As Electric Vehicle (EV) charging stations become more prevalent to fuel the growing EV infrastructure, the Open Charge Point Protocol (OCPP) has become essential for connecting these chargers with their central management systems. Despite its popularity, OCPP version 1.6 lacks critical security features, making the charging infrastructure vulnerable to a range of cyberattacks, including Denial of Charging, Charging Profile Manipulation, Unauthorized Access, and Heartbeat Flooding. To tackle these issues, our work introduces a lightweight, Dual-Layer Intrusion Detection System (IDS) designed specifically for OCPP 1.6-enabled EV chargers. Our approach is built and validated using the Federated OCPP 1.6 Intrusion Detection Dataset. It relies on a neural network architecture that looks at both Application-level and Network-level data to detect suspicious behavior. For performance evaluation, we compared our neural network with a Random Forest (RF) baseline. Ultimately, the fully quantized INT8 Multi-Layer Perceptron (MLP) model was deployed to an ESP32 microcontroller, confirming that it can deliver robust performance on resource-constrained hardware. Testing showed that our dual-layer IDS provides high detection accuracy, achieving 96-98% post-deployment accuracy across protocol layers with low latency of 0.19 ms, and consuming only 14.77 μJ per inference. By comparing pre-deployment (on PC) and post-deployment (on-device) results, we tracked how the TinyML model’s performance shifts from development to real-world edge deployment.

The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis. Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems. Traditional centralized methods of medical image analysis pose significant risks to patient privacy and data security, as they require the aggregation of sensitive information in a single location. Furthermore, these methods often suffer from limitations related to data diversity and scalability, hindering the development of universally robust diagnostic models. Recent advancements in machine learning, particularly deep learning, have shown promise in enhancing medical image analysis. However, the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations. This paper introduces FedViTBloc, a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning (FL) combined with Vision Transformers (ViT) and blockchain technology. The proposed system ensures patient data privacy and security through fully homomorphic encryption and differential privacy techniques. By employing a decentralized FL approach, multiple medical institutions can collaboratively train a robust deep-learning model without sharing raw data. Blockchain integration further enhances the security and trustworthiness of the FL process by managing client registration and ensuring secure onboarding of participants. Experimental results demonstrate the effectiveness of FedViTBloc in medical image analysis while maintaining stringent privacy standards, achieving 67% accuracy and reducing loss below 2 across 10 clients, ensuring scalability and robustness.

The Internet of Medical Things (IoMT) is a trans-formative technology that enables medical systems and devices to collaborate seamlessly to improve healthcare delivery. However, the widespread adoption of IoMT raises significant privacy concerns, particularly regarding the aggregation and analysis of sensitive medical data. This paper proposes a novel approach to address these challenges through the utilization of Federated Learning (FL) techniques. We present a comprehensive framework for privacy-preserving data aggregation, leveraging FL to collaboratively train machine learning models across distributed devices while preserving data privacy and security. Our approach decentralizes the model training process and performs computations locally on edge devices. This ensures that sensitive patient data remains secure and never leaves the respective devices, eliminating the need to share data with a central server. The central server combines the weights of the parameters and transmits only the updated weights to each device. Furthermore, we conducted experimental evaluations to demonstrate the effectiveness and efficiency of our proposed approach by achieving high accuracy (90.91%) while ensuring privacy. Additionally, we compared the model with other models and related work to evaluate its performance in terms of accuracy, privacy preservation, and overall effectiveness in securing IoMT data. Overall, our work contributes to the advancement of privacy-enhancing technologies for IoMT, paving the way for more secure and trustworthy healthcare systems in the era of connected medical devices.

While deep learning techniques like convolutional neural networks (CNNs) have shown promise in automating brain tumor detection from magnetic resonance imaging (MRI) images, a critical gap remains in model accuracy and efficiency for multi-class model classification and model explainability. This lack of accuracy and interpretability hinders trust and adoption in clinical settings. This study introduces an innovative approach by enhancing the traditional ResNet50 architecture with advanced regularization techniques and additional convolutional layers to address these challenges. Our model improves training efficiency and robustness, achieving an impressive 98% accuracy on multi-class MRI image classification. Furthermore, integrating Gradient-weighted Class Activation Mapping(Grad-CAM) with modified ResNet50 architecture can improve patient outcomes by explaining automated brain tumor detection.

The emergence of the Internet of Things (IoT) has triggered a massive digital transformation across numerous sectors. This transformation requires efficient wireless communication and connectivity, which depend on the optimal utilization of the available spectrum resource. Given the limited availability of spectrum resources, spectrum sharing has emerged as a favored solution to empower IoT deployment and connectivity, so adequate planning of the spectrum resource utilization is thus essential to pave the way for the next generation of IoT applications, including 5G and beyond. This article presents a comprehensive study of prevalent wireless technologies employed in the field of the spectrum, with a primary focus on spectrum-sharing solutions, including shared spectrum. It highlights the associated security and privacy concerns when the IoT devices access the shared spectrum. This survey examines the benefits and drawbacks of various spectrum-sharing technologies and their solutions for various IoT applications. Lastly, it identifies future IoT obstacles and suggests potential research directions to address them.

