Objective: Cervical cancer ranks among the top causes of death among females in developing countries. The most important procedures that should be followed to guarantee the minimizing of cervical cancer’s aftereffects are early identification and treatment under the finest medical guidance. One of the best methods to find this sort of malignancy is by looking at a Pap smear image. For automated detection of cervical cancer, the available datasets often have missing values, which can significantly affect the performance of machine learning models. Methods: To address these challenges, this study proposes an automated system for predicting cervical cancer that efficiently handles missing values with SMOTE features to achieve high accuracy. The proposed system employs a stacked ensemble voting classifier model that combines three machine learning models, along with KNN Imputer and SMOTE up-sampled features for handling missing values. Results: The proposed model achieves 99.99% accuracy, 99.99% precision, 99.99% recall, and 99.99% F1 score when using KNN imputed SMOTE features. The study compares the performance of the proposed model with multiple other machine learning algorithms under four scenarios: with missing values removed, with KNN imputation, with SMOTE features, and with KNN imputed SMOTE features. The study validates the efficacy of the proposed model against existing state-of-the-art approaches. Conclusions: This study investigates the issue of missing values and class imbalance in the data collected for cervical cancer detection and might aid medical practitioners in timely detection and providing cervical cancer patients with better care.

The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity. However, although many researchers are interested in the PLA and how it might be used to improve wireless security, there is surprisingly little literature on the subject, with no systematic overview of the current state-of-the-art PLA and the main foundations involved. Therefore, this paper aims to determine and systematically compare existing studies in the physical layer authentication. This study showed whether machine learning approaches in physical layer authentication models increased wireless network security performance and demonstrated the latest techniques used in PLA. Moreover, it identified issues and suggested directions for future research. This study is valuable for researchers and security model developers interested in using machine learning (ML) and deep learning (DL) approaches for PLA in wireless communication systems in future research and designs.

A cyber-physical system (CPS) integrates various interconnected physical processes, computing resources, and networking units, as well as monitors the process and applications of the computing systems. Interconnection of the physical and cyber world initiates threatening security challenges, especially with the increasing complexity of communication networks. Despite efforts to combat these challenges, it is difficult to detect and analyze cyber-physical attacks in a complex CPS. Machine learning-based models have been adopted by researchers to analyze cyber-physical security systems. This paper discusses the security threats, vulnerabilities, challenges, and attacks of CPS. Initially, the CPS architecture is presented as a layered approach including the physical layer, network layer, and application layer in terms of functionality. Then, different cyber-physical attacks regarding each layer are elaborated, in addition to challenges and key issues associated with each layer. Afterward, deep learning models are analyzed for malicious URLs and intrusion detection in cyber-physical systems. A multilayer perceptron architecture is utilized for experiments using the malicious URL detection dataset and KDD Cup99 dataset, and its performance is compared with existing works. Lastly, we provide a roadmap of future research directions for cyber-physical security to investigate attacks concerning their source, complexity, and impact.

Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs). UAV spatial coordinates, determined via Global Positioning System (GPS) signals, are susceptible to cyberattacks due to unencrypted and unauthenticated transmissions with GPS spoofing being a significant threat. To mitigate these vulnerabilities, intrusion detection systems (IDSs) for UAVs have been developed and enhanced using machine learning (ML) algorithms. However, Adversarial Machine Learning (AML) has introduced new risks by exploiting ML models. This study presents a UAV-IDS employing AML methodology to enhance the detection and classification of GPS spoofing attacks. The key contribution is the development of an AML detection model that significantly improves UAV system robustness and security. Our findings indicate that the model achieves a detection accuracy of 98%, demonstrating its effectiveness in managing large-scale datasets and complex tasks. This study emphasizes the importance of physical layer security for enhancing IDSs in UAVs by introducing a novel detection model centered on an adversarial training defense method and advanced deep learning techniques.

Wireless physical layer authentication has emerged as a promising approach to wireless security. The topic of wireless node classification and recognition has experienced significant advancements due to the rapid development of deep learning techniques. The potential of using deep learning to address wireless security issues should not be overlooked due to its considerable capabilities. Nevertheless, the utilization of this approach in the classification of wireless nodes is impeded by the lack of available datasets. In this study, we provide two models based on a data-driven approach. First, we used generative adversarial networks to design an automated model for data augmentation. Second, we applied a convolutional neural network to classify wireless nodes for a wireless physical layer authentication model. To verify the effectiveness of the proposed model, we assessed our results using an original dataset as a baseline and a generated synthetic dataset. The findings indicate an improvement of approximately 19% in classification accuracy rate.

The proliferation of Internet of Things (IoT) devices and their vulnerability to malware infections pose critical security challenges in IoT networks and multi-access edge computing (MEC). Traditional federated learning-based IoT malware detection (FL-IMD) methods face limitations in privacy, reliability, and client authentication, necessitating innovative solutions. This study proposes a reliable and privacy-preserving federated learning framework (RPFL) that integrates elliptic curve digital signature algorithm (ECDSA), homomorphic encryption and blockchain technology to enhance privacy, reliability, and client verification in FL-IMD. To address challenges with fully homomorphic encryption (FHE), particularly its reliance on an external aggregator, we introduce two smart contract-based schemes: one to incentivize client participation and another to mitigate aggregator failures. Experimental results on the N-BaIoT dataset show that RPFL achieves IoT malware detection accuracy comparable to state-of-the-art methods, while significantly enhancing reliability and privacy in the aggregation process. Furthermore, our blockchain integration outperforms the prominent blockchain-based FL framework, BCFL, by reducing communication costs and latency. These findings highlight the potential of RPFL to advance privacy-preserving, reliable, and secure FL-based IMD in IoT networks and MEC environments.