Accurate waveform identification, especially at low SNRs, remains an open challenge; both classical hypothesis-test detectors and modern deep neural networks degrade sharply as SNR falls. We address this with a graph-neural approach that converts each noisy in-phase/quadrature (IQ) segment into a multi-scale temporal graph: every sample becomes a node linked to neighbors at multiple lags (±1,±2,±4,±8) and to feature-space k-nearest neighbors. Each node carries a six-channel feature vector $\left[ {I,Q,|x|,\phi ,\delta \phi ,|x{|^2}} \right]$ designed to surface subtle patterns masked by noise. A hybrid CNN-GNN then performs classification: a 1D convolutional front-end learns local temporal embeddings, followed by a deep residual GNN with interleaved GCN and GATv2 layers to propagate information over the graph. We use Jumping Knowledge (JK) aggregation to preserve multi-hop and shallow cues, and an attention-based readout to emphasize the most informative nodes. On a synthetic four-class dataset (LTE, 5G NR, Radar, noise) at −10dB average SNR, the proposed model attains 91.3% test accuracy, exceeding strong baselines. This demonstrates substantial performance gains over conventional CNNs and deep learning classifiers in the low-SNR regime.

AI agent-based systems are becoming increasingly integral to modern software architectures, enabling autonomous decision-making, dynamic task execution, and multimodal interactions through large language models (LLMs). However, these systems introduce novel and evolving security challenges, including prompt injection attacks, context poisoning, model manipulation, and opaque agent-to-agent communication, that are not effectively captured by traditional threat modeling frameworks. In this paper, we introduce ASTRIDE, an automated threat modeling platform purpose-built for AI agent-based systems. ASTRIDE extends the classical STRIDE framework by introducing a new threat category, A for AI Agent-Specific Attacks, which encompasses emerging vulnerabilities such as prompt injection, unsafe tool invocation, and reasoning subversion, unique to agent-based applications. To automate threat modeling, ASTRIDE combines a consortium of fine-tuned vision-language models (VLMs) with the OpenAI-gpt-oss reasoning LLM to perform end-to-end analysis directly from visual agent architecture diagrams, such as data flow diagrams(DFDs). LLM agents orchestrate the end-to-end threat modeling automation process by coordinating interactions between the VLM consortium and the reasoning LLM. Our evaluations demonstrate that ASTRIDE provides accurate, scalable, and explainable threat modeling for next-generation intelligent systems. To the best of our knowledge, ASTRIDE is the first framework to both extend STRIDE with AI-specific threats and integrate fine-tuned VLMs with a reasoning LLM to fully automate diagram-driven threat modeling in AI agent-based applications.

Modern 5G networks offer a network-sliced infrastructure where each network slice contains a dedicated 5G core software service layer. The 5G core software services in each slice shares common core network resources to meet specific customer needs. A primary challenge in 5G network slicing involves resource sharing and efficient network slice orchestration. Container-based methodologies, including tools like Docker and Kubernetes, have become popular for orchestrating 5G network slice services and managing configurations in microservices-based cloud-native service deployment. However, despite their utility, these tools present significant challenges. Their complexity often necessitates dedicated DevOps teams for effective management, while configuration management can prove arduous, and end-to-end supply chain oversight is lacking. To address these challenges, this paper introduces “Llama-Recipe,” a cloud-native 5G-core service deployment and orchestration platform integrating Generative AI, SBOM, PBOM and NFT. 5G-core service configurations across different network slices are represented as “HOCON (Human-Optimized Config Object Notation)” config objects adhering to the GitOps paradigm. Leveraging custom-trained Meta's Llama2 LLM, Llama-Recipe generates the Kubernetes manifests for network-sliced 5G-core services based on the defined HOCON configurations. The generated Kubernetes manifests of the 5G-core services are deployed in designated Kubernetes clusters utilizing GitOps tools (e.g., ArgoCD), ensuring seamless and automated deployment processes. Additionally, Llama-Recipe introduced a novel mechanism to handle end-to-end supply chain verification of 5G-core software services using Software-Bill of Materials (SBOM) and Pipeline-Bill of Materials (PBOM). SBOMs track all the dependencies and PBOMs facilitate the comprehensive tracking of end-to-end supply chain data for 5G-core software services, enhancing transparency and security. These PBOMs are also generated using the fine-tuned Meta's Llama-2 LLM and are encoded as NFT tokens with a novel NFT token schema. This schema enables easy verification and validation of supply-chain data during deployments, thus helping to prevent various supply-chain attacks. To fine-tune the Meta's Llama2 LLM, we've undertaken a meticulous training process, collaborating with Qlora to transform a 4-bit quantized pre-trained language model into Low-Rank Adapters(LoRA). The effectiveness of the Llama-Recipe is demonstrated through a real-world test-bed deployment in a sliced network scenario, utilizing multiple 5G cores (i.e., Open5GS) across Ericsson's new Radio Access Network (RAN).

