Micromobility, encompassing low-speed transport modes such as e-scooters and bicycles, plays a pivotal role in enhancing urban mobility within the Intelligent Transportation System (ITS). For such an environment, a multitude of challenges arises, including optimizing system architecture and managing energy consumption efficiently. This paper presents an innovative approach aimed at improving micromobility integration within an Internet of Vehicles (IoV) ecosystem by leveraging Vehicular Adhoc Networks (VANETs) and IEEE 802.11p communication standard. To reduce energy consumption while maintaining costeffective infrastructure, we propose an energy-saving strategy that optimizes the operation by introducing mobile RSUs (mRSUs) with dynamic and adaptive states called (”ON/OFF”). Furthermore, we introduce a k-Nearest Neighbors (KNN) algorithm, leveraging Machine Learning (ML), to predict the optimal speed of these mRSUs, enhancing their efficiency and adaptability. The effectiveness of these approaches is rigorously evaluated based on the activation time of the mRSU, the total energy consumption metric, and using a meta-analysis method.

In recent years, Internet of Things (IoT) keeps evolving and permitting end users to benefit from its advantages. For example, automation, continuous monitoring and improved productivity. Although this technology facilitates the user’s life, the experience can be ruined due to cyberattacks aiming to interrupt the service and collect unauthorized data. Therefore, we propose a trust-based system to ensure continuous control of the system’s users. Any interaction between the user and the trust system is managed by a permissioned blockchain. A smart contract will be responsible for the trust’s verification, update and retrieve actions. More tests are provided in smart contracts to be launched automatically and preserving subsequently the security of the system. Regarding the trust evaluation, our approach allows the detection of Denial-of-service attempts. Also, the results show that a user’s transactions’ latency increases in parallel with transactions’ number. Additionally, the throughput increases accordingly with the transactions number invoked on blockchain ensuring system’s scalability.

Micromobility vehicles such as electric scooters and bikes represent an emerging technology that offers many benefits. To ensure the efficient integration of these vehicles into Cooperative Intelligent Transport Systems (C-ITS), it is necessary to meet the stringent requirements of V2X communication technologies, which demand ultra-reliable and low-latency to guarantee safety and seamless connectivity. V2X connections can be based on ETSI ITS-G5 or Cellular communication (C-V2X) incorporating fifth generation (5G) network. In this paper, we propose a hybrid communication architecture tailored for micromobility vehicles that leverages the benefit of each Radio Access Technology (RAT). We propose a novel decentralized RAT selection strategy based on Deep Reinforcement Learning (DRL), integrating a Double Deep Q-Network (DDQN) algorithm. The proposed approach enables each micromobility vehicle to dynamically select the optimal RAT combination that maximizes communication reliability and packet delivery ratio (PDR) while optimizing resource usage. Performance evaluations conducted under a real city scenario in Bordeaux, France, with 100 micromobility vehicles demonstrate that the proposed hybrid system achieves up to 99% PDR compared to static RAT selection strategies, which confirms the effectiveness of the DDQN-driven approach in enhancing communication performance for emerging micromobility-based V2X applications.

Micro-mobility, such as electric scooters and bicycles, has set a trend in recent years. This low-speed mode of transport, widespread and getting connected, represents a significant paradigm shift in contemporary transportation dynamics. To integrate these low-speed types of transport within the Intelligent Transport System (ITS), the paper proposes the use of the Vehicular Ad-Hoc Network (VANET) communication model within the Internet of Vehicles (IoV) using the IEEE 802.11p short-range communications standard. We propose a geofencing model that allows an optimal control of micromobility entities speeds in limited geographical areas and we simulate several use cases which can take place within our geofencing model. In addition, an energy saving approach is proposed given the presence of several roadside units (RSUs) in geofencing zones.

