Advances in Vehicular Ad-hoc Networks (VANETs): challenges and road-map for future development

Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of vehicular ad-hoc networks (VANETs) over the past few years. A vehicular network consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of intelligent transportation system (ITS). Hence, governments, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interesting field of mobile wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well as the ways forward to achieving the long awaited ITS

An Explainable AI System for Medical Image Segmentation With Preserved Local Resolution: Mammogram Tumor Segmentation

Medical image segmentation aims to identify important or suspicious regions within medical images. However, many challenges are usually faced while developing networks for this type of analysis. First, preserving the original image resolution is of utmost importance for this task where identifying subtle features or abnormalities can significantly impact the accuracy of diagnosis. While introducing the dilated convolution improves the resolution of the convolutional neural network (CNN), it is not without shortcoming, i.e., the loss of local spatial resolution due to increased kernel sparsity in checkboard patterns. To address this shortcoming, we conceptualize a double-dilated convolution module for maintaining local spatial resolution while improving the receptive field size. Then, this approach is applied, as a proof-of-work, to tumor segmentation task in mammograms. In addition, our proposal also tackles the class imbalance problem, originating at the pixel level of the mammogram screenings, by identifying and selecting the best candidate among a number of potential loss functions to facilitate mass segmentation. We also carry out quantitative and qualitative evaluations of the interpretability of our proposal by leveraging Grad-CAM (Gradient weighted Class Activation Map). We also present a comparative performance evaluation with existing explainable techniques tailored for segmenting images. Moreover, an empirical assessment on lesion segmentation is conducted on mammogram samples from the INBreast dataset, both with and without incorporating our envisaged dilation module into CNN. The obtained results elucidate the effectiveness of our proposal based on mass segmentation performance measures, such as Dice similarity and Miss Detection rate. Our analysis also promotes using the Tversky Loss function in training pixel-imbalanced data and integrating Grad-CAM for explaining image segmentation results

Enhancing Security in ZigBee Wireless Sensor Networks: A New Approach and Mutual Authentication Scheme for D2D Communication

The latest version of ZigBee offers improvements in various aspects, including its low power consumption, flexibility, and cost-effective deployment. However, the challenges persist, as the upgraded protocol continues to suffer from a wide range of security weaknesses. Constrained wireless sensor network devices cannot use standard security protocols such as asymmetric cryptography mechanisms, which are resource-intensive and unsuitable for wireless sensor networks. ZigBee uses the Advanced Encryption Standard (AES), which is the best recommended symmetric key block cipher for securing data of sensitive networks and applications. However, AES is expected to be vulnerable to some attacks in the near future. Moreover, symmetric cryptosystems have key management and authentication issues. To address these concerns in wireless sensor networks, particularly in ZigBee communications, in this paper, we propose a mutual authentication scheme that can dynamically update the secret key value of device-to-trust center (D2TC) and device-to-device (D2D) communications. In addition, the suggested solution improves the cryptographic strength of ZigBee communications by improving the encryption process of a regular AES without the need for asymmetric cryptography. To achieve that, we use a secure one-way hash function operation when D2TC and D2D mutually authenticate each other, along with bitwise exclusive OR operations to enhance cryptography. Once authentication is accomplished, the ZigBee-based participants can mutually agree upon a shared session key and exchange a secure value. This secure value is then integrated with the sensed data from the devices and utilized as input for regular AES encryption. By adopting this technique, the encrypted data gains robust protection against potential cryptanalysis attacks. Finally, a comparative analysis is conducted to illustrate how the proposed scheme effectively maintains efficiency in comparison to eight competitive schemes. This analysis evaluates the scheme’s performance across various factors, including security features, communication, and computational cost

Detection of Distributed Denial of Charge (DDoC) Attacks Using Deep Neural Networks With Vector Embedding

To prevent excessive strain on the electrical grid and avoid long waiting times of the electric vehicle (EV) at charging stations, charging coordination mechanisms have been implemented. However, there is a potential vulnerability that enable adversaries to launch distributed denial of charge (DDoC) attacks. In these attacks, fake charging requests are sent to book charging time slots without showing up for charging. Existing mechanisms assume the requests from EVs are valid and do not address the detection of DDoC attacks. This research paper aims to assess the disruptive capabilities of DDoC attacks on charging coordination mechanisms and utilize deep neural networks incorporated with vector embedding to develop detectors that can protect against these attacks. The detection approach relies on identifying abnormal behavior that deviates from the typical patterns of charging demand at the charging station. To train and evaluate the detectors, we utilize real routes of vehicles and technical parameters of EVs released by their manufacturers to create a benign dataset. Subsequently, various attacks are introduced to generate a malicious dataset. By analyzing this dataset, temporal and spatial correlations are identified, which can be learned by our detectors to detect the attacks accurately. The reason for the design of our detector is based on utilizing the embedding layer to identify concealed patterns in regular charging demand information. Additionally, the deep learning network is employed to comprehend and learn the connections over time in the sequential data, as well as the relationships between the data of adjacent charging stations. We conducted thorough experiments to assess our detectors, and the outcomes demonstrate that the suggested detectors are highly effective in terms of accurately detecting of DDoC attacks while keeping false alarms to a minimum

Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation

Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Research literature demonstrates that the support vector machine (SVM) consistently demonstrates remarkable accuracy in medical diagnosis. Nonetheless, safeguarding patients’ health data privacy and preserving the intellectual property of diagnosis models is of paramount importance. This concern arises from the common practice of outsourcing these models to third-party cloud servers that may not be entirely trustworthy. Few studies in the literature have delved into addressing these issues within SVM-based diagnosis systems. These studies, however, typically demand substantial communication and computational resources and may fail to conceal classification results and protect model intellectual property. This paper aims to tackle these limitations within a multi-class SVM medical diagnosis system. To achieve this, we have introduced modifications to an inner product encryption cryptosystem and incorporated it into our medical diagnosis framework. Notably, our cryptosystem proves to be more efficient than the Paillier and multi-party computation cryptography methods employed in previous research. Although we focus on a medical application in this paper, our approach can also be used for other applications that need the evaluation of machine learning models in a privacy-preserving way such as electricity theft detection in the smart grid, electric vehicle charging coordination, and vehicular social networks. To assess the performance and security of our approach, we conducted comprehensive analyses and experiments. Our findings demonstrate that our proposed method successfully fulfills our security and privacy objectives while maintaining high classification accuracy and minimizing communication and computational overhead

Countering Evasion Attacks for Smart Grid Reinforcement Learning-Based Detectors

Fraudulent customers in smart power grids employ cyber-attacks by manipulating their smart meters and reporting false consumption readings to reduce their bills. To combat these attacks and mitigate financial losses, various machine learning-based electricity theft detectors have been proposed. Unfortunately, these detectors are vulnerable to serious cyber-attacks, specifically evasion attacks. The objective of this paper is to investigate the robustness of deep reinforcement learning (DRL)-based detectors against our proposed evasion attacks through a series of experiments. Firstly, we introduce DRL-based electricity theft detectors implemented using the double deep Q networks (DDQN) algorithm. Secondly, we propose a DRL-based attack model to generate adversarial evasion attacks in a black box attack scenario. These evasion samples are generated by modifying malicious reading samples to deceive the detectors and make them appear as benign samples. We leverage the attractive features of reinforcement learning (RL) to determine optimal actions for modifying the malicious samples. Our DRL-based evasion attack model is compared with an FGSM-based evasion attack model. The experimental results reveal a significant degradation in detector performance due to the DRL-based evasion attack, achieving an attack success rate (ASR) ranging from 92.92% to 99.96%. Thirdly, to counter these attacks and enhance detection robustness, we propose hardened DRL-based defense detectors using an adversarial training process. This process involves retraining the DRL-based detectors on the generated evasion samples. The proposed defense model achieves outstanding detection performance, with a degradation in ASR ranging from 1.80% to 9.20%. Finally, we address the challenge of whether the DRL-based hardened defense model, which has been adversarially trained on DRL-based evasion samples, is capable of defending against FGSM-based evasion samples, and vice versa. We conduct extensive experiments to validate the effectiveness of our proposed attack and defense models

Oral semaglutide effectiveness and safety in real world practice; The REVOLUTION study

Aims: This study seeks to provide insights into the practical application and effects of oral semaglutide in Saudi T2DM patients under routine medical supervision. Methods: The primary outcome measure was the laboratory HbA1c. Secondary measures included fasting blood glucose (FBG), weight, and hypoglycemia. All variables were checked after six months and 12 months of initiation. Results: The analysis of this study included 245 uncontrolled (HbA1c > 7 %) T2DM patients. The mean baseline HbA1c was 10.1 % (1.2). HbA1c was reduced by an average of 3.1 % (0.8) and 3.2 % (0.8) at 6 and 12 months, respectively. The frequency of hypoglycemia events in the last three months before semaglutide was initiated was 4.4 (1.1). The frequency of hypoglycemia events in the last three months was 2.2 (0.8) and 0.7 (0.4) at 6-month and 12-month follow-up visits, respectively. The percent reduction in body mass index (BMI) was an average of 13.0 % (1.4) and 19.7 % (3.4) at six months and 12 months, respectively. Lipid profile and blood pressure were improved at six months and 12 months. Conclusions: Oral semaglutide provided substantial glycemic and weight-loss benefits in adult individuals with T2DM