Current wind energy data platforms face significant challenges in securing and managing extensive data from both offshore and onshore wind farms. These challenges include vulnerabilities to cyber-attacks, data tampering, breaches, complex data-sharing issues due to privacy concerns and regulatory compliance, and a lack of scalability and flexibility in analytical tools for real-time data processing. This paper proposes a novel multilayered data security architecture, termed "VindSec-Llama," to address these challenges. It integrates Generative AI, blockchain, federated learning, and Pipeline Bill of Materials (PBOM) to enhance data analytics, model development, and security across several layers, including Infrastructure, Data Lake, Federated Learning, MLOps, Data Provenance, and LLM. Each layer is designed to meet specific functional requirements, such as handling large datasets, facilitating secure federated learning, automating risk management, and ensuring data provenance and traceability. The platform, deployable in server environments (cloud or on-premises), complies with the Risk Management Framework (RMF) guidelines and security standards. It features a blockchain-enabled, coordinator-less federated learning system to enhance data privacy and security by enabling the development of privacy-preserving machine learning models with data from different wind farms. Automation plays a pivotal role throughout VindSec-Llama, with Meta’s custom-trained Llama-3 LLM used for generating remediation scripts in the Infrastructure Layer and for producing PPBOM in the MLOps Layer. The Llama-3 LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The MLOps pipeline setup, a critical functionality of VindSec-Llama, ensures seamless integration and deployment of machine learning models, embodying best practices in continuous integration and delivery. This setup is geared towards maximizing security, compliance, and operational efficiency. A prototype of the platform has been implemented within a wind-energy testbed with the collaboration of Department of Energy US, illustrating its practical applications and benefits.

Large Language Models (LLMs) are widely recognized for their applications in natural language processing tasks, but their potential extends far beyond traditional use cases. This paper introduces "Bassa-Llama," a novel platform that harnesses LLMs for predictive tasks in the realm of network security. Specifically, we propose a platform for real-time network attack detection in Wind Power Plants, leveraging a fine-tuned version of Meta’s Llama-3 LLM alongside blockchain and NFT-based data storage. Using a network PCAP dataset containing both malicious and benign packets, we fine-tune the Llama-3 LLM, with Quantized Low-Rank Adapter (QLoRA), to detect anomalies in network traffic. This approach ensures optimal performance on consumer-grade hardware while significantly enhancing the model’s ability to accurately analyze PCAP data and identify attack patterns. The end-to-end orchestration of the real-time network attack detection flow for Wind Power Plants is fully automated through blockchain smart contracts, and NFTs for storing identified attack data from the PCAP. To the best of our knowledge, this research represents the first effort to utilize a fine-tuned LLM for real-time network attack detection tasks. The results highlight the transformative potential of combining fine-tuned LLMs with blockchain and NFTs to build robust and secure network defense systems for Wind Power Plants. A prototype of the proposed platform was developed in collaboration with the U.S. Department of Energy, utilizing a simulated Wind Power Plant as a testbed.

