The Internet of Things (IoT) is an evolving paradigm expected to permeate every aspect of human existence. IoT is a growing trend in which numerous devices interconnect with each other to transmit sensitive data. One of its significant application areas is the Internet of Medical Things (IoMT), which promises a contemporary healthcare environment via linked sensors, clinical systems, and wearable medical devices. However, public communication among these devices faces challenges like security, privacy, authentication, and machine learning/modeling attacks. Additionally, most existing schemes are vulnerable to impersonation, denial-of-service, and machine-learning/modeling attacks. Therefore, this article addresses these challenges by designing a lightweight authentication scheme that utilizes a one-time physical unclonable function (OPUF) and elliptic curve cryptography to reduce the likelihood of machine learning/modeling attacks on wearable medical devices. We utilizeOPUFto resist machine learning/modeling attacks. We also employ a rate-limiting mechanism that restricts the number of authentication requests within a specific time window to enhance resistance against denial-of-service (DoS) attacks. Moreover, the devised scheme also offers resistance to impersonation, session key leakage, ephemeral secret leakage, desynchronization, and ML-based modeling attacks. We analyze the security and reliability of the devised scheme using informal and formal analysis. Informal analysis indicates that the scheme offers essential security features, while formal analysis substantiates these findings. In the end, we present the results of the performance analysis, which show that the devised scheme achieves an average reduction of 26.92% and 21.53% in computational and communication costs, respectively.

Computer-aided detection through deep learning is becoming a prevalent approach across various fields, including the detection of anomalies in medical procedures. One such medical procedure involves the placement of medical tubes to provide nutrition or other medical interventions in critically ill patients. Medical tube placement can be highly complex and prone to subjective errors. Malposition of medical tubes is often observed and associated with significant morbidity and mortality. In addition, continuous verification using manual procedures such as capnography, pH testing, auscultation, and visual inspection through chest X-ray (CXR) imaging is required. In this paper, we propose a Conv-MTD, a medical tube detection (MTD) model that detects the placement of medical tubes using CXR images, assisting radiologists with precise identification and categorizing the tubes into normal, abnormal, and borderline placement. Conv-MTD leverages the EfficientNet-B7 architecture as its backbone, enhanced with an auxiliary head in the intermediate layers to mitigate vanishing gradient issues common in deep neural networks. The Conv-MTD is further optimized using post-training 16-bit floating-point (FP16) quantization, which significantly reduces memory consumption by 50% and 2x improvement in inference speed without compromising accuracy. This optimization allows Conv-MTD to achieve efficient performance without requiring high-end computational resources, making it suitable for deployment on point-of-care devices. Conv-MTD provided the best performance, with an average area under the receiver operating characteristic curve (AUC) of 0.95 using the open-source RANZCR CLiP dataset. The proposed Conv-MTD has the potential to operate on resource-constrained point-of-care devices due to its use of FP16 computation, enabling low-cost and automated assessments in various healthcare settings.

In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system in RFID-based supply chain management, which is intricately linked with the advances in the Internet of Things (IoT). RFID technology employs electromagnetic fields to identify and track tags on objects and revolutionizes the management and tracking of items in the supply chain. However, the public communication among RFID tags, RFID readers, and supply chain infrastructure predominantly escalates security and privacy challenges. Several authentication protocols have been proposed to overcome these challenges. However, the vulnerability of most proposed protocols to numerous security attacks renders them inefficient. Therefore, to address these crucial challenges, we devised an RFID-based authentication protocol for supply chain management systems. The incorporation of a physically unclonable function (PUF) into the protocol fortifies the system against physical tampering attacks. To validate the security and effectiveness of the devised protocol, both informal and formal security analysis are conducted. The formal security analysis is conducted using the widely used random oracle model. The informal security analysis reveals that the devised protocol provides enhanced and efficient security features. Furthermore, we perform a comparative analysis with related protocols, focusing on critical performance metrics like communication cost, computation cost, and overall security features. The results of the comparative analysis are promising, indicating a substantial 30.92% reduction in computational cost and a 23.98% reduction in communication cost in comparison to related protocols, thus highlighting the protocol’s superior performance and resource efficiency.

