The Internet of Vehicles (IoV) represents a transformative advancement in intelligent transportation systems, enabling multi-node networks to exchange critical information in an open, wireless environment. However, the proliferation of IoV and Vehicle-to-Everything (V2X) communication systems introduces significant cybersecurity challenges across all the network layers. These issues, if exploited, can compromise the vehicle safety, the user privacy, and the integrity of transportation networks. This paper provides a comprehensive analysis of multi-layer IoV security, it addresses vulnerabilities in weak nodes and proposes robust mitigation strategies to safeguard intelligent transportation infrastructures.

As smart cities evolve, they integrate various applications such as intelligent transportation systems, energy management, healthcare, and public safety, all of which depend on interconnected networks. These applications rely on massive data exchanges between sensors, devices, and cloud services, making the system more efficient but also exposing it to cybersecurity challenges. Cyber threats, including data breaches, denial of service (DoS) attacks, and malware, can disrupt essential services, compromise privacy, and endanger lives. The complexity of smart city infrastructure amplifies vulnerabilities, making real-time detection and localization of attacks a critical necessity. In this paper, we propose a novel framework for attack detection and localization specifically designed for smart city environments. The framework integrates machine learning-based intrusion detection systems (IDS) with packet analysis techniques. Upon detection of an anomaly, detailed packet analysis is performed to extract crucial information, such as IP addresses, GPS coordinates, and other metadata. This enables precise localization of the attack's source, facilitating rapid response and mitigation. The combination of machine learning for anomaly detection with packet-level analysis ensures a comprehensive approach, significantly improving detection accuracy and localization precision. Extensive evaluations on real-world datasets demonstrate the efficacy of the proposed method in enhancing the security of smart city networks, while reducing false positives and improving real-time response capabilities. This framework represents a critical advancement in protecting smart cities from evolving cyber threats.

In order to improve the quality of human existence, comfort and efficiency are key objectives in smart environments. It is now possible to construct smart cities due to the latest advancements in Internet of Things (IoT) technology. Privacy and security are major concerns in IoT-based smart objects. Smart environments are at risk for safety from IoT-based technologies. Intrusion detection systems (IDSs) created for IoT environments are essential for preventing IoT-related security threats. Many cyber security systems use IDSs to find intrusions. Anomaly-based IDS learns the typical pattern of system activity and alerts on anomalous events as they happen as opposed to analyzing monitored events against a database of known intrusion events, as is the case with signature-based IDS. The installation of IDS on the IoT network is the main topic of this paper. Key design approach presented in this paper must be taken into consideration when developing an intrusion detection system for the Internet of Things. In this study, we use the Convolutional Neural Network (CNN) to identify attacks on nine commercial IoT devices. Using an actual N-BaIoT dataset that was taken from a real system and included both benign and harmful patterns, extensive empirical research was conducted. The testing results demonstrated a good accuracy of the CNN model in identifying botnet assaults from security cameras with accuracies of 90.25% and 91.76%. Overall, the CNN model was effective in accurately identifying botnet attacks from a variety of IoT devices.

Internet of Medical Things (IoMT) is gaining interest as an emerging paradigm for healthcare improvement. Cyber-security is one of the major issues breaking down its expansion. Indeed, IoMT ecosystem complexities and cyber-attacks development require thinking about smart and efficient security solutions. Machine Learning (ML) techniques are widely used to help detecting abnormalities and intrusions in such environments in order to improve trustworthiness in Connected Medical Devices (CMD). Towards this direction, risk assessment is also proposed to proactively evaluate the security of such platforms. Regarding the complexity and heterogeneity of IoMT, dealing with the inherent security risks is a challenging task. In this context, we aim to evaluate the cumulative risk of CMD based on anomaly detection in IoMT traffic via ML algorithms. Our model relies on anomalies detection coupled with intrinsic risk assessment of medical devices trying to have a holistic risk evaluation for the platform.

Emerging networks envisage to establish a modern digital society that tends to be more valuable on both social and economic levels. The objective is to resolve current network challenges and offer adequate security measures. As a consequence, adaptive architecture is necessary for upcoming networks. An emerging paradigm that can overcome the limitations of conventional networks is software-defined network (SDN), especially when coupled with Network Function Virtualization (NFV). It offers the capacity to dynamically manage and control the entire network by decoupling the control plane from the data plane. Nevertheless, various new network security issues must be handled. More opportunities to deliver intelligence inside of networks are given by SDN. This is why, thanks to SDN’s characteristics, the use of machine learning methods is easily implemented. In this study, we introduce different existing network intrusion detection data sets, with a strong attention to SDN specific new dataset. Furthermore, we suggest an intelligent way to identify intrusions within SDN/NVF networks using a publicly available new SDN datasets (SDN Intrusion) and several Machine Learning techniques. Finally, we present and discuss our obtained results.