5G networks are expected to provide high-speed, low-latency, and reliable connectivity to support various applications such as autonomous vehicles, smart cities, and the Internet of Things (IoT). However, the performance of 5G networks can be affected by several factors such as interference, congestion, signal attenuation, or attacks, which can lead to packet loss and retransmissions. Retransmissions in the network may be seen as an essential measure to improve network reliability, but a high retransmission rate may indicate issues that can help network operators mitigate possible service disruptions or threats to network users. A deep learning-based approach has been proposed to predict downlink retransmissions in 5G networks, achieving as much as 5%- 15% improvement over traditional prediction algorithms.

This paper takes a view on the identity management system in 5G using self-sovereign identity (SSI) solutions. It focuses on the use case of quasi-static and mobile devices that require a good connection on an isolated ship. The identities need to be self-managed when the ship is disconnected at sea, and the newly created identities need to be resolved when the ship regains connection. An overview of the decentralized / distributed identity management in 5G along with the call flows is presented in this paper. Furthermore, we highlight its impact on the large-scale device identity management process and its interoperability with minimum alterations in the current 5G identity management system. This work explores the security benefits of the decentralized property of SSI and measures the performance difference in merging identities with disconnected parts of the system. The results focus on three main measures of 5G identity management systems that include control overhead, verification delay, and identity reconciliation. The paper presents the costs/benefits to the SSI solution compared to centralized identity management systems.

Summary Wireless sensor networks (WSNs) have attained a great attraction of researchers in the recent years. In these networks, many structures are considered that have different properties. This article offers a unique approach, the inter‐core based reconnection strategy (ICRS), which is intended to improve the robustness of Scale‐Free Networks (SFNs) in the setting of wireless sensor networks (WSNs), with a special emphasis on the Internet of Health Things (IoHT) network. SFNs' vulnerabilities to malicious assaults while remaining resilient to random attacks. The proposed ICRS overcomes this issue by offering a novel reconnection approach that employs separate edges between network centers. Destructive assaults that have a significant impact on network connectivity, emphasizing the importance of a robust network that can resist a variety of attacks. ICRS is positioned as a solution that optimizes the network via reconnection techniques, changing it into an onion‐like structure with increased robustness. The simulation results depict that ICRS outperforms the existing algorithms in terms of robustness enhancement. The results show that ICRS performs 48%, 29%, 22%, and 16% better than Barabasi Albert (BA), Hill Climbing (HC), Simulated Annealing (SA), Random Edge Swap Mechanism (RESM), and Robustness Strategy (ROSE), respectively.

Content retrieval in content-centric vehicular networks faces challenges that include high latency, especially when content is stored far from the requesting vehicle. On-path caching feature in the conventional vehicular named data Networks (VNDN) enables content storage that can reduce latency. However, due to the constantly changing dynamic ad hoc nature of the vehicular network, the availability of stored content for the requester vehicle cannot be guaranteed. In addition, without knowing which content will be requested, where it will be requested and when it will be requested, the content caching functionality of VNDN is underutilized. To address this issue, this manuscript proposes a content prefetching scheme for the Content-centric Internet of Vehicles (CIoV) by introducing the content Value of Popularity ($VoP$) matrix. Considering vehicles requesting content of similar interests, we evaluate$VoP$through three value update functions that follow the power law of the time elapsed since the last content requested. By multiple parameters of consumer vehicle similarity, an on-road proactive content retriever vehicle is selected. The simulation results showed that the proposed proactive on-path content prefetching mechanism significantly reduces the content delivery delay while increasing the success delivery ratio by 48% and extends the spread of content within the network by 53%.

Rrecently, communication technologies have been advanced rapidly to guarantee large bandwidth with low latency and reduced overhead. In addition, many new technologies are in pursuit to ensure guaranteed communication and services such as 5th and 6th generation (5G/6G) services and technologies, future Internet architectures, edge computing techniques, edge cloud-based architectures, services virtualization, and so forth. The prime objective of these new technologies is to make user-produced information easily accessible to the users on the go. Hence, these technologies play a vital role in forms of wireless networks. However, in this Special Issue, we solely focus on the new solutions, services, communication techniques, and architectures to provide efficient information communication to the vehicular networks.