The introduction of the coronavirus (COVID-19) has resulted in higher surveillance costs, international hospital overcrowding, and disastrous diseases for the public. In order to treat and diagnose patients, wireless body networks (WBAN) have become essential. In computer-assisted rehabilitation and health surveillance, WBAN offers a wide range of applications, including those pertaining to emergency medical response systems, such as in the present pandemic situation. The patient's physiological characteristics are measured by biomedical sensor nodes, and the results are stored on a system called a local therapy unit. Only authorized users (doctors and other healthcare professionals) can remotely view the data from these patients as long as they are linked to their smart devices. However, in extreme circumstances, abnormal or incorrect measurements caused by damaged or malicious sensor nodes may result in incorrect diagnosis and even patient death. Patient privacy is in danger when these new technologies are used without taking data security into account. Thus, it is crucial for healthcare apps to retain anonymity. In order to resolve these problems, we suggest an effective healthcare system based on distributed agents. This system will ensure the secure collection and transfer of COVID-19 patient data to healthcare experts and will make it easier to identify and diagnose fatal irregularities. Our main contribution is to provide distributed real-time processing, learning, and risk analysis capabilities in order to deliver an intelligent and secure system. Agents are in charge of locating legitimate patient datasets from Tunisian doctors. In order to do this, we calculated risks related to sensors, signals, and the wireless medical network, sent data via Telnet and NetCat, employed ANN as the learning method, and securely sent data using the AES encryption protocol security. Our method exhibited performance values of 99% accuracy, 100% recall, and an F-score of 99.4% with a running time of 1.50 seconds.

The increase in human life expectancy, the COVID-19 pandemic and the rising cost of medical services have created enormous challenges for the governments and the healthcare industry. Unhealthy lifestyle has increased the need for continuous health monitoring and disease diagnosis. In the absence of a precise and effective treatment for COVID-19, providers around the world have made efforts to use drugs commonly used in other cases of indications to save the lives of infected people. A very important discovery was made between the prolongation of the QTc interval (corrected QT) associated with the ECG and mortality in patients with coronavirus. In addition, the QTc prolongation is mainly related to the drug treatments taken during the hospitalization of these COVID-19 patients. It is therefore very important to identify the influence of COVID-19 and medical treatments on the prolongation of the QT interval, which causes cardiovascular disturbances, but there is no identification process that allows us to differentiate patients who have a prolonged QT interval following cardiovascular disease from those who have COVID-19 and receive treatments that influence this interval. In this article, we propose a detection system based on machine learning. Random Forest was used for a multi-class classification. The learning results give an accuracy of 82% and a process execution time of 1.5s. Our simulations confirm that the QTc interval can be an identification tool for COVID-19 and post-COVID-19 patients.

Vehicular cloud computing (VCC) networks provide communication between vehicles (vehicle-to-vehicle) and between communication equipment placed along streets (vehicle-to-infrastructure). Its purpose is to analyze the vehicle environment and make it more comfortable. Additionally, it provides more safety for vehicles and drivers on the road with the exchange of critical information and safety alerts. Given the specific requirements of vehicular networks, it has become a frequent target of dangerous attacks. Indeed, erroneous alert messages, vehicle speed and loss of information due to the wireless connection, are some examples of problems that can affect safety. For this, we propose in this work a reputation management system for securing VCCs, which is used to analyze each vehicle to give it a reputation score. Depending on this score, nodes can accept or refuse communication with neighbors. After evaluation, we notice that the reputation decreases with the increase of the vehicle speed. Moreover, the introduced overhead in terms of delay is equal to 4.2ms, which is interesting, by considering the introduced security features.

The fifth-generation (5G) vehicular networks is enable to disseminate information about road and environmental conditions. Those networks unwittingly generated security and privacy vulnerabilities due to their architecture. As vehicular network implementation becomes more prevalent, many researchers have proposed numerous schemes to address these flaws. Nonetheless, these solutions were unable to address all of the vehicular networks' privacy and security issues. The reason for this manuscript is to give a comprehensive overview of vehicular networks and their components, as well as the numerous sorts of attacks that can be used against them, as well as all requirements interms of privacy and safety for authentication with privacy-preserving schemes for 5G-based car communication. This study takes a full look at present authentication with privacy-preserving schemes and compare them according to all privacy and security criteria, overheads of communication and computation, and the level of resilience to various sorts of attacks. It also offers a critical analysis of previous studies. This manuscript could be applied as a guide and reference for developing new security and privacy solutions for 5G-based vehicle communication.

