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This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today’s world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.

Sixth-generation (6G) communication is emerging as a seamless and massive connection of almost all digital devices. Vehicles, which are extensively linked with human mobility, must keep up with a certain technological pace to maintain compatibility with the 6G era. 6G is also expected to potentially revolutionize Vehicle-to-Everything (V2X) communication. However, this modernization will surface several security challenges in the complex heterogeneous architecture of V2X communication in 6G. Similarly, the expansion of V2X is expected to introduce its own unconventional security risks and vulnerabilities. Therefore, the current paper aims to provide an overview of the security challenges associated with and solutions for V2X communication in the upcoming 6G era to visualize the future of this research domain. This paper discusses the architecture and standards utilized in 6G-enabled V2X communications and provisions a comprehensive analysis of V2X security in the Confidentiality, Integrity, Availability, Authentication, and Access Control (CIA3) domains. Through this process, we analyze the impact of the emerging technological concepts of Blockchain and Federated Learning (FL) in 6G-enabled V2X communication. Therefore, we propose a Blockchain-enabled FL-based generic security architecture for V2X communication in 6G networks. Ultimately, this review highlights the key lessons that have been learned and the future research directions in the domain of security of V2X communications in the 6G including Privacy in 3D Fog Computing, Privacy in Augmented Reality, Secure Software Defined Networking (SDN), Physical Layer Security in the THz Spectrum, SUMO (Simulation of Urban MObility), and Intrusion Detection using AI.

Contemporary mobile networks, offering advanced services such as highly dependable and fast communication (URLLC) and extensive device-to-device connectivity (mMTC), are paving the way for the upcoming 6G era. These networks are expanding their capabilities beyond traditional voice and short messaging services, enabling diverse devices to connect to the cellular network. However, with this increased connectivity comes a heightened vulnerability at the radio interface, which is the primary access medium for mobile network communication. This research work focuses on safeguarding the availability of the radio interface in the face of emerging threats. Threats to radio interface availability can originate either directly from exploiting the 3GPP radio protocol stack within base stations or indirectly through the IP protocol stack carried over the user plane. In particular, this research paper delves into user plane DDoS attacks leveraging the IP protocol stack to generate excessive traffic. It introduces a novel detection method situated within the Radio Access Network (RAN). This method analyzes the patterns of radio protocols and their functionalities to identify user plane DDoS attacks initiated from User Equipment (UEs). Importantly, the method does not rely on directly inspecting user plane packets like IP packets but rather leverages the characteristics of 3GPP radio protocols (e.g., MAC, RLC, PDCP) to detect IP DDoS attacks closer to their origin. This early detection capability helps prevent DDoS traffic from propagating to the transport network. The implications of this research extend beyond the current generation of networks, as it lays a foundation for enhancing security in the forthcoming 6G networks. As 6G aims to deliver even more advanced services and connectivity across a diverse array of devices, the robust security measures proposed in this work will be instrumental in ensuring the reliability and availability of these cutting-edge networks. The analysis employed in this paper showcase the performance with accuracy of 98.9% for DDoS attack detection.

Supply chain as an industry has gone through four-fold changes in the last century. Born as a bare-bones structure in 1.0 it grew to incorporate some form of record preservation in 2.0 and then integrated communication between two entities in 3.0. Supply chain 4.0, the current one, has total global integration of multiple entities with the records digitised. But increasing entities and pipelines, means increasing complexities, overhead and soft spots. In this paper, a systematic literature review is done with the objective of analysing existing Supply Chain 4.0. The focus of the paper is the usage of blockchain technology in the electronic industry to provide a decentralised architecture. Several papers were compared on the basis of different schemas like the type of blockchain network used, platform deployed on, security of frameworks, representation of unique identity, testing authenticity, working implementation, cost of implementation, etc. The pros and cons of various privacy and security methodologies are also explored and discussed. The paper also discusses the open issues and challenges in the same area of interest. Finally, the paper outlines the future scope to be delved into as a part of the future research.

