Remote Attestation (RA). However, the advancement in the attackers' approaches and the increased complexity, large network size, and vehicle mobility allow the attacks to bypass these security solutions, making RA less effective. In this paper, we propose LHASIoV, an attestation and healing protocol for IoV. LHASIoV has many features such as competent-wise (treats different entities of the system differently), geographical location-aware (traces forensics of security breaches and eases healing compromised vehicles), gradual healing (via slicing the healing software) of compromised vehicles, and resistance to single-point-of-failure. We provide proof-of-concept implementation and formal operational and security analysis for LHASIoV. To show its practical feasibility and effectiveness, we provide performance analysis by implementing it on the Omnetpp simulator. The simulation results show that for an IoV system that has 100 vehicles moving with a speed range of 15-25 mph, LHASIoV needed only 5.27 seconds to complete the vehicle's attestation. For this number of vehicles and compared to the existing protocols, LHASIoV reduced the communication and storage costs on average by 54.46% and 43.92%, respectively.

Internet of Drones (IoD) is a framework to set up drones networks that may serve multiple purposes, e.g., data collection. New IoD applications (such as drone assisted internet of vehicles) envision the simultaneous collection of multiple data types. Although authentication may prevent unauthorized users to access the collected data, existing authentication solutions do not distinguish between the different types of data collected by drones. Therefore, authenticated users may receive sensitive data regarding another user incurring hence in a privacy leakage. In this paper, we propose SETCAP, a novel Service-Based Energy-Efficient Temporal Credential Authentication Protocol for IoD. SETCAP exploits the distinction between data types to prevent information leakage. We formally test SETCAP against the Real-Or-Random (ROR) model and implemented SETCAP in Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. Moreover, we validated SETCAP via non-mathematical security analysis to show its security against many attacks. We assessed the superiority of SETCAP in terms of functionality and security characteristics as well as computation, communication, and energy costs. The communication cost of creating a session in SETCAP is approximately 20% smaller than that of creating a session in the closest state-of-the-art protocol. Furthermore, the framework that we propose requires the creation of a number of sessions that are additive in terms of the number of drones and users, whereas the existing solutions are multiplicative. SETCAP is therefore a secure and scalable solution for resource-constrained devices such as drones.