Unmanned Aerial Vehicles (UAVs) are at the center of a number of mission-critical applications, however, the largescale deployment of these vehicles is accompanied by cybersecurity risks that can compromise the operational safety and data integrity. A machine learning-based network intrusion detection system implemented on UAV-GCS communications along with the UAV Network Intrusion Detection Dataset (UAV-NIDD) is what we are suggesting. Our method treats the class imbalance problem with the CRISP-DM approach, which utilizes such advanced resampling methods as Borderline-SMOTE. The ensemble voting classifier of ours combines Random Forest, Decision Tree, and XGBoost models to reach the highest possible level of detection performance. We base our approach on the UAV-NIDD Scenario 1 experimental evaluation, where our method achieves a macroaveraged F1-score of 0.95 on a $20 \%$ hold-out test set for 11 types of UAV network intrusion as the result. This research marks the first comprehensive machine learning study that used the UAVNIDD dataset, showing the value of the systematic data science methodologies in solving cybersecurity problems and putting the spotlight on the use of UAV-specific datasets as a source of security solutions.

ABSTRACT Smart contracts are self‐executing programs that operate on a blockchain network. They are designed to automate transaction execution without the need for intermediaries. Once deployed in the blockchain network, smart contracts cannot be altered. However, this immutability can also lead to security risks. If a smart contract contains a vulnerability upon deployment, it cannot be corrected, leaving the code vulnerable. Therefore, incorporating security considerations during the design phase of smart contract development is crucial. Access control is a key security concept that must be integrated into the design of smart contracts to prevent unauthorized access to critical functions or data. In this paper, we introduce a Model‐Driven Architecture (MDA) approach to design access control for smart contracts, and we validate, using Ethereum, the proposed approach using Smart Contract Security Verification Standard (SCSVS) rules.

Unmanned Aerial Vehicles (UAVs) industry has experienced rapid growth and widespread adoption across various sectors. This increased affordability of UAVs has led to their extensive use, making them valuable assets for critical missions. However, the growing reliance on UAVs has also exposed them to various attacks. Multiple researchers have proposed frameworks based on different technologies to address these issues. Among these, AI-based frameworks and intrusion detection systems have emerged as prominent solutions for detecting UAV attacks. Due to the existing vulnerabilities, it is crucial to implement additional security measures to protect UAV processes. In this regard, a responsive Security Operations Center (SOC) offers a suitable solution to bridge this gap. This paper explores the need for a SOC specifically tailored for UAVs, focusing on cyber awareness and control measures. We emphasize the importance of control mechanisms within a SOC to ensure the protection of UAVs. We propose a responsive security solution for UAVs by integrating a SOC within an AI-based framework. The detection and mitigations expected through combining SOC in an AI-based IDS are promising. They indicate the effectiveness of the chosen techniques, which guarantees UAVs security.

Industry 4.0 is going through a transitional period via the radically automotive transformations. In particular, unmanned aerial vehicles have significantly contributed to the development of intelligent and connected transportation systems. Thus, the continuous development using diverse technologies to achieve a variety of high-performance services raised the security concerns regarding communicating entities. Thus, being managed by networked controllers, UAVs uses controller area networks (CAN) protocol to broadcast information in a bus. However, this protocol is used as a de facto standard which does not have sufficient security features that raise the security risks. This issue caught the attention of the automotive industry researchers and several studies have attempted to improve the security of the CAN protocol attack detection. However, the proposed studies established general perspective solution and did not pay attention to UAVCAN attack detection. To alleviate these concerns, this paper proposed a dynamic intrusion detection frameworks (DIDF) for UAVCAN. The proposed UAVCAN DIDF scheme adopts an artificial intelligence (AI) based model to achieve high detection performance. We performed experiments using public UAVCAN dataset to evaluate our detection system. The experimental results demonstrate that UAVCAN DIDF has significantly reached a high detection rate with a high true positive and a low false negative rate. The simulation results are encouraging and demonstrate the effectiveness of UAVCAN DIDF.