Unmanned Aerial Vehicles (UAVs) are advanced technologies that are initially utilized for military apps like border monitoring and reconnaissance in opposed territories. Internet of Things (IoTs) assisted UAV networks suggest the combination of IoT technology with UAVs to generate a networked system that improves the abilities and utility of UAVs for several apps. UAVs’ inherent features namely quick deployment, high dynamicity, low deployment and operational costs, and line of sight communication motivated researchers in the IoT field to assume UAV’s combination into IoT systems near the concept of UAV-assisted IoT systems. However, security concerns with UAVs are evolving as UAV nodes are suitable attractive targets for cyber threats because of extremely developing volumes and poor and weak inbuilt security. Therefore, this paper presents a Modified Marine Predators Algorithm with a Deep Learning-Driven intrusion detection (MMPADL-ID) approach for IoT Assisted UAV Networks. The presented MMPADL-ID technique proposes to identify and classify the presence of intrusions in accomplishing security in IoT-assisted UAV networks. In the MMPADL-ID technique, the feature selection process is performed by the design of MMPA. In addition, the MMPADL-ID technique incorporates the Elman neural network (ENN) model for the recognition and classification of the intrusions. Furthermore, the honey badger algorithm (HBA) can be applied for the hyperparameter tuning of the ENN model and results in improved performance. The simulation value of the MMPADL-ID technique can be tested on benchmark datasets. An extensive comparative outcome reported the better solution of the MMPADL-ID algorithm with existing approaches for various aspects.

In the 5G and beyond 5G networks, achieving security-aware data transmission needs to convert clients ’ requests into a service function chain (SFC), each service function (SF) providing a certain security guarantee. With diverse configuration techniques, an SF may own multiple versions, each version providing various security guarantees with diverse costs. It should be notice that, as the recent software failures have caused severe financial loss, great attentions from both academia and industry have been put onto the SFC reliability. In the literature, existing works have solely investigated the following two fields: 1) how to deploy a security-aware SFC, and 2) how to protect a traditional SFC. Simply applying these techniques to dealing with the problem of security-aware SFC protection might not be efficient as the backup and primary SFCs may not be identical for security-aware SFCs. Therefore, how to jointly take these fields into account is challenging and remains open. To tackle the above problem, this paper studies how to construct and embed a security-aware SFC with asymmetric dedicated protection. We mathematically define this problem and name it security-aware service function chaining, embedding, and protection with multi-versioned SFs (SFCEP-MF) with the objective of cost optimization. Next, to optimize the SFCEP-MF problem, we construct an efficient algorithm, called augmenting-path with primary-first disjoint SFP identifier (APPF-DSI). Extensive simulation results show that the APPF-DSI algorithm outperforms the benchmark approaches that are directly extended from the state-of-the-art.

Network slicing has emerged as a cornerstone technology within the 5G ecosystem, enabling efficient resource allocation, service customization, and support for various applications. Its ability to deliver Network-as-a-Service (NaaS) brings a new paradigm of adaptable and efficient network provisioning. However, with the diversification of services and the increasing complexity of network infrastructures, a simultaneous rise in security vulnerabilities becomes evident. These flaws go beyond the limitations of conventional network security and affect various aspects of network slice (NS) implementation and management. The limitations of traditional security, such as static policies, single point of failure, and challenges in effectively securing network slicing deployments, underscore the need to explore security measures tailored to the dynamic nature of 5G networks. To ensure the robust security of 5G networks, it is essential to consider various security concerns such as isolation, authentication, and authorization. Furthermore, dynamic orchestration and inter-slice communication security challenges must be proactively tackled. The security concerns related to 5G networks must be addressed comprehensively to ensure the safe and secure operation of the network. Our survey paper goes into these complex security issues, providing an in-depth and systematic review of the various contexts in which they emerge. We have identified the five most vulnerable areas in Network Slicing: Slice-Lifecycle, Communication type slice uses, Technologies used to provide service, Management threats, and End Devices utilized in service. Apart from threats in these vulnerable areas, we also discussed a few generous attacks that can be launched to disrupt network-slicing services. Furthermore, this study is a valuable resource for evaluating the current state of research efforts in this domain, contributing to the ongoing enhancement of security measures and the overall robustness of network-slicing technology. In doing so, we aim to ensure the secure and sustainable evolution of 5G networks as they become increasingly integral to our digital infrastructure.

