The emergence of large-scale quantum computers threatens the security of classical public-key cryptosystems, making it essential to adopt post-quantum (PQ) security in Intelligent Transportation Systems (ITS). We introduce a framework that blends quantum-resilient cryptographic primitives with a sharded blockchain architecture. Each shard maintains a local ledger for its vehicle group, enabling real-time transactions and efficient certificate management without overloading any single chain. A lightweight global chain periodically anchors all shards, preserving system-wide consistency and blocking malicious revocations. Vehicles register or revoke PQ credentials via a lightweight Proof-of-Stake consensus, while roadside units (RSUs) handle signature verification to offload on-board computation. We further demonstrate practicality through a federated-learning case study in which vehicles exchange signed model updates over the same secure channel. SUMO/TraCI simulations with 2 000 vehicles and 10 shards show that despite PQ overhead the system sustains near real-time delays and high throughput. The framework thus offers a decentralized, quantum-resilient solution for secure Vehicle-to-Everything communications in next-generation ITS.

The Dark web is considered the concealed part of the internet and harbors a huge assortment of cyber threats that compromise global security. One must understand what goes into the technical infrastructure of the Dark Web to develop an effective strategy for monitoring threats, conducting investigations, and implementing the appropriate security measures necessary to protect against illegal activities and data breaches originating from the Dark Web. In such a rapidly changing cyber threat landscape, especially threats originating from the Dark Web, it calls for a re-evaluation of the traditional information security curricula at academic institutions. This educational research work investigates the compelling need to integrate Dark Web Education into Cybersecurity programs for arming the future workforce with a comprehensive knowledge base and skillset necessary to fight modern-day cyber threats. A review of the current state of cybersecurity education reveals wide gaps in knowledge and readiness about Dark Web issues. This paper presents a structured approach for integrating Dark Web topics into the existing curricula on cybersecurity, focusing on legal, ethical, and technical dimensions. We believe in balance: on one side, theoretical knowledge; on the other, hands-on experiences that ensure the learner takes away just how complex the Dark Web is-without taking part in or condoning any illegal activities. Equally, a set of recommendations are commented on for educators and developers of curricula to integrate education about Dark Web safely and effectively into their cybersecurity programs, which will enhance the overall quality and relevance of cybersecurity education in preparing the students for the challenges of the digital age.

Federated Learning (FL) enables participant devices to collaboratively train deep learning models without sharing their data with the server or other devices, effectively addressing data privacy and computational concerns. however, FL faces a major bottleneck due to high communication overhead from frequent model updates between devices and the server, limiting deployment in resource-constrained wireless networks. In this paper, we propose a three-fold strategy: firstly, an Adaptive Feature-Elimination Strategy to drop less important features while retaining high-value ones; secondly, Adaptive Gradient Innovation and Error Sensitivity-Based Quantization, which dynamically adjusts the quantization level for innovative gradient compression; and thirdly, Communication Frequency Optimization to enhance communication efficiency. We evaluated our proposed model's performance through extensive experiments, assessing accuracy, loss, and convergence compared to baseline techniques. The results show that our model achieves high communication efficiency in the framework while maintaining accuracy.

Vehicular Ad-hoc Networks (VANETs) are integral to intelligent transportation systems, enabling vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing. However, the highly dynamic nature of VANETs introduces challenges, such as unpredictable network conditions, high latency, energy inefficiency, and task failure. This research addresses these issues by proposing a hybrid AI framework that integrates supervised learning, reinforcement learning, and Particle Swarm Optimization (PSO) for intelligent task offloading and resource allocation. The framework leverages supervised models for predicting optimal offloading strategies, reinforcement learning for adaptive decision-making, and PSO for optimizing latency and energy consumption. Extensive simulations demonstrate that the proposed framework achieves significant reductions in latency and energy usage while improving task success rates and network throughput. By offering an efficient, and scalable solution, this framework sets the foundation for enhancing real-time applications in dynamic vehicular environments.