Blockchain is emerging as one of the most promising and resourceful security technologies for cloud infrastructures. In a distributed database system, blockchain is used to store, read, and validate transactions. It can improve security, trustworthiness, and privacy by using an unchallengeable, shared distributed ledger on cloud nodes. Cloud-based healthcare systems (CHS) are vulnerable to various threats and attacks such as identity theft, medical fraud, insurance fraud, and alteration of critical patient data. Secure retrieval, access, and storage of data on CHS are necessary to protect critical medical data. Accordingly, the integrated cloud and BlockChain (ICBC) architecture emerge as a potential solution for shaping the next era of a healthcare system while providing efficient, secure, and effective patient care. In this context, this paper presents an in-depth exploration of advanced approaches to securing cloud-based healthcare data management systems using blockchain technologies. It provides a taxonomy and highlights the benefits and limitations of the approaches examined.

With the fast expansion of the internet of vehicles (IoV) and the emergence of new types of threats, the traditional machine learning-based intrusion detection systems must be updated to meet the security requirements of the current environment. Recently, deep learning has shown exceptional performance in IoV intrusion detection. However, deep learning-based intrusion detection system (DL-IDS) models are more fixated and dependent on the training dataset. In addition, the behavior changes with the occurrence of attacks. They pose a real problem for the DL-IDS and make their detection more complicate. In this paper, we present a deep transfer learning based intrusion detection in-vehicle (TRLID) model for IoV using the CAN bus protocol. In our proposed model, a data preparation approach is proposed to clean up bus data and convert it to an image for usage as input to the deep learning model. Indeed, we used transfer learning characteristics because they enable us to transfer the source task's knowledge to the target task. Therefore, we trained our model using different dataset including different attacks. The experimental results show that our proposed TRLID achieved good results where the intelligence integration of transfer learning was efficient for attacks detection.

The Internet of Things (IoT) enables the interconnection of devices that collect massive amounts of data, making IoT security requirements crucial and secured through encryption. However, traditional encryption protocols are no longer suitable for all IoT scenarios. We propose a new lightweight encryption method optimized for patient information protection in healthcare, considering the limited capacity of portable medical devices. Our method has been experimentally demonstrated to be effective, with a largest entropy of 7.9917 in medical images (computed tomography) and average coding and decoding times of 3.8952 sec and 3.0584 sec, respectively. The encoded image exhibits an even distribution of pixels and lower correlation coefficients between neighboring pixels, supporting the effectiveness of our less complex method compared to current state-of-the-art methods.

A resilient and guaranteed Unmanned Aerial Vehicles (UAVs) security framework should be designed to be secure against different types of attacks and faults. Recent developments have seen a proliferation of methods for improving UAVs security. Although, many studies proposed different approaches using artificial intelligence to enhance their security. Unfortunately, no study yet worked on an examination of an hybrid framework on UAVs faults and attacks and different architectures. Hence, our article aims to provide a prior detection results by proposing an hybrid adaptive framework for faults and attacks detection for UAVs applied on centralized and decentralized architectures. Our framework is based on two entry flows for faults and attacks in order to learn high-level features automatically from data. We validated our framework using deep-learning architectures. Finally, the empirical results show that our framework reached over 85% and 96,7% in term of accuracy for UAVs faults and attacks, respectively.

The Internet of Vehicles (IoV) is an Internet of Things (IoT) application that offers several utilities such as traffic analysis, safe driving, road optimization, and travel comfort. Software-Defined Networking (SDN) technology has been shown to provide various benefits to support the IoV. However, the construction of IoV makes it a complex system posing several challenges among which the important ones are security and privacy of data. Intrusion Detection Systems (IDSs) have been proposed in the IoV to identify cyber attacks and protect private data. Recently work has started to implement IDSs based on Federated learning as collaborative IDSs have proved effective security of IoV. In another hand, trust management has revolutionized the IoV filed, providing decision-making support to secure the network. Stating that an SDN-driven IoV architecture in which nodes trustworthiness gets assessed can provide a promising framework for IDS, we propose in this paper a Federated learning-based IDS for the IoV under the SDN structure. We integrate trust metrics to assist in securing the IoV network. Simulation experiments are conducted to validate the proposal.