Modern technologies adopt Internet of Things (IoT) devices to increase water management efficiency and enhance water quality services. However, the limitations of IoT devices, such as small sizes and poor security, weaken the Water Distribution System (WDS) security, and many attackers compromise the critical components of WDS. Cyber-physical attacks (CPAs) are considered one of the biggest challenges that decrease the security factors in WDS by disrupting normal operations and tampering with the critical data of the water system. For instance, an attacker can change the water pump’s speed, disrupting the service. An attacker can also alter the data of water quality parameters to contaminate the water. It is important to propose solutions to increase security in the WDS and defend against CPAs and security threats. Although several intrusion detection methods were proposed in the literature to detect WDS CPAs, many issues still need solutions, such as detecting attacks with smaller false alarms, minimizing the time to disclose the attacks, determining the location of the compromising components, and recovering solutions for the attacked components. Therefore, this paper proposes a model based on a deep learning algorithm called a Conditional variational Autoencoder (CVAE) to disclose CPAs and mitigate their bad effects on WDS. The proposed method consists of a neural network, an encoder to compress data, and a decoder to decompress data. The objective goal is to minimize the reconstruction error between the encoded-decoded data and the initial data. We apply the CVAE on some well-known datasets. The experiment results show that our proposed CVAE method performs better than others. After analyzing the CVAE model with other existing models, we get the highest performance by reaching %98 accuracy.

The integration of fog computing in vehicular networks has led to significant advancements in road safety, traffic control, entertainment, and comfort services. Vehicular Fog Computing (VFC) emerges as an optimistic solution, offering a pathway to responsive service requests. VFC uses either onboard vehicle computers or vehicles as fog infrastructure between the underlying networks and the cloud, addressing the limitations of centralized data processing in traditional cloud computing. However, VFC faces security vulnerabilities due to the open nature of its deployment. In this survey, we explore the security threats confronting VFC and review existing solutions related to their detection and mitigation capabilities. This paper provides a comprehensive overview of the foundational concept of Fog Computing, VFC architectures, and their critical role in various intelligent computing applications. Moreover, it spotlights the trust and security concerns in deploying VFC and real-time big data analytics within the vehicular environment. This survey identifies pressing issues and outlines potential research directions, offering insights for the research community to address while designing secure VFC architectures.

Internet and cloud-based technologies have facilitated the implementation of large-scale Internet of Things (IoT) networks. However, these networks are susceptible to emerging attacks. This paper proposes a novel lightweight system for detecting both high- and low-volume Distributed Denial of Service (DDoS) attacks in IoT networks, namely Genetic Algorithm (GA) and t-Test for DDoS Attack Detection (GADAD). The GADAD system employs edge-based technologies and has three phases. In the first phase, it creates and preprocesses an HL-IoT (High- and Low-volume attacks in IoT networks) dataset, which includes both high- and low-volume DDoS attacks. The second phase introduces a novel and lightweight method, called GAStats, for optimal feature selection using the GA and statistical parameters (Stats.). In the third phase, the system trains three tree-based Machine Learning (ML) models: Random Forest (RF), Extra-Tree (ET), and Adaptive Boosting (AdaBoost), along with other ML models, using both the self-generated HL-IoT dataset and the publicly available ToN-IoT dataset. The evaluation includes the assessment of key performance metrics such as accuracy, precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC), computation time, and scalability analysis with overall system performance. The experimental results illustrate the efficacy of the feature selection method in optimizing the system’s efficiency in detecting DDoS attacks in IoT networks, along with a reduction in computation time compared to existing state-of-the-art techniques.