The Internet of Things (IoT), which requires architectures with scalable, trustworthy, and well-configured solutions, is evolving toward a multi-tenant and multi-application state. Low-power wireless technologies are a key component of IoT. However, using a software-based centralized architecture in the context of a low-power wireless IoT network poses significant difficulties, including the inability to control traffic, unreliable links, network contention, and high associated overheads that may materially interfere with network performance.To overcome security issues in Software Defined Wireless Networks (SDWN), enterprises and scientists have to develop new techniques and methods for recognizing corrupted entities and threats. In this article, we present a method for identifying and fixing security issues in SDWN that relies on machine learning algorithms using WSN-DS (Wireless Sensor Networks dataset). The main finding of this research is the suggestion of a comprehensive and smart approach to rapidly detect and resolve various wireless network security problems.

Smart cities are made up of various components that are interconnected. These components exchange data on an ongoing basis and they facilitate the lives of citizens. Its use of Information and Communication Technology (ICT) was a key factor in its sustainable development. Nonetheless, this development contributed to a rise of safety threats, criminal use of information and several other security and privacy challenges. As a result, security and privacy concerns have emerged as a significant problem for smart cities. Safety factors for smart cities have become a concern for all those involved in this field. In this study, we deeply examine and review and the concept of smart cities and the challenges it faces at first. In a second phase, we mainly address the research gap of security in smart cities and present an analysis of associated security challenges. In the last section, we introduce our approach that aims to: (i) capture the processes of penetration attempts, alterations and cyber attacks; (ii) truck malicious behaviors and locate their sources; and (iii) finally setup controls to repel and prevent them. To illustrate the applicability and efficiency of our solution, We refer to a case of study to demonstrate the efficacy of our detection method, using different machine learning algorithms and the dataset CICIDS2017.

One of the most challenging issues facing Internet of Medical Things (IoMT) cyber defense is the complexity of their ecosystem coupled with the development of cyber-attacks. Medical equipments lack built-in security and are increasingly becoming connected. Moving beyond traditional security solutions becomes a necessity to protect patients and organizations. In order to effectively deal with the security risks of networked medical devices in such a complex and heterogeneous system, we need to measure security risks and prioritize mitigation actions. In this context, we propose a Fuzzy AHP-based method to assess security attributes of connected medical devices and compare different device models against a selected profile with regards to the user requirements. The proposal aims to empower user security awareness to make well-educated decisions.

The dynamic state of networks presents a challenge for the deployment of distributed applications and protocols. Ad-hoc schedules in the updating phase might lead to a lot of ambiguity and issues. By separating the control and data planes and centralizing control, Software Defined Networking (SDN) offers novel opportunities and remedies for these issues. However, software-based centralized architecture for distributed environments introduces significant challenges. Security is a main and crucial issue in SDN. This paper presents a deep study of the state-of-the-art of security challenges and solutions for the SDN paradigm. The conducted study helped us to propose a dynamic approach to efficiently detect different security violations and incidents caused by network updates including forwarding loop, forwarding black hole, link congestion, network policy violation, etc. Our solution relies on an intelligent approach based on the use of Machine Learning and Artificial Intelligence Algorithms.

The Internet of Medical Things (IoMT) is creating all sorts of new applications and capabilities for healthcare services and transforming medical care in lasting and impactful ways. Jointly, new security and cyber-security risks are arisen. Nevertheless, traditional risk management frameworks cannot be directly applied to the IoMT context. In fact, IoMT devices naturally favor usability instead of security and are contained in a distributed and mistrustful environment. The main goal of this paper is to introduce and technically detail an adaptive risk management model for IoT-based medical systems. The proposal performs risk quantification in different layers. To do so, a deep analysis of IoMT security risks is conducted and an agent-based risk management model is then explained.

Software Defined Networking (SDN), as an emerging paradigm, offers a centralized control platform by disassociating the forwarding process of network packets (data plane) from the routing process (control plane). However, the distributed state of the Openflow rules across various flow tables and the involvement of multiple independent rules writers may lead to problems of inconsistencies and conflicts within configurations at the infrastructure level. To tackle these issues, we propose, in this paper, an offline approach to fix violations at data plane side and a fine-grained control of SDN switches flow tables. Our solution considers Flow entries Decision Diagram (FeDD) as data structure and relies on formal techniques for analyzing the policy defects and resolving misconfigurations. It allows ensuring that the operator's policies are correctly applied in an optimal way. The implemented prototype, on top of OpendayLight, of our solution and experimentations, based on a real network configurations topology, demonstrate the scalability and applicability of our approach.