Post-quantum cryptography (PQC) is essential to secure vehicular ad-hoc networks (VANETs) against emerging quantum computing threats. However, selecting an appropriate Post-Quantum Key Encapsulation Mechanism (PQ-KEM) is challenging due to varying performance metrics such as key generation time, encapsulation/decapsulation latency, and ciphertext overhead. This issue becomes particularly critical in VANETs, where vehicles and roadside units (RSUs) must rapidly and securely exchange data under dynamic network conditions. Current methods typically overlook the initial key distribution phase, leaving communications vulnerable at the earliest interaction. To address these challenges, we created an extensive, open-source benchmark dataset that rigorously evaluates several candidate PQ-KEM algorithms based on performance factors relevant to vehicular environments. Leveraging this benchmark, we developed a lightweight deep learning model that dynamically selects the most suitable PQ-KEM algorithm by predicting optimal performance considering security requirements and real-time conditions such as message size and network congestion. Each recommended PQ-KEM algorithm is authenticated using Dilithium-2 post-quantum signatures, ensuring secure and quantum-resilient initial key distribution between vehicles and RSUs. Our comprehensive simulations demonstrate that our adaptive PQ-KEM selector significantly reduces end-to-end latency and ciphertext overhead without compromising security, thus enhancing secure, efficient communication in VANET scenarios.

In the Internet of Medical Things (IoMT), specifically in the field of medical image classification—particularly for skin cancer detection—traditional methods face challenges related to data privacy, heterogeneity, and the need for personalization across institutions. This research proposes a personalized federated learning (PFL) framework Meta-XPFL that addresses these challenges through a decentralized approach, allowing institutions to collaboratively train models without sharing raw data. The framework integrates meta-learning for adaptability, and self-supervised learning to leverage unlabeled data and secure multiparty computation (SMPC). Adversarial training improves model robustness, while attention mechanisms enhance the focus on relevant image features. The use of explainable AI techniques ensures interpretability, which is crucial in clinical settings. To validate the proposed framework, experiments were conducted on the HAM10000 dataset for skin cancer classification, demonstrating significant improvements in model accuracy, privacy preservation, and robustness against adversarial attacks compared to traditional methods. The results indicate that the framework not only enhances scalability and diagnostic accuracy but also offers a privacy-preserving solution that can be extended to various types of medical images, making it adaptable for broader applications in IoMT.

The ongoing expansion of the digital landscape has led to a growing convergence between the fields of artificial intelligence (AI) and network security. This has necessitated the need for universities to incorporate AI into their network security curriculum. Although traditional network security courses are considered crucial, they lack the agility to address constantly evolving threats. AI offers a transformative solution to such difficulties with its predictive analytics, real-time intrusion detection, and adaptive learning capabilities. This study highlights the importance of incorporating AI into network security curricula at the undergraduate level. A modification to the curriculum is proposed, wherein AI themes are integrated into network security courses and labs. The proposed curricula include understanding theoretical AI concepts and designing AI -augmented hands-on laboratories. The pedagogy emphasizes the tools, and frame-works that facilitate the construction of AI models for intrusion detection, mal ware analysis, and network analytics. This plays a significant importance in providing a simulated environment for students to engage with AI tools and methods to address authentic cyber threats.

Fog computing is a technology that brings computing, storage, and networking services closer to devices and systems, aiming to improve speed, efficiency, and data processing capabilities for various applications. The growing importance of fog and edge computing technologies means we need new and better ways to teach students about these areas. This paper offers a detailed guide on how to use xFogSim, an extended version of the Omnet++ network simulator, for teaching fog and edge computing. We give students a clear path to follow, starting with simple network designs and moving to more complex ones, helping them understand how federated learning works. We tested xFogSim with a group of students and found that it really helps them grasp fog and edge computing ideas better than traditional teaching methods. xFogSim also gives practical information about important performance metrics, helping bridge the gap between what students learn in class and what they need to know in the real world. This paper shows that using xFogSim in classrooms gives students a strong base in distributed computing systems, getting them ready for future tech challenges.