The Internet of Things (IoT) has revolutionized various domains, enabling interconnected devices to communicate and exchange data. The integration of Artificial Intelligence (AI) in IoT systems further enhances their capabilities and potential benefits. Unfortunately, in the era of AI, ensuring the privacy and security of the IoT faces novel and specific challenges. IoT security is imperative, necessitating comprehensive strategies, including comprehension of IoT security challenges, implementation of AI methodologies, adoption of resilient security frameworks, and handling of privacy and ethical concerns to construct dependable and secure IoT systems. It is vital to note that the term ‘security’ encompasses a more comprehensive view than cyberattacks alone. Therefore, with an emphasis on securing against cyberattacks, this comprehensive survey also includes physical security threats on the IoT. It investigates the complexities and solutions for IoT systems, placing particular emphasis on AI-based security techniques. The paper undertakes a categorization of the challenges associated with ensuring IoT security, investigates the utilization of AI in IoT security, presents security frameworks and strategies, underscores privacy and ethical considerations, and provides insights derived from practical case studies. Furthermore, the survey sheds light on emerging trends concerning IoT security in the AI era. This survey provides significant contributions to the understanding of establishing dependable and secure IoT systems through an exhaustive examination of the present condition of IoT security and the ramifications of AI on it.

Building smart services for smart cities has become a significant focus of the Internet of Things (IoT). These IoT devices are able to sense their surroundings and react appropriately. Smart city applications emphasize the necessity of safe data sharing across heterogeneous devices. Certain behaviors taken while sharing could aim at compromising security, privacy, and integrity. The centralized repository that is currently in place made the majority of hacks possible. The sharing of sensitive data and authentication are essential stages in guaranteeing the security of applications associated with IoT. Blockchain and IoT are two widely used technologies, with IoT focusing on data collection via various devices and blockchain enabling data integrity. This paper introduces a novel blockchain-based framework to ensure the security and integrity aspects of IoT data. The proposed SecPrivPreserve framework ensures security through various phases including initialization, registration, data protection, authentication, data access control, validation, and data sharing and download. Diverse security mechanisms such as passwords (OTP), encryption, and hashing have been deployed in various phases to strengthen security merits confidentiality, privacy, and integrity. Since the SecPrivPreserve framework is simulated in a permissioned blockchain platform the merits and tamper-proof and non-repudiation are automatically considered. Moreover, data protection uses Chebyshev polynomials and interpolation. The presented framework has experimented with Fabric SDK. The experimental results of the proposed framework are compared with the BaseLine state-of-the-frameworks, The experimental analysis reveals that the proposed SecPrivPreserve approach achieved 34 Sec improvement in terms of responsiveness 94 Sec as computational time, encryption quality as 0.87 Sec and 0.82 Sec for detection rate.

In the era of big data, protecting digital images from cyberattacks during network transmission is of utmost importance. While various image encryption algorithms have been developed, some remain vulnerable to specific cyber threats. This paper presents an enhanced version of the image encryption algorithm based on bit-plane extraction (BPCPD) to address its vulnerability to chosen-plaintext attacks. The proposed cryptographic system encompasses three primary phases. The initial phase involves bit-plane extraction from the plaintext image and the generation of random sequences and a random image using multiple chaotic maps, such as the chaotic Arnold map and the chaotic CAT map. The second phase is dedicated to permutation operations, which comprise three sub-phases: multi-layer permutation, multi-round permutation, and recursive permutation. In the third phase, diffusion is introduced to the permuted image through pixel substitution, coupled with XOR operations performed on the respective bit-planes of the random image. To gauge the efficiency of the proposed encryption scheme, a range of experimental analyses are conducted, including histogram analysis, contrast assessment, entropy measurement, correlation analysis, encryption quality assessment, and investigations into noise attacks and occlusion attacks. The results of these experimental analyses, in comparison to an existing encryption scheme, demonstrate that the proposed framework exceeds both BPCPD and other existing encryption schemes in various aspects of performance.