Access control is a main security measure for the prevention of loss, disclosure or degradation of sensitive information in business. As such, it has become a source of inspiration for many researchers who have undertaken to conduct studies related to that subject. More specifically, risk management in access control is a topic that captivates information and communication technology scientists. Several approaches are defined in literature that can be classified into two main trends: some researchers discuss risk management based on user access, while some other consider policies expression when assessing the risk. In this paper, we study the thematic of enforcing risk awareness/management in access control systems. We review, classify and present a comprehensive synthesis of scientific articles that deal specifically with risk management in access control. We mainly discuss risk management approaches that deal with access control policy expressions and conformity.

Nowadays, the domain of Information System (IS) security is closely related to that of Risk Management (RM). As an immediate consequence, talking about and tackling the security of IS imply the implementation of a set of mechanisms that aim to reduce or eliminate the risk of IS degradations. Also, the high cadence of IS evolution requires careful consideration of corresponding measures to prevent or mitigate security risks that may cause the degradation of these systems. From this perspective, an access control service is subjected to a number of rules established to ensure the integrity and confidentiality of the handled data. During their lifecycle, the use or manipulation of Access Control Policies (ACP) is accompanied with several defects that are made intentionally or not. For many years, these defects have been the subject of numerous studies either for their detection or for the analysis of the risks incurred by IS to their recurrence and complexity. In our research works, we focus on the analysis and risk assessment of noncompliance anomalies in concrete instances of access control policies. We complete our analysis by studying and assessing the risks associated with the correlation that may exist between different anomalies. Indeed, taking into account possible correlations can make a significant contribution to the reliability of IS. Identifying correlation links between anomalies in concrete instances of ACP contributes in discovering or detecting new scenarios of alterations and attacks. Therefore, once done, this study mainly contributes in the improvement of our risk assessment model.

Firewalls as an approved and highly deployed security mechanism have an important role in setting up reliable security policies to ensure the protection of private and critical systems and infrastructures. While a firewall is considered as an essential node in Information Systems (IS) security and represents the backbone of security solutions, its effectiveness is highly dependent on the efficiency of its configuration and the reliability and coherence of its filtering policy. Enhancing the efficiency of access control solutions via improving the quality and the capacity of firewalls attracted several researchers which led to several generations of firewall technologies. In this context, we introduce the novel concept of FW-TR firewall that integrates a trust-risk assessment approach in firewall solutions. Evaluating and involving the trust-risk associated to the filtering rules and policy in a firewall solution helps primary in: (i) strengthening the quality of the firewall filtering service; (ii) discovering firewall misconfigurations; (iii) analyzing firewall rules for anomalies detection; and (iv) changing the firewall behavior facing critical and malicious scenarios. The current paper defines a framework for organizing thinking about incorporating policies and rules trust-risk values in firewall filtering solutions that constitute what we called FW-TR: the new generation of firewalls.

Substantial advances in Information and Communication Technologies (ICT) bring out novel concepts, solutions, trends, and challenges to integrate intelligent and autonomous systems in critical infrastructures. A new generation of ICT environments (such as smart cities, Internet of Things,edge-fog-social-cloudcomputing, and big data analytics) is emerging; it has different applications to critical domains (such as transportation, communication, finance, commerce, and healthcare) and different interconnections via multiple layers of public and private networks, forming a grid of critical cyberphysical infrastructures. Protecting sensitive and private data and services in critical infrastructures is, at the same time, a main objective and a great challenge for deploying secure systems. It essentially requires setting up trusted security policies. Unfortunately, security solutions should remain compliant and regularly updated to follow and track the evolution of security threats. To address this issue, we propose an advanced methodology for deploying and monitoring the compliance of trusted access control policies. Our proposal extends the traditional life cycle of access control policies with pertinent activities. It integrates formal and semiformal techniques allowing the specification, the verification, the implementation, the reverse-engineering, the validation, the risk assessment, and the optimization of access control policies. To automate and facilitate the practice of our methodology, we introduce our systemSVIRVROthat allows managing the extended life cycle of access control policies. We refer to an illustrative example to highlight the relevance of our contributions.

The considerable increase of the risk associated to inner threats has motivated researches in risk assessment for access control systems. Two main approaches were adapted: (i) a risk mitigation approach via features such as constraints, and (ii) a risk quantification approach that manages access based on a quantified risk. Evaluating the risk associated to the evolutions of an access control policy is an important theme that allows monitoring the conformity of the policy in terms of risk. Unfortunately, no work has been defined in this context. We propose in this paper, a quantified risk-assessment approach for monitoring the compliance of concrete RBAC-based policies. We formalize the proposal and illustrate its application via a case of study.