The standardization of the fifth-generation (5G) communications has been completed, and the visioning and planning of the sixth-generation (6G) communications have begun, with an objective of casting the high technical standard of new spectrum, high time and phase synchronization accuracy, and 100% geographical coverage to flexibly and efficiently connect upper trillion-level devices in the future. The transition from 5G to 6G is expected to integrate all operational networks, especially the Internet of Things (IoT), which involves massive heterogeneous devices to interact with our physical world.

Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology, blockchain has been broadly used in fintech, smart homes, public health, and intelligent transportation due to its properties of decentralization, collective maintenance, and immutability. Although the dynamism of blockchain abounds in various fields, concerns in terms of network communication interference and privacy leakage are gradually increasing. Because of the lack of reliable attack analysis systems, fully understanding some attacks on the blockchain, such as mining, network communication, smart contract, and privacy theft attacks, has remained challenging. Therefore, in this study, we examine the security and privacy of the blockchain and analyze possible solutions. We systematical classify the blockchain attack techniques into three categories, then discuss the corresponding attack and defense methods based on these categories. We focus on (1) the attack and defense methods of mining pool attacks for blockchain security issues, such as block withholding, 51%, pool hopping, selfish mining, and fork after withholding attacks, in the attack type of consensus excitation; (2) the attack and defense methods of network communication and smart contracts for blockchain security issues, such as distributed denial-of-service, Sybil, eclipse, and reentrancy attacks, in the attack type of middle protocol; and (3) the attack and defense methods of privacy thefts for blockchain privacy issues, such as identity privacy and transaction information attacks, in the attack type of application service. Finally, we discuss future research directions for blockchain security.

Internet of Things (IoT) devices are being used widely in the fields of smart city, smart grid, environmental monitoring, Internet of Vehicles and other fields that need large-scale sensing data. However, the research on storage and computation power for IoT is still in its early stages. The game theory converts the interaction between two IoT devices into a game where the conflict is resolved by utilizing the game’s equilibrium conditions. Our goal with the game theory is to maximize the utility for every device in the IoT network. In this article, we review the recent game-theory-based solutions proposed in IoT networks. We summarize game theory concepts and categorize the common game models for ease of understanding for the reader. Later, we focus on analyzing solutions proposed in resource allocation, task scheduling, node selection, quality of service, and network security. Finally, we summarize research challenges and propose future research directions.

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k -anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person's daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider the data privacy on OSNs at the network level rather than the user level. This network-level privacy helps us to prevent information leakage to third-party users, such as advertisers. We propose a novel scheme that combines the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We use an enhanced equi-cardinal clustering (ECC) as a way to achieve k-anonymity. We further improve k-anonymity with l-diversity. Our proposed enhanced ECC ensures that there are at least “k” users in any given network as well as the attributes in each cluster has at least l-distinct values. We further provide proofs on how the proposed ECC ensures k-anonymity and the maximum information loss. We consider a weighted directed social network graph as an input to our method to consider the existing complexities in a social network. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person's daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider data privacy on online social networks at the network level rather than user level. This network level privacy helps us to prevent information leakage to third-party users like advertisers. We propose a novel scheme that combines both node and edge privacy of the OSN by clustering the similar users. We further improve traditional clustering by utilizing the concept of k-anonymity. Our enhanced equicardinal clustering ensures that there are at least k users in any given network. We further provide proofs on how the proposed equicardinal clustering ensures k-anonymity and the maximum information loss. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.

The recommender system is mainly used in the e-commerce platform. With the development of the Internet, social networks and e-commerce networks have broken each other’s boundaries. Users also post information about their favorite movies or books on social networks. With the enhancement of people’s privacy awareness, the personal information of many users released publicly is limited. In the absence of items rating and knowing some user information, we propose a novel recommendation method. This method provides a list of recommendations for target attributes based on community detection and known user attributes and links. Considering the recommendation list and published user information that may be exploited by the attacker to infer other sensitive information of users and threaten users’ privacy, we propose the CDAI (Infer Attributes based on Community Detection) method, which finds a balance between utility and privacy and provides users with safer recommendations.

This paper analyses an image watermarking scheme based on Support Vector Regression (SVR) proposed by R. Shen et al. We describe various attacks against this scheme and show that watermark tampering can be done even when one does not know the secret key used to embed the watermark. Next we discuss methods to extract the keys used in the scheme under various usage scenarios. Our results show that Shen et al.'s scheme is not secure.