Deep learning (DL) has demonstrated remarkable achievements in various fields. Nevertheless, DL models encounter significant challenges in detecting and defending against adversarial samples (AEs). These AEs are meticulously crafted by adversaries, introducing imperceptible perturbations to clean data to deceive DL models. Consequently, AEs pose potential risks to DL applications. In this paper, we propose an effective framework for enhancing the robustness of DL models against adversarial attacks. The framework leverages convolutional neural networks (CNNs) for feature learning, Deep Neural Networks (DNNs) with softmax for classification, and a defense mechanism to identify and exclude AEs. Evasion attacks are employed to create AEs to evade and mislead the classifier by generating malicious samples during the test phase of DL models i.e., CNN and DNN, using the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and Square Attack (SA). A protection layer is developed as a detection mechanism placed before the DNN classifier to identify and exclude AEs. The detection mechanism incorporates a machine learning model, which includes one of the following: Fuzzy ARTMAP, Random Forest, K-Nearest Neighbors, XGBoost, or Gradient Boosting Machine. Extensive evaluations are conducted on the MNIST, CIFAR-10, SVHN, and Fashion-MNIST data sets to assess the effectiveness of the proposed framework. The experimental results indicate the framework’s ability to effectively and accurately detect AEs generated by four popular attacking methods, highlighting the potential of our developed framework in enhancing its robustness against AEs.

The Internet of Things (IoT) is an emerging technology with a high market growth rate. In the IoT, machines and users from different levels must collaborate to exchange data and share resources. The IoT opens the door for a vast improvement in all aspects of human life. However, the increasing adoption of the IoT in many sectors makes it difficult to control security risks. For this reason, there is a need for more effort in both research and industry to address the risks and find convenient solutions. This systematic literature review delves deeply into understanding the unique challenges and requirements posed by IoT environments. Through a detailed examination of 96 selected studies, this paper primarily addresses three research questions. The study concludes by summarizing key requirements, technologies, and metrics founded on our comprehensive analysis, aiming to steer further research in the domain. As IoT continues its expansion into various facets of our daily lives, there’s a paramount need to integrate with emerging technologies and ensure scalability. Prioritizing real-world implementations is crucial for the next wave of innovations in access control systems.

Supply chains increasingly develop toward complex networks, both technically in terms of devices and connectivity, and also anthropogenic with a growing number of actors. The lack of mutual trust in such networks results in challenges that are exacerbated by stringent requirements for shipping conditions or quality, and where actors may attempt to reduce costs or cover up incidents. In this paper, we develop and comprehensively study four scenarios that eventually lead to end-to-end-secured sensing in complex IoT-based supply chains with many mutually distrusting actors, while highlighting relevant pitfalls and challenges—details that are still missing in related work. Our designs ensure that sensed data is securely transmitted and stored, and can be verified by all parties. To prove practical feasibility, we evaluate the most elaborate design with regard to performance, cost, deployment, and also trust implications on the basis of prevalent (mis)use cases. Our work enables a notion of secure end-to-end sensing with minimal trust across the system stack, even for complex and opaque supply chain networks.

The development of smart vehicles has been a major focus of the automotive industry in recent years. Smart vehicles, equipped with advanced sensors and communication technologies, represent a transformative paradigm in modern transportation systems. Some of the challenges associated with the introduction of smart vehicles include developing reliable sensors, creating robust communication networks, and ensuring the security of vehicle systems. This paper proposes a Blockchain-based framework for accident prevention on the Internet of Vehicles, where vehicles monitor the state of the route and transmit information about hazardous situations to the Blockchain network. Once the event is confirmed, warnings are sent to all vehicles, and their speed is automatically reduced to avoid accidents. The tests in a 3D graphical simulator, combined with Hyperledger Besu technology for the creation of the Blockchain network, demonstrated both horizontal and vertical scalability, validating the potential of this framework for real-world integration. Moreover, it presents a fast reaction to anomalous situations on a route compared to human reactions under similar circumstances.

