Abstract

The Internet of Medical Things (IoMT) is a collection of smart healthcare devices, hardware infrastructure, and related software applications, facilitating to connect healthcare information technology system via the Internet. It is also called IoT in healthcare, facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data. In other words, IoMT is an amalgam of medical devices and applications, which improves overall healthcare outcomes. However, this system is prone to security- and privacy-related attacks on healthcare data. Therefore, providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential. To mitigate this, we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT. The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT. Furthermore, we provide the security analysis of the proposed scheme to show its security against different possible attacks. Moreover, a comparative study of the proposed scheme with other similar schemes is presented. Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs, and security and functionality attributes. Finally, we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

Abstract

The cyber physical systems integrate the sensing, computation, control and networking processes into physical objects and infrastructure, which are connected through the Internet to execute a common task. Cyber physical systems can be applied in various applications, like healthcare, transportation, industrial production, environment and sustainability, and security and surveillance. However, the tight coupling of cyber systems with physical systems introduce challenges in addressing stability, security, efficiency and reliability. The machine learning (ML) security is the inclusion of cyber security mechanism to provide protection to the machine learning models against various cyber attacks. The ML models work through the traditional training and testing approaches. However, execution of such kind of approaches may not function effectively in case if a system is connected to the Internet. As online hackers can exploit deployed security mechanisms and poison the data. This data is then taken as the input by the ML models. In this article, we provide the details of various machine learning security attacks in cyber physical systems. We then discuss some defense mechanisms to protect against these attacks. We also present a threat model of ML security mechanisms deployed in cyber systems. Furthermore, we discuss various issues and challenges of ML security mechanisms deployed in cyber systems. Finally, we provide a detailed comparative study on performance of the ML models under the influence of various ML attacks in cyber physical systems.

Abstract

Elliptic Curve Cryptography (ECC)-based authenti-cation schemes for the Radio-frequency identification (RFID) environment have emerged as a very safe, secure, and robust candidate of mutual authentication. However, the limited resource availability in a passive tag makes such schemes difficult to realize in practice. Public/private key pair generation, storage, and the computation using those keys are the major bottlenecks towards the faster and efficient design of such schemes. Therefore, we have designed our scheme without using public/private key pairs. We only utilize the Elliptic Curve Discrete Logarithm Problem (ECDLP) property for the realization of our key-less scheme on the secure elliptic curves. Our scheme is primarily aimed at efficient implementation of authentication schemes in Warehouse Management System (WMS), where the data is stored in local servers. This new idea helps to save the memory space in the tags and the server. Moreover, the computation cost also reduces to a great extent in comparison to other schemes. We have compared our scheme with several other schemes and found it efficient in terms of storage cost as well. The simulation result of our scheme with the popular automated software validation tool, Scyther, shows that it is safe from different possible attacks.

Abstract

Healthcare 5.0 is a system that can be deployed to provide various healthcare services. It does these services by utilising a new generation of information technologies, such as Internet of Things (IoT), Artificial Intelligence (AI), Big data analytics, blockchain and cloud computing. Due to the introduction of healthcare 5.0, the paradigm has been now changed. It is disease-centered to patient-centered care where it provides healthcare services and supports to the people. However, there are several security issues and challenges in healthcare 5.0 which may cause the leakage or alteration of sensitive healthcare data. This demands that we need a robust framework in order to secure the data of healthcare 5.0, which can facilitate different security related procedures like authentication, access control, key management and intrusion detection. Therefore, in this review article, we propose the design of a secure generalized healthcare 5.0 framework. The details of various applications of healthcare 5.0 along with the security requirements and threat model of healthcare 5.0 are provided. Next, we discuss about the existing security mechanisms in healthcare 5.0 along with their performance comparison. Some future research directions are finally discussed for the researchers working in healthcare 5.0 domain.

Abstract

Dynamic charging is a potential technology that would enable electric vehicles to be charged while they are in motion. Significant security and privacy concerns, on the other hand, arise as a result of communications between electric vehicles and a variety of dynamic charging system entities occurring over a public channel. Numerous authentication protocols have been presented recently to address security concerns associated with dynamic charging system. Nonetheless, we provide a lightweight authentication protocol suite that enables mutual authentication and session key agreement between the electric vehicle and the charging system while safeguarding against numerous attacks. The EV-PUF is based on physical unclonability, which is gaining popularity as a substitute for ultra-lightweight authentication and resistance to machine learning based attacks. Additionally, the security of the EV-PUF suite has been demonstrated using the random oracle model and state-of-the-art tool, Tamarin. According to the performance analysis, it is apparent that the EV-PUF suite improves efficiency in terms of consumption of less communication and computing overhead.