Cyber-physical system (CPS) provides a well-organized integration betweencommunication,computation, andcontrol(3C) technologies. CPS has been widely used in the vehicular networks and it requires to discover multimodal data from the physical system to make appropriate decisions and actions, for example, congestion warnings, applying brakes, adjusting speed limits, etc. Information discovery and availability at individual network elements is one of the fundamental foundations of CPS. In this paper, we proposed two multimodal network information discovery schemes for vehicular CPS using the Named Data Networking (NDN). One of the proposed schemes simply modifies the pull-based NDN communication mechanism to discover multimodal multi-hop data from the network and the other scheme uses the Interest broadcast suppression (IBS) mechanism. The proposed Interest broadcast suppression scheme adapts the holding time technique to defer the Interest forwarding and its computation involves the hop-count, distance, and other network parameters. Simulation results show that the proposed schemes discover about 172 and 162 percent more multimodal information from approximately 283 and 210 percent more network area by suppressing approximately 50 percent of the Interest broadcast storm in highway and the urban traffic scenarios, respectively.

Named Data Networking (NDN) uses the content name to enable content sharing in a network using Interest and Data messages. In essence, NDN supports communication through multiple interfaces, therefore, it is imperative to think of the interface that better meets the communication requirements of the application. The current interface ranking is based on single static metric such as minimum number of hops, maximum satisfaction rate, or minimum network delay. However, this ranking may adversely affect the network performance. To fill the gap, in this paper, we propose a new multi-metric robust interface ranking scheme that combines multiple metrics with different objective functions. Furthermore, we also introduce different forwarding modes to handle the forwarding decision according to the available ranked interfaces. Extensive simulation experiments demonstrate that the proposed scheme selects the best and suitable forwarding interface to deliver content.

The Internet-of-Things (IoT) is the future of the Internet, where everything will be connected. Studies have revealed that fog/edge computing-based services will play a major role in extending the cloud by carrying out intermediary services at the edge of the network. Fog/edge computing-based IoT's (FECIoT) distributed architecture enhances service provisioning along the Cloud-to-Things continuum, thereby making it suitable for mission-critical applications. Furthermore, the proximity of fog/edge devices to where the data is produced makes it stand-out in terms of resource allocation, service delivery, and privacy. From the business perspective, FECIoT will lead to a boom and spring up of small-to-medium-sized enterprises, thereby encouraging inclusion for all. To this end, we present a comprehensive survey on state-of-the-art IoT literature over the period 2008-2018 and propose the FECIoT framework which covers the enabling technologies, services, and open research issues. A tutorial approach is employed, progressing from basic to more advanced concepts within the IoT domain. Lastly, we show how FECIoT can be deployed in real-life cyber-physical systems, such as the intelligent transportation system, smart grid, smart health-care, smart homes, and smart environment.

During the past two decades, we have witnessed a tremendous development in Vehicular networks, while exploring emerging communication technologies such as vehicular cyber-physical systems (VCPS). Basically, VCPS requires multimodal data from the physical system to take appropriate decision and actions, for example, the congestion warnings, applying brakes, adjusting speed limits, etc. However, there are multiple systems interconnected in the VCPS with different communication capabilities and data communication between those systems that lead us to a challenging task. In this paper, we consider named data networking (NDN) as a promising solution to enhance the reachability of Data among multi-hop VCPS. NDN offers a simple pull-based content communication in the network with multiple interfaces and also supports heterogeneity in terms of communications technologies. The proposed NDN forwarding scheme enables vehicles to send one Interest (request) to collect multiple instances of the Data from different content sources in the network. Simulation results show that the proposed scheme can collect information from many nodes that are at longer distance from the information requesting nodes.

Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.