Nowadays, there are large amount of data generated by the IoT objects. For that, big data and Cloud and Fog Computing technologies have become increasingly important. Unfortunately, these environments still present many difficulties. And according to the statistics, we note that the major percentages of attacks come mainly from end users. Hence, we need an access control system. So, we focus our work on user access control. In fact, our contribution in this paper is mainly focused on securing user access to Fog Computing networks. For this, we propose, an efficient and complete protocol for access control service, which based on the trust assessment and the monitoring of the user's activities. Afterwards, we aim to improve our protocol by integrating the concept of certificates. We have shown through analysis studies and simulation results that the proposed scheme provides both security requirements in terms of confidentiality, integrity, availability, and anonymity; and network performance in terms of access response latency.

The concept of distribution in the Cloud and Fog computing makes the network more vulnerable to malicious activities. This requires use of distributed firewalls to stop incoming intrusions, where the Controllers are used to mitigate rules for the purpose of cooperation based on risk analysis. These security components are used in the architecture of Distributed Cloud / Fog with a zoned topology. We propose in this paper to use this topology that implements an access-control based on a set of cooperation levels between user-FN (FN: Node Fog). In addition, we secure the exchange of cooperation messages by guaranteeing integrity, confidentiality and authentication while avoiding replay attacks. This improves the security of the cloud network. As proof, we simulate Cloud/Fog network using the NeSSi² tool. This simulation obtained results are hopeful in term of delay and transmission rate must be a decrease of 1.5 ms. Furthermore, the protocol used in exchange of cooperation messages, is analyzed using Security Protocol Animator (SPAN) for Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The obtained validation results show that the scheme is safe.

Human Activity Recognition (HAR) is one of the critical subjects of research in health and human machine interaction fields in recent years. Algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Decision Tree (DT) and many other algorithms were previously implemented to serve this common goal but most of the traditional Machine learning proposed solutions were not satisfying in term of accuracy and real time testing process. For that, a human activities analysis and recognition system with an embedded trained ANN model on Raspberry PI for an online testing process is proposed in this work. This paper includes a comparative study between the Artificial Neural Network (ANN) and the Recurrent Neural Network (RNN), using signals produced by the accelerometer and gyroscope, embedded within the BlueNRG-Tile sensor. After evaluate algorithms performance in terms of accuracy and precision which reached an accuracy of 82% for ANN and 99% for RNN, obtained ANN model was implemented in a Raspberry PI for real-time predictions. Results show that the system provides a real-time human activity recognition with an accuracy of 86%.

Since the appearance of the revolutionary field on the domain of the power industry called smart grid, many challenges are proposed. However, the main challenge is how to secure a large amount of data using smart grid. In a smart grid, the power consuming data are collected to optimize energy utilization. However, the collection of electricity usage data may cause the leakage of user privacy-sensitive information that threatens the user's privacy. In fact, we propose, a secure energy prototype for smart grids with data confidentiality, data integrity, intrusion detection and filtering. In fact, in the proposed prototype, we achieve data source authentication, data aggregation, where the data confidentiality is based on the AES-CBC (Advanced Encryption Standard -Cipher Block Chaining) algorithm and the data integrity is based on the SHA (Secure Hash Algorithm) algorithm. The proposed prototype delivers energy related information safely throughout the data transfer in a chain of a smart grid. Therefore, we propose a filtering process to prevent remote attacks. We have noticed from the python implementation results of a prototype that the proposed security mechanisms do not introduce a significant overhead for all the process.

Cloud based Intrusion Detection Systems (IDS) presented an effective way to increase the detection rates of malicious activities in the past couple of years. However, the implementation of these systems using traditional techniques is a herculean task due to the exponential growth of network bandwidth with the very limited availability of computational resources. One topic that intuitively stands out as a potential for solving this issue is Artificial Intelligence (AI). In this paper, we present an overview of recent detection proposals in the field of security intrusion detection using the contributions of new trends in AI (Artificial Intelligence) with an experiment carried out to evaluate the performance of different machine learning techniques using the most used dataset in information security research NSL-KDD and its various components in WEKA. Results show that the best classification algorithm is Random Forest with a Precision of 98.6%.