The realm of academic security education is primarily focused on defensive strategies. However, there's a growing acceptance of offensive techniques, initially crafted by hackers. Several educators in the field of information security believe that incorporating offensive strategies into the curriculum creates more adept security professionals than focusing solely on defensive methods. Students in information security courses must engage in offensive and defensive tactics to effectively handle malicious activities and devise suitable security measures. This paper presents a case study on executing an in-depth, practical cybersecurity laboratory exercise centered on a prevalent network attack, the DNS spoofing attack, which is vital for network security training. The primary educational goal of this hands-on lab exercise is to equip students with the skills to conduct a DNS spoofing attack within a controlled, virtual network environment using Kali Linux. The introduction of this offensive cybersecurity lab exercise resulted in enhanced student performance; however, it also raised significant ethical issues. Consequently, the paper outlines several measures that academic institutions should consider to mitigate the risks associated with teaching offensive strategies in information security education programs.

Early treatment is required to control the symptoms and serious complications caused by schizophrenia (SZ). People suffering from SZ require lifelong treatment. The use of machine learning (ML) models to detect various health problems such as SZ has received considerable attention from researchers in recent years. This study investigated the effectiveness of various ML models to detect and predict SZ using electroencephalogram data. A dataset of 14 healthy schizophrenic patients was used, and 12 features were extracted after applying independent component analysis. Three traditional ML models (logistic regression, support vector machine, and K-nearest neighbors) and a convolutional neural network (CNN) were trained, and their performance was compared. Results demonstrated that the CNN model outperformed the other three models with the highest accuracy score of 95% on validation data. Our results highlight the potential of using ML in the early detection and prediction of SZ, which can help in timely and effective treatment.

Federated learning (FL) is a promising approach for training AI models across multiple clients in Edge Computing (EC), without sharing raw local data. By enabling local training and aggregating updates into a global model, FL maintains privacy while facilitating collaborative learning. Nevertheless, FL encounters several challenges, including trustworthy client participation, inefficient model aggregation due to client with malicious or less accurate model. In this paper, we propose a trustworthy FL method incorporating Q-learning, trust, and reputation mechanisms, enhancing model accuracy and fairness. This method promotes client participation, mitigates malicious attacks' impact, and ensures fair model distribution. Inspired by reinforcement learning, the Q-learning algorithm optimizes client selection using the Bellman equation, enabling the server to balance exploration and exploitation for improved system performance. Furthermore, we explored the advantages of peer-to-peer FL settings. Extensive experimentation demonstrates our proposed trustworthy FL approach's effectiveness in achieving high learning accuracy while ensuring fairness across clients and maintaining efficient client selection. Our results reveal significant improvements in model performance, convergence speed, and generalization.

Streaming large amounts of data to cloud data centers cause network congestion resulting in high network and energy consumption. The concept of fog computing is introduced to reduce workload from backbone networks and support delay-sensitive Internet of Things (IoT) applications. The concept places compute, storage, and network services closer to the source of the requests. In general fog-based simulators are used for better understanding and optimum fog resource allocation. Unfortunately, most of the simulators lack core features like network delay, latency, packet error rate, energy consumption, and distributed fog node management. In this paper, we propose a fog simulation framework termed as xFogSim to support latency-sensitive applications at the fog layer with multi-objective optimization to trade-off cost, availability, and performance among the fog federation. Moreover, during peak load, the framework provides locality-aware distributed broker node management that enables borrowing resources from nearby fog locations to meet service and energy requirements. The results show that the framework is lightweight, configurable, and scalable, capable of handling a large number of user requests using dynamic resource provisioning across the fog federation.

The concept of smart farming has led to the use of technology to enhance agricultural productivity. With access to low-cost sensors and management systems, more farmers are adopting this technology to achieve sustainable growth. However, in literature, there are no simulation platforms to help researchers and users understand sensor deployment, and data collection and processing. In this article, we propose a framework designed to provide a complete farming ecosystem. The toolkit facilitates users to simulate custom farming scenarios, specifically to identify sensor placement, coverage area, line-of-sight deployment, and data gathering through the relay mechanism or airborne systems, mobility models for mobile nodes, energy models for on-ground sensors and airborne vehicles, and backend computing support using the fog computing paradigm. Furthermore, in most of the existing works, network parameters are ignored, which can impact the overall performance of any deployed system. Therefore, the proposed framework also provides a benchmark in terms of transmission delay, packet delivery ratio, energy consumption, and system resources usage.