Internet of Things (IoT) devices are crucial components in e-healthcare networks. It enables remote patient health monitoring and facilitates seamless communication among medical sensors, wearable devices, and healthcare providers through public communication channels. Despite these advantages, the use of public communication among medical sensors in e-healthcare networks introduces critical challenges, such as vulnerability to impersonation, physical capture, and ephemeral secret leakage, particularly in resource-constrained environments. In recent years, various access control protocols have been developed to mitigate these risks. However, these protocols often fail to ensure robust security while incurring significant communication and computation overhead. To overcome these limitations, we propose a lightweight and robust access control protocol for IoT-based e-healthcare networks using chaotic maps. We propose a novel protocol that integrates a PUF-based mechanism to mitigate the challenges of physical tampering and cloning attacks in e-healthcare networks. It leverages the inherent uniqueness of PUF and enhances security through the high-entropy properties of chaotic maps. We analyze the proposed protocol informally, which confirms that it significantly bolsters efficiency and security. We also validate the security using the Random or Real (RoR) model. Moreover, we verify the security of the proposed protocol using Scyther. These analyses highlight that the proposed protocol offers robust resistance to numerous attacks, such as impersonation, physical capture, and ephemeral secret leakage. Moreover, we also compare it with existing and relevant protocols. The comparative analysis showcases its superior performance. Notably, the proposed authentication protocol significantly reduces 46.84% computational overhead and decreases 31.30% communication overhead, underscoring its enhanced performance and resource efficiency.

Due to the development of internet rapidly, the secure transmission of information has become more and more importance. Until now, there are many scholars study in the topic of data hiding. Specially, the reversible data hiding scheme catch the researcher's attention. No matter how the researchers use the different technology to embed the secret information, they always try to increase the hide space within the permission quality of the cover image. However, the hiding positions are subject to the highest point by using the NSAS-scheme or HKC-scheme. To overcome this shortcoming, a reversible data hiding based on adaptive hiding location will be proposed in this paper. According to the experimental results, we can prove that our proposed scheme still not only keeps high embedding capacity and stego-image quality but also maintain the better security.

In 2001, the sanitizable signature was proposed by Steinfeld et al. Until now, many sanitizable signature schemes have been proposed. However, current sanitizable signature schemes are faced with the dishonest sanitizer or additional sanitizing problem due to the sanitizer can modify the signed document unrestrictedly. Therefore, a flexible sanitizable signature scheme based on bilinear mapping will be proposed in this paper. According to our security analysis, this proposed scheme is not only to keep the security requirement of sanitizable signature but also improve the disadvantage of related schemes.

RFID is a convenient and efficient mechanism used for identification or authentication in industry, agriculture, commerce, health care, entrance control, etc. Most of the RFID systems need security mechanisms for authentication and privacy protection. In 2004, Gao et al. propose a RFID system for supply chain, their system has drawbacks on security. Recently, Chen et al. proposed an improvement to enhance the security. However, an adversary can masquerade as a legal user if he/she eavesdrops the transmitted message in this scheme. In this paper, we will show that Chen et al.psilas protocol is not as secure as they declared and then an improvement protocol to fix the flaws is proposed. The improvement protocol is secure with merits of privacy protection, resisting counterfeit attack and obtain mutual authentication.

One of the killer applications for radio frequency identification (RFID) system is the automatic identification of physical objects. To do that, each object must be labeled with a RFID tag containing identifying data. However, collisions occur when several tags send their data simultaneously. This paper proposes a secure scheme for zero-collision RFID tags identification based on code division multiple access (CDMA) and hash-chain mechanics. The scheme not only allows several tags send their identifying data simultaneously without collision, but also provides the following secure properties for RFID reader and tags: mutual authentication, tag anonymity, robustness and forward secrecy.

For original paper see ibid., vol. 7, no. 2, p. 330-8 (2005). To protect multimedia data in audio and video streaming applications from unauthorized access by intruders, Yeung et al. recently proposed a multikey multimedia proxy with enhanced security features based on asymmetric reversible parametric sequences (ARPS) and the RSA technique. The authors claimed that their scheme provided a number of security features, including data confidentiality against member collusion. However, the security of their scheme is not as robust as they claim. In this paper, it is shown that the YLY-scheme remains vulnerable to collusion attacks