Preventing Use-After-Free (UAF) bugs is crucial to ensure temporal memory safety. Against UAF attacks, much research has adopted a well-known approach, lock-and-key, in which unique, disposable locks and keys are first assigned respectively to objects and pointers, and then on every memory access, checked for a match. Attention has been drawn again to this approach by recent work that capitalizes on a vast abundance of virtual address (VA) space in the lock assignment, thus being able to prevent UAFs in stripped binary. However, as this VA-based lock-and-key scheme tends to rapidly consume virtual space, it is likely to suffer from high performance overhead. In this paper, we propose a new scheme, called the VA tagging, whose goal is to tackle this performance problem with the support of the Memory Tagging Architecture (MTA) introduced in several commodity processors. In our scheme, the original VA-based locks are augmented with tags of MTA. As a VA-based lock can be assigned to multiple objects with different tags, the same VA is reused for many objects without compromising temporal safety. We have observed in our experiments that this tagging scheme lowers the VA consumption rate drastically by one order of magnitude. We implement a light-weight memory allocator, Vatalloc, by modifying existing allocators, dlmalloc and jemalloc, to employ the VA tagging scheme for efficient prevention of UAFs. Our evaluation shows that Vatalloc with allocator modifications only incurs 1.70% (on dlmalloc) and 3.05% (on jemalloc) of runtime overhead without considering performance degradation of MTE. As a result of simulating the tagging architecture assuming the worst-case, postulating MTE precise trapping mode incurs performance overhead of 30.9% based on dlmalloc, and 25.5% based on jemalloc. If imprecise mode is assumed, the slowdown is measured 16.9% for dlmalloc and 12.0% for jemalloc respectively. Vatalloc only incurs 19.0% and 3.0% memory overhead for dlmalloc and jemalloc respectively.

As reliance on disruptive applications based on Artificial Intelligence (AI) and Blockchain grows, the need for secure and trustworthy solutions becomes ever more critical. Whereas much research has been conducted on AI and Blockchain, there is a shortage of comprehensive studies examining their integration from a security perspective. Hence, this survey addresses such a gap and provides insights for policymakers, researchers, and practitioners exploiting AI and Blockchain’s evolving integration. Specifically, this paper analyzes the potential benefits of the integration of AI and Blockchain as well as the related security concerns, identifying possible mitigation strategies, suggesting regulatory measures, and describing the impact it has on public trust.

Advanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in autonomous vehicles (AVs), have raised the need to research ADAS security technology. The security issues raised by incorporating these technologies into automobiles must be addressed to protect the privacy and safety of passengers and other road users. As a result, the purpose of this research is to investigate the security issues that arise from the integration of ADAS technologies. Addressing these challenges holds the potential to establish a foundation for enhanced safety and dependability within transportation networks amidst the ongoing advancements in vehicle technology. This paper starts by describing the vulnerabilities, threats, assaults, and defense mechanisms of the ADAS. It then delves into the attacks and countermeasures in three categories, namely VANET, Hardware, and Adversarial attacks. VANET attacks encompass threats targeting Vehicular Ad Hoc Networks, aiming to disrupt communication among vehicles or between vehicles and infrastructure. Hardware attacks focus on vulnerabilities within the physical components of ADAS, including sensors, processors, or communication modules. Adversarial attacks involve deliberate manipulations or perturbations introduced into machine learning models or algorithms utilized within ADAS. These attacks aim to deceive or undermine the functionality of AI-based systems, causing misclassification, compromising system integrity, and posing risks to user safety by exploiting vulnerabilities in the AI decision-making process. Finally, this study highlights potential areas for future research, such as the utilization of artificial intelligence (AI), the necessity of industry-wide standardization, and recommends specific future work tailored to each attack described in the corresponding sections.