The advent in Wireless Body Area Networks (WBANs) provides promising services for remote diagnostic and monitoring. The security requirements in this context are challenging and evolving. The WBAN should support these security requirements to continuously provide efficient services to patients. Trust management is one of the interesting security mechanisms that can be enforced to enhance the WBAN security. In this paper, we propose a new cluster-based approach to manage trust in WBAN. The clusters are defined based on the direct and indirect exchanges between nodes. Our approach is hybrid: it calculates the direct and the indirect trust based on the priority of forwarded messages, the feedbacks of different exchanges, and the credibility coefficient. The simulation results show the efficiency of our model against three different attacks: the on-off attack, the collusion attack, and the newcomer attack.

In several countries, the connected systems and especially the Internet of Medical Things (IoMT) based systems has been deployed with other advanced technologies to counter and to mitigate the spread of the coronavirus disease 2019 known as (COVID-19) and to smooth the severity of the pandemic. Accordingly, many IoMT-based systems merged with the use of blockchain, artificial intelligence and big data analytics proposed and adopted by diverse countries and governments to counter COVID-19 pandemic. In this context, this paper highlights the needs, the architecture, the design guidelines and the provided services related to our proposed system named (CMK-COVID: Connected Medical Kiosks to Counter COVID-19). Besides that, this paper offers useful insights into the literature related IoMT-based solutions countering COVID-19.

Recently, the Vehicular Ad-hoc network (VANET) has progressively gained attention from both industry and research with the rapid development of wireless communication technology and intelligent vehicles. The vehicles exchange messages with other entities but cannot share the resources between them. Therefore, security is required in some scenarios including integrity, traceability, confidentiality, notarization of exchanged information as well as access control. In order to provide a secure vehicle communication and access control, we propose an ABAC access control model using smart contract on the blockchain. The use of the blockchain facilitates the sharing of secure messages among vehicles. Moreover, vehicles are able to share the resources with each other exploiting the access control policy on the XACML standard. Then, we evaluate the access response time and the storage overhead of the proposal.

Access control in Blockchain context is a crucial step. During this process it is important to verify that users have the right roles to get access to the protected resources. To model its access control, the Ethereum Blockchain is based on RBAC model. The whole process is written in a smart Contract imported from The OpenZepplin Library [1]. However, the automatic generation of the access control policy still missing. This automation can widely enhance and simplify the implementation of access control for developers of blockchain based applications. In this paper, we propose a new model for automatically generating the access control smart contract. Our approach is based on the Model Driven Engineering (MDE).

In this paper, we investigate the concept of reputation to improve the resistance of vehicular networks against malicious and misbehaving vehicles. We propose a robust reputation management system, which consists of a model for reputation calculation and a credibility model to enhance network efficiency. The reputation score or value reflects the behavior of a vehicle towards other vehicles and network services (selfish, cooperative, malicious, misbehaving, etc.); while the credibility of vehicles is used to determine whether a reputation score given by a vehicle is correct to deal with malicious vehicles that use reputation calculation to spoil the network operation. We first describe how the reputation score of each vehicle is determined. Then, we introduce a non-cooperative game, where each vehicle aims to maximize its credibility. The effectiveness of the proposed model is demonstrated through extensive simulations.

Nowadays, the vehicular ad hoc networks (VANETs) received attention. However, due to the dynamic characteristics of VANETs, its network transmission efficiency is low. To resolve this issue, many investigations and contributions suggest the integration of Information Centric Networks (ICN) into VANETs. In this paper, we propose a new approach named VC-ICN integrating a clustering protocol and ICN into vehicular network. The proposed approach allows vehicles to enhance their content delivery ratio. The simulation results for real context prove the accomplishment of VC-ICN in information-centric vehicular network context.