Abstract

Smart healthcare technologies transform the tradi- tional healthcare system in all possible ways. Smart healthcare has enormous number of benefits over the traditional system. However, at the same time, it also suffers from some healthcare data security and privacy related issues as the potential Internet attackers may get access to the sensitive healthcare through the deployment of various kinds of attacks. To mitigate these issues, in this paper, a new lightweight remote user authentica- tion and key establishment scheme (in short, UAKM-SH) has been proposed by making the use of lightweight cryptographic operations. The security analysis of UAKM-SH is conducted to prove its robustness against various possible attacks. A detailed comparative study among the proposed UAKM-SH and other ex- isting schemes shows that UAKM-SH performs better in terms of computation cost, communication cost, and the offerered security and functionality features. Finally, the testbed implementation of UAKM-SH is given to observe its behaviour in the real time scenario. Index Terms—Smart healthcare, authentication, key agree- ment, security, testbed implementation.

Abstract

Parallel computing of hash functions along with the security requirements have great advantage in order to reduce the time consumption and overhead of the CPU. In this article, a keyed hash function based on farfalle construction and chaotic neural networks (CNNs) is proposed, which generates a hash value with arbitrary (defined by user) length (eg, 256 and 512 bits). The proposed hash function has parallelism merit because it is built over farfalle construction which avoids the dependency between the blocks of a given message. Moreover, the proposed hash function is chaos based (ie, it relies on chaotic maps and CNNs which have non-periodic behavior). The security analysis shows that the proposed hash function is robust and satisfies the properties of hash algorithms, such as random-like (non-periodic) behavior, ideal sensitivity to original message and secret key, one-way property and optimal diffusion effect. The speed performance of the hash function is also analyzed and compared with a hash function which was built based on sponge construction and CNN, and compared with secure hash algorithm (SHA) variants like SHA-2 and SHA-3. The results have shown that the proposed hash function has lower time complexity and higher throughput especially with large size messages. Additionally, the proposed hash function has enough resistance to multiple attacks, such as collision attack, birthday attack, exhaustive key search attack, preimage and second preimage attacks, and meet-in-the-middle attack. These advantages make it ideal to be used as a good collision-resistant hash function.

Abstract

With the ongoing revolutionary growth of the industrial Internet of Things and smart grid networks, smart grid (SG) communication has been acknowledged as a next-generation network for intelligent and efficient electric power transmission. In SG networks, smart meters (SMs) generally send requests for electricity demand to service providers (SPs), which deal with the requests for efficient energy distribution. However, SGs experience many security issues with the deployed SMs and untrusted wireless communication. To tackle these security issues, we propose a privacy-preserving authentication scheme for demand response management in SGs, called BPPS. It can resist various attacks and achieve secure mutual authentication with key agreement; moreover, it provides integrity of demand-response data using blockchain. Moreover, we perform the informal and formal (mathematical) security analysis to confirm that BPPS is secure against various attacks and achieves session key security, respectively. Furthermore, we conduct the performance and simulation analysis for SGs

Abstract

The deployed vehicles in an Internet of Vehicles (IoV) can take intelligent decisions by means of exchanging the real- time traffic-related information between the vehicles and IoV in- frastructures. This further reduces the probability of the traffic jams and accidents. However, the insecure (public) communication among the various entities in IoV makes various security threats and attacks that can be launched by passive/active adversaries present in the network. In view of this context, there is a need of an efficient cryptographic primitive which can produce single compact signature. A multi-signature scheme (MSS) empowers a collection of signers to conjointly sign a given message using a single compact signature that can be verified by any verifier. Herein, we put forward a new identity-based multivariate MSS, namely MV-MSS, which is built on top of the intractability of multivariate-quadratic (MQ) problem. The fact is that multivariate public key cryptosystem provides fast, post-quantum safe and effi- cient primitives, which makes it the front runner candidate among the post-quantum cryptographic candidates. MV-MSS is proven to be secure in the existential unforgeability under chosen-message and chosen identity attack model if solving the MQ problem is NP-hard. We then incorporate the designed MV-MSS in IoV ap- plication where the leader (cluster head) selected from a group of vehicles in a dynamic cluster forms the multi-signatures on the messages securely received from its member vehicles. Later, the messages along with their multi-signatures are forwarded to the nearby road-side unit (RSU) of the cluster head, which are then for- warded to a cloud server in the blockchain center maintained by a Peer-to-Peer (P2P) cloud servers network. In this way, the messages

Abstract

With rapid advancements in the technology, almost all the devices around are becoming smart and contribute to the Internet of Things (IoT) network. When a new IoT device is added to the network, it is important to verify the authenticity of the device before allowing it to communicate with the network. Hence, access control is a crucial security mechanism that allows only the authenticated node to become the part of the network. An access control