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1. Authorization Framework for Secure Cloud Assisted Connected Cars and Vehicular Internet of Things

   https://doi.org/10.1145/3205977.3205994  

   Gupta, Maanak; Sandhu, Ravi (June 2018, Proceedings of 23rd ACM Symposium on Access Control Models and Technologies (SACMAT’18))

   Internet of Things has become a predominant phenomenon in every sphere of smart life. Connected Cars and Vehicular Internet of Things, which involves communication and data exchange between vehicles, traffic infrastructure or other entities are pivotal to realize the vision of smart city and intelligent transportation. Vehicular Cloud offers a promising architecture wherein storage and processing capabilities of smart objects are utilized to provide on-the-fly fog platform. Researchers have demonstrated vulnerabilities in this emerging vehicular IoT ecosystem, where data has been stolen from critical sensors and smart vehicles controlled remotely. Security and privacy is important in Internet of Vehicles (IoV) where access to electronic control units, applications and data in connected cars should only be authorized to legitimate users, sensors or vehicles. In this paper, we propose an authorization framework to secure this dynamic system where interactions among entities is not pre-defined. We provide an extended access control oriented (E-ACO) architecture relevant to IoV and discuss the need of vehicular clouds in this time and location sensitive environment. We outline approaches to different access control models which can be enforced at various layers of E-ACO architecture and in the authorization framework. Finally, we discuss use cases to illustrate access control requirements in our vision of cloud assisted connected cars and vehicular IoT, and discuss possible research directions. 

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2. Attributes Aware Relationship-based Access Control for Smart IoT Systems

   Lopamudra Praharaj; Safwa Ameer; Maanak Gupta; Ravi Sandhu (December 2022, 2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC))

   The pervasive nature of smart connected devices has intruded on our daily lives and has become an intrinsic part of our world. However, the wide use of the Internet of Things (IoT) in critical application domains has raised concerns for user privacy and security against growing cyber threats. In particular, the implications of cyber exploitation for IoT devices are beyond financial losses and could constitute risks to human life. Most deployed access control solutions for smart IoT systems do not offer policy individualization, the ability to specify or change the policy according to the individual user’s preference. As a result, currently deployed systems are not well suited to specify access control policies in a multi-user environment, where users access the same devices to perform different operations. The system’s security gets tricky when the smart ecosystem involves complicated social relationships, much like in a smart home. Relationship-based access control (ReBAC), widely used in online social networks, offers the ability to consider user relationships in defining access control decisions and supports policy individualization. However, to the best of our knowledge, no such attempt has been made to develop a formal ReBAC model for smart IoT systems. This paper proposes a ReBAC IoT dynamic and fine-grained access control model which considers the social relationships among users along with the attributes to support an attributes-aware relationship-based access control model for smart IoT systems. ReBAC IoT is formally defined, illustrated through different use cases, implemented, and tested. 

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3. Attributes Aware Relationship-based Access Control for Smart IoT Systems

   Lopamudra Praharaj; Safwa Ameer; Maanak Gupta; Ravi Sandhu (December 2022, 2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC))

   The pervasive nature of smart connected devices has intruded on our daily lives and has become an intrinsic part of our world. However, the wide use of the Internet of Things (IoT) in critical application domains has raised concerns for user privacy and security against growing cyber threats. In particular, the implications of cyber exploitation for IoT devices are beyond financial losses and could constitute risks to human life. Most deployed access control solutions for smart IoT systems do not offer policy individualization, the ability to specify or change the policy according to the individual user’s preference. As a result, currently deployed systems are not well suited to specify access control policies in a multi-user environment, where users access the same devices to perform different operations. The system’s security gets tricky when the smart ecosystem involves complicated social relationships, much like in a smart home. Relationship-based access control (ReBAC), widely used in online social networks, offers the ability to consider user relationships in defining access control decisions and supports policy individualization. However, to the best of our knowledge, no such attempt has been made to develop a formal ReBAC model for smart IoT systems. This paper proposes a ReBAC IoT dynamic and fine-grained access control model which considers the social relationships among users along with the attributes to support an attributes-aware relationship-based access control model for smart IoT systems. ReBAC IoT is formally defined, illustrated through different use cases, implemented, and tested. 

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4. PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber–Physical Systems

   https://doi.org/10.3390/s24030938  

   Bathalapalli, Venkata K.; Mohanty, Saraju P.; Kougianos, Elias; Iyer, Vasanth; Rout, Bibhudutta (February 2024, Sensors)

   This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber–physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient’s Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle’s Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient’s gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient’s BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time. 

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5. A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

   https://doi.org/10.1145/3406093  

   Otoum, Safa; Kantarci, Burak; Mouftah, Hussein (July 2021, ACM Transactions on Internet Technology)

   Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of . 

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