The annual increase in the number of accidents across the world have demonstrated the urgent need of supporting V2X communications in order to minimize killed and injured risks on roads. V2X is considered as one of the key applications of 5G system where it uses the non-orthogonal multiple access (NOMA) technique as its access technique for its high transmission rates, high throughput, massive connectivity and low latency. In this work we provide a secure resource allocation algorithm to assure allocating vehicles according to several needed criteria to improve the allocation mechanism and to satisfy users need in the network.

The wireless body sensor network (WBSN) is an emerging technology in the healthcare domain, which collects data from vital parameters of the patient's body, using small portable components such as sensors. It allows to continuously monitor patients without restricting their movements and to inform the health specialist of the evolution of their states. However, the deployment of new technologies in health applications without taking into account data security makes the privacy of patients vulnerable. Thus, these systems have insufficient performance for large and varied data sets, because they do not process information sufficiently diverse to cover all scenarios. Moreover, the interpretation of physiological signals is a tedious process in which human errors can be caused.To address these issues, we propose, in this paper, an effective health system that ensures the secure collection and transfer of patient data to the physician, to facilitate the diagnosis and the detection of life threatening diseases. Our main contribution is to offer real-time, high quality processing, learning and analysis capabilities, in a smart and secure system. For that, we have collected real patient datasets from Tunisian doctors.To achieve this goal, we used ANN and XgBoost as learning algorithms and AES as an encryption protocol for sending secure data to medical personnel for diagnostic purposes. The results show an accuracy of 80% with an execution time of 1.54 s.

Recently the concept of Intelligent Transportation Systems (ITS) has been of great interest to the vehicular networks known as VANETs. The main objective of these networks is to reduce accidents in the road. However, major challenges need to be addressed to provide secure and reliable road communication in anonymous and sometimes hostile environments. As in any communication system the vehicular networks must operate in compliance with constraints in terms of quality of service. These constraints are all the more stringent when it comes to providing safety services on the road. In this article, we propose a new Priority-based Safety Management and Slot Reservation for Authenticated Vehicle (PSMAV) to study the categories of vehicles and applications in VANET. The idea of the PSMAV is to give priority to emergency messages and uses our previous MAVnet protocol to solve the security problem. The analyzes and simulations show that our protocol ensures the security requirements for safety applications in VANETs.

Lately, there has been a lot of interest in how to ensure secure communication in Wireless Body Sensor Networks (WBSN). To accomplish this finish, applied researchers have become increasingly identified their own detection techniques. However, WBSN still vulnerable for many attacks, for instance: Jamming, Sybil attacks, Sinkhole, Denial of service (DOS), etc. Therefore, intrusion detection is officially compulsory process that helps physicians to get accurate medical decisions. The present work proposes an intrusion cancellation to add efficiency to medical anomaly detection based on Electrocardiogram (ECG) and Electromyogram (EMG) analysis. The efficiency and accuracy of the proposed approach are evaluated using the Matlab tool. Hence, our scheme may be considered as a promising accurate detection of false alert rates.

The rapid evolution of the wireless communication technologies along with the growing number of connected vehicles and the limitation of available radio resources make scheduling tasks very important for LTE-V2X vehicular networks. Accordingly, this paper presents a new optimal scheduling algorithm for V2X connections called DSA-PA, which improves performances in terms of Quality of Service (QoS), throughput and fairness. The proposed algorithm consider the traffic classification imposed by V2X exigency and aims to maximize the system throughput by taking into account the channel conditions of Vehicular User Equipment (VUE) expressed by the Signal-to-Interference-plus-Noise Ratio (SINR) value. The optimal distribution of the available Resources Blocks (RBs) is based on a dynamic reallocation process depending in the average blocking rate value (ABR) especially for vehicular Safety Traffic (ST).

In recent years, the work environments such as the cloud and Internet of Things (IoT) have become more sophisticated, and for proof, the access control network has become very important. To adapt to changing environments, in cloud computing, access network verification is an important requirement for researchers to integrate traditional verification using virtual firewalls.