Medical institutions are increasingly adopting IoT platforms to share data, communicate rapidly and improve healthcare treatment abilities. However, this trend is also raising the risk of potential data manipulation attacks. In decentralized networks, defense mechanisms against external entities have been widely enabled while protection against insider attackers is still the weakest link of the chain. Most of the platforms are based on the assumption that all the insider nodes are trustworthy. However, these nodes are exploiting of this assumption to lead manipulation attacks and violate data integrity and reliability without being detected. To address this problem, we propose a secure decentralized management system able to detect insider malicious nodes. Our proposal is based on a three layer architecture: storage layer, blockchain based network layer and IoT devices layer. In this paper, we mainly focus on the network layer where we propose to integrate a decentralized trust based authorization module. This latter allows updating dynamically the nodes access rights by observing and evaluating their behavior. To this aim, we combine probabilistic modelling and stochastic modelling to classify and predict the nodes behavior. Conducted performance evaluation and security analysis show that our proposition provides efficient detection of malicious nodes compared to other trust based management approaches.

MANETs (Mobile Ad hoc Networks) are described assets of mobile nodes connected with wireless links. To be efficient, routing protocols in MANETs should, in fact, manage mobility, handle nodes energy dissipation and ensure security. We argue in this paper that trust negotiation is appropriate in such context to enhance the network performances. Trust concept is of concern to communication and network protocol designers. Thus, building trust relationships among participating nodes is critical to enabling collaborative optimization of system metrics. The main contribution of this paper is an extension of our previous proposition DTMCA (Delegation Trust Mobility-based Clustering Approach) which defines a new clustering approach, a trust management process and a delegation process. This environment allows the localization and the isolation of malicious nodes in MANETs. The extension proposed in this paper extends the trust management process by adding a trust negotiation module used in order to minimize the risk that malicious nodes join the MANETs.

We argue in this paper that while trust management through negotiation performed in IT system might seem appropriate at first sight, it has to be extended to address specific concerns to interoperable environments like the case in Intelligent Transportation Systems - ITS environment. In this context, negotiation does not only depend on trust requirements, but is strongly linked to the security policies as well as to the privacy related attributes shared among entities during communication. This paper discusses what needs to be negotiated in ITS environment and how we can model these negotiation rules in security policies. Besides, it proposes an approach to use the XeNA negotiation framework [1] to negotiate these different types of resources.

Workflow management systems (WFMS) are concerned with the control and coordination of operational business processes, called workflows. When workflow technology is deployed in domains where processes have simple coordinative requirements, the flow of control and data may be easily mapped onto process effectiveness. With the diversity of resources, subjects and activities in the system, ensuring a secure execution environment of the workflow becomes a critical issue. In this paper, we are interested in deploying a WFMS security policy. We investigate this issue either within intra or inter organizational workflows. The later case is more complex and requires more sophisticated control since it includes communications between different organizations.

Workflows are operational business processes. Workfow Management Systems (WFMS) are concerned with the control and coordination of these workflows. In recent years, there has been a trend to integrate WFMS in distributed inter-organizational systems. In this case malfunctioning of one WFMS can affect more than one organization, making the correct functioning of a WFMS a critical issue. Thus, an important function of WFMS is to enforce the security of these inter-organizational workflows. Several works have been done to integrate the security aspects in the workflow specification. Unfortunately, these research works generally adopt a centralized management approach and are based on static access control models. Therefore, they do not deal with flow control, a very important requirement in WFMS. In this paper, we suggest a decentralized and dynamic approach to handle a security policy in workflows taking into account access and flow control.

The use of ROHC (robust header compression) for multimedia applications as VoIP over 3G radio links has shown a good performance in reducing the overhead of RTP encapsulation. Some efforts have been made to use ROHC over IEEE 802 links, but the solution needs to be optimized. The use of ROHC out of its operating assumptions adds some additional problems. These problems occur when the padding is used within small packets. This paper introduces an optimized solution to send ROHC packets over IEEE 802 links