Internet of Medical Things-based healthcare system (IoMTHS) is a kind of industrial information system that integrates life monitoring, pathological inference and drug therapy. However, the sensitive nature and high value of its data make it a prime target for cyberattacks. Although many multiserver authentication protocols have been studied in recent years to ensure that only authorized users can access medical services, new vulnerabilities are always identified and covertly utilized by the smarter adversary due to lack of continuous monitoring and dynamic authentication, reducing the trustworthiness of IoMTHS. To address above challenges, in this article, we propose a multiserver authentication scheme with integrated monitoring (MAIM) for IoMTHS, which achieves user locked access control by strictly and continuously binding system access permissions and user behavior. MAIM consists of a three-factor-based static authentication (TFSA) and a deep learning-based continuous authentication (DLCA). TFSA utilizes double-anonymity strategy to protect users’ privacy and track their malicious behaviors, and uses physical unclonable function (PUF) to protect the security of privacy information in users’ devices and servers, which achieves lightweight and three-factor secrecy. The DLCA trains a deep neural network to recognize the legitimacy of users based on the user behavior transmitted by their sensing devices. TFSA is provably secure under the random oracle model, whereas DLCA exhibits high feasibility with experimental accuracy reaching 100%.

In vehicular ad-hoc networks (VANETs), existing traffic accident handling schemes are adoptable only in the scenario with roadside unit (RSU) deployment, and the generation of accident reports relies on RSUs and witness vehicles. Without the confirmation from involved vehicles in the accident, it will probably cause disputes afterward, and RSU captured attacks may also affect authentication of vehicles and correctness of accident reports. To address the above issues, we propose a vehicle to vehicle (V2V) and vehicle to RSU (V2R) authentication and traffic accident handling protocol, in which accident vehicles are sufficient to generate accident reports, thereby enhancing autonomy of the vehicular communication system and reducing reliance on external infrastructure. Furthermore, for ensuring integrity and traceability of accident reports, we utilize blockchain to keep registration information and jointly signed reports, which realizes efficient and secure mutual authentications in V2V and V2R protocols. Finally, for preserving privacy of vehicles, we integrate elliptic curve cryptosystem (ECC) and symmetric encryption to design a dynamic pseudo-identity strategy, which still allows the registration center to track malicious vehicles. The formal security proof and comparative analysis validate that our protocol preserves higher security and lower overhead by comparison with related schemes.

To ensure the secure access control mechanisms for the wireless sensor networks (WSN) with a 5G-enabled IoT system, very recently, Sahoo et al. (IEEE Internet of Things Journal, 10(17), pp. 15087-15099, DOI: 10.1109/JIOT.2023.3264565, Sep. 1, 2023) proposed a three-factor-based authentication scheme of 5G WSN for IoT system, and claimed that their scheme can achieve all security properties. However, in this paper, we carefully analyze their scheme and point out that their scheme is vulnerable to user impersonation attacks, sensor node captured attacks, simultaneous impersonation attacks on the gateway and sensor node, and cannot achieve user unlinkability and three-factor secrecy, and an adversary can obtain the encrypted data sent from the sensor node, and can also forge the encrypted data. In addition, we analyze the reasons why their scheme suffers from these attacks and propose the remedial suggestions.

Due to the prevalence of GPS-equipped cameras (e.g., smartphones and surveillance cameras), massive amounts of geo-tagged images capturing urban streets are increasingly being collected. Consequently, many smart city applications have emerged, relying on efficient image search. Such searches include spatial-visual queries in which spatial and visual properties are used in tandem to retrieve similar images to a given query image within a given geographical region. Towards this end, new index structures that organize images based on both spatial and visual properties are needed to efficiently execute such queries. Based on our observation that street images are typically similar in the same spatial locality, index structures for spatial-visual queries can be effectively built on a spatial index (i.e., R*-tree). Therefore, we propose a class of R*-tree indexes, particularly, by associating each node with two separate minimum bounding rectangles (MBR), one for spatial and the other for (dimension-reduced) visual properties of their contained images, and adapting the R*-tree optimization criteria to both property types.