With the emergence of Internet of Things (IoT) and the growth of cloud computing technology, Electronic Health systems are integrated as a significant and active domain, which enables the development of medical practices with an affordable cost. Thus, the questions about preserving the security and privacy of the sensitive user's information are raised. Hence, in order to convince users to move their sensitive health records to cloud networks, a strong and secure access control schemes should be employed to protect their data. In fact, access control schemes play a key role in the protection of e-Health records, by which several policies will coexist to grant security and privacy in a network. That is why, one of the essential targets in this work is to retain a secure access to health cloud services. Thus, this paper offers a certificate-based access control strategy, where we introduce an access method for e-health cloud that is mainly based on trust assessment. The main features of the proposed scheme are the integration of trust concept with the monitoring process in order to ensure more security in access control. We more explained the methodology of the proposed approach through appropriate evaluation results, which improves system security and performance by minimizing the time spent to have permissions to access services and optimizing the overall system resource utilization.

Mobile Cloud Computing (MCC) is a technique in which mobile users are able to use the cloud services that enable cost reduction, flexibility and on demand, scalability. In fact, MCC enables to share the hardware, data and storage. However, access network verification is an important requirement; the traditional cloud computing architecture has specific security requirements that are not captured by traditional network access control models, e.g. Virtual firewalls (VF). The latter uses an effective static verification method that is not adapted to the MCC, where existing threats increase because of the combination of different types of networks (Mobile and Internet). Thus, distributing firewalls is introduced by researchers to prevent possible attacks and secure complex networks like MCC. However, it is difficult to design, manage and cooperate firewall policies. Thence, we propose in this paper an architecture for MCC motivating the use of distributed firewalls/Controllers, where two levels of firewall cooperation are proposed: horizontal cooperation in the cloud and vertical cooperation between the mobile and the cloud. This architecture is in charge of administering and updating rules between distributed firewalls and their neighbors via the Controller component. For validation, we use a cloud implementation based on the Openstack platform. The retrieved delay is promising with regard to the traffic burden.

Vehicular Ad Hoc Networks (VANETs) offer many interesting services such as infotainment applications and safety information to vehicles. In order to protect these networks, we should ensure the confidentiality, integrity and authentication for V2I (Vehicles to Infrastructure) communications and V2V (Vehicle to Vehicle) communications. Nevertheless, in VANET environments, mobility of the vehicles is very high, so that a sound security can increase the Quality of Service (QoS) of the protocol. "The Dual Authentication and Key Management Techniques" is a solution that is created to authenticate and secure VANET communication. However, it suffers from some lack of security aspects. In this paper, we devise an improvement to this scheme in order to get a deliberate compromise between security and quality of service.

Cloud computing is a distributed and dynamic computing model that still has many difficulties. Intrusion prevention based on access control is an essential research challenge to better ensure the security of the cloud network. The flexibility and the scalability of the application of access control are the essential issues in a cloud environment. This paper proposes the use of risk and role based dynamic access control for preventing intrusions targeting the cloud computing, where the main enhancement of the proposed procedure is that access decision is mainly based on the resources available in the cloud services providers. The proposed scheme get the access decision based on a mixture of eXtensible Access Control Markup Language (XACML) decisions, risk analysis, and vulnerabilities assessment. Experimental results based on a comparison with related work are also presented to show the efficiency of the proposed approach.

Cloud Computing is a new promoted technology today. The adoption of the cloud computing has been increased for both enterprises and end-users. But, the major critical problem of this technology is its security that is becoming more and more serious. Thus, intrusion detection has been considered the best solution to secure the Cloud platform. Therefore, the main aim of all new researches is to retrieve an effective and performing intrusion detection system adaptable to cloud environment. In this paper, we propose a new Intrusion Detection System (IDS) for the -+8 rates and detection delay in order to make real time detection in the whole of Cloud platform based on the OpenStack framework. The results show that the C-DIDS is more efficient in term of delay compared to the existing GCCIDS solution.

The distribution's feature of cloud computing and the increasing threat of attacks and malicious activities make necessary the use of distributed firewalls to control access to networks. Network-based access management and control is an essential research challenge to enhance the security of the cloud network. We propose, in this paper the use of risk-based access decisions in a distributed and cooperative firewall topology. The proposed model is based on a combination of eXtensible Access Control Markup Language (XACML) decisions and risk-based network access control in the cloud. For proof, we simulate cloud network using the NeSSi2tool. The obtained results are promising in term of delay and blocking rate.

Due to the real-time requirement of message in Vehicular Ad hoc NETworks (VANET), it is necessary to authenticate vehicles to achieve security, efficiency, and conditional privacy-preserving. Privacy is of utmost relevance in VANETs. For this reason, we have proposed a new protocol called ”Multilevel Authentication Protocol” (MAP) that considers different vehicle categories. The proposed protocol is based on our Multilevel Authentication protocol for Vehicular networks (MAVnet). However, the MAP leads to human safety, where the priority is given to the ambulance vehicles. For evaluation, we use the Java language to develop a demo application and deployed it on the Network Security Simulation (Nessi2). Compared with an existing authentication protocol, MAP markedly decreases the delay of exchanging messages while preserving conditional privacy.

In recent years, medical wireless sensor networks suffer from several anomalies and defaults, which can cause the malfunctioning of the whole network, many anomaly detection mechanisms, have been proposed. Therefore, the anomaly detection process is a required function in the medical environment to help doctors in making appropriate and accurate decisions. We propose in this paper to enhance the anomaly detection algorithm by adding a correlation function using data from different body sensor types. For evaluation, we use the Matlab tool, which attests that the algorithm adds efficiency and accuracy to the anomaly detection process between analyzed medical signals.

Security and privacy are both involved in vehicular communications for successful adoption and deployment of the VANET. Generally, attacks cause anomalies to the network functionality. A secure Vehicular Ad Hoc Networks (VANET) system, while exchanging information should protect the system against unauthorized message injection, message alteration, eavesdropping. In this paper, we propose, an enhanced authentication protocol called MAVnet for Multilevel authentication protocol. The proposed protocol considers the different vehicle categories and guarantees authentication, where a third party is used, which is the Authentication, Authorization and Accounting (AAA) server. The MAVnet protocol is then carried out for tracking services by managing information using the DBMS (Database Management System). The main purpose of our work is to ensure the vehicle authentication, while respecting the constraints of vehicle categories by minimizing thing overhead of communications. Two ways of simulation are performed. The first one uses AVISPA framework, where simulation results of the protocol demonstrate that it is SAFE (according to AVISPA goals). And, the second simulation uses the Java Cryptography Extension (JCE) library. The simulation results show that the privacy can be maintained better by applying the proposed architecture and it's able to lessen the number of exchanging messages and time lag. The results show the superiority of MAVnet is significantly higher than that of the certificate-based authentication protocol and the identity-based authentication protocol.

VANETs (Vehicular ad hoc networks) are an important communication to offer safety information and other infotainment applications to vehicles. To secure these networks, it is important to ensure the authentication and confidentiality of communicationV2V (vehicle to vehicle) and V2I (Vehicles to infrastructure). However, in VANET environments, vehicles are moving with a very high speed on the roads, so a strong security can degrade the performance of the protocol. Among the research that presents some schemes to secure VANET communications, we mention the Dual Authentication and Key Management Techniques that suffers from several lack of security. In this paper, we present an enhancement to this solution to obtain a deliberate compromise between quality of service and security. Security analysis proves that our scheme is secure and the result of performance validation is promising.

We propose in this work an error detection process for wireless networks, applied to a previously published transmitter/Receiver system model. This model is based on a bit interleaved coded modulation (BICM) scheme over a frequency selective channel. The detection process is able to discern the attacked block: encoder, modulator or channel. We prove using simulations that the deployed intrusion detection system (IDS) is competitive by comparing it to existing intrusion detection systems.

This paper proposes to manage data structures manipulated by the proposed correlation function based on a new concept called multi-violation detectors (MvD). The MvD-based correlation function manipulates events based on metrics that evaluate them. The detection and the correlation processes are managed using a MLP (multi-layer perceptron)-like network architecture. The manipulated data structures in the MLP-like network mainly include attacks, metrics and MvD values. To add more efficiency to the implemented processes, these values are managed through a structured database where an SQL-like language for MvD retrieval is introduced. The proposed approach is based on a relational calculus. Therefore, its completeness is mathematically proved.

This paper presents an approach for wireless intrusion detection based on signal analysis with wavelet transform. The main function of this approach is performed by an entity called radio supervisor, which can be implemented in any node of the wireless network. Radio supervisor is also able to detect distributed attacks by cooperating with other radio supervisors via a signal-based correlation scheme that we provide for the circumstance. Finally, functions and features of the proposed approach are studied through simulations.