In this paper, we propose a distributed interest forwarder selection (DIFS) scheme that mitigates the interest broadcast storm in vehicular named data networks. In DIFS, a vehicle sends an interest packet piggybacking its location, distance to the neighbors, and speed. In this case, the immediate neighbors do not have the requested content and rank themselves to be an eligible interest forwarder by using multiple attributes. Additionally, every intermediate vehicle uses a digital map to be selected as forwarders in both (forward and backward) directions of the consumer. Simulations show that DIFS satisfies more interest packets with less delay as compared with the recent forwarding solutions.

Named Data Networking (NDN) has been recently added to the future Internet family. NDN is basically an extension to the Content Centric Network (CCN) and is expected to support various applications. Those applications are to be supported by the future internet architectures. NDN believes in naming the content rather than using end-to-end device names. Recently, NDN has been adapted into Vehicular Ad hoc Networks (VANETs) and hence, we name it Vehicular NDN (VNDN). At it's early stage, VNDN faces several challenges such as consumer/provider mobility, Interest/Data forwarding, content caching and so on. Mostly, VNDN relies on the fact that Data is sent back to the consumer via same path the Interest packet was received from. However, we analyzed that it's not true in a VANET and there is lack of discussion about managing the Data flow back to the consumers in the current literature of VNDN. In this paper, we therefore, pursue to control the data flooding/broadcast storm of the conventional VNDN by proposing our scheme “CONET”. The main idea of CONET is to allow the consumer vehicle to start hop counter in the Interest message and upon receiving that interest by any potential provider, to include Time To Live (TTL) value with data messages. The TTL value includes the number of hops, Data packets should travel on its way back to the consumer. Simulation results show that CONET forwards less Copies of Data Messages Processed (CDMP) while achieving similar Interest Satisfaction Rate (ISR) as the basic VNDN. In addition, CONET also minimizes the overall Interest Satisfaction Delay (ISD), respectively.

Recently, Vehicular Ad Hoc Networks (VANETs) have been providing a number of services for on-road users, including video content retrieval using 3G and 4G networks. However, owing to the highly dynamic network topology of VANETs, such services is highly susceptible to poor performance. In such a real-time scenario, a requesting vehicle may not be able to guarantee the video quality using its own wireless interface independently. Thus, motivated to provide a quality video stream, we propose a Fleet Based video Retrieval (FBR) scheme that allows the requesting vehicle to download the H.264/SVC encoded video stream in collaboration with its 1-hop neighbors. The collaborators are selected considering the multiple characteristics of each neighboring vehicle, i.e., 1-hop distance, link duration, velocity, and the available cellular bandwidth. The selected vehicles download the video data using their wireless link and then forward it to the requested vehicle through a Dedicated Short-Range Communication (DSRC) protocol. For comparison, we first evaluate our FBR with 3G equipped vehicles and secondly, we designed FBR to work in 4G/LTE environment. Through simulations, we found that FBR has outperformed the recently proposed scheme in terms of receiving video quality and video-flow handling.

Frequent topological changes due to high mobility is one of the main issues in Vehicular Ad-hoc NETworks (VANETs). In this paper, we model transmission probabilities of 802.11p for VANETs and effect of these probabilities on average transmission time. To evaluate the effect of these probabilities of VANETs in routing protocols, we select Dynamic Source Routing (DSR), Fish-eye State Routing (FSR) and Optimized Link State Routing (OLSR). Framework of these protocols with respect to their packet cost is also presented in this work. A novel contribution of this work is enhancement of chosen protocols to obtain efficient behavior. Extensive simulation work is done to prove and compare the efficiency in terms of high throughput of enhanced versions with default versions of protocols in NS-2. For this comparison, we choose three performance metrics; throughput, End-to-End Delay (E2ED) and Normalized Routing Load (NRL) in different mobilities and scalabilities. Finally, we deduce that enhanced DSR (DSR-mod) outperforms other protocols by achieving 16% more packet delivery for all scalabilities and 28% more throughput in selected mobilities than original version of DSR (DSR-orig).

In this paper, a framework for experimental parameters in which Packet Delivery Ratio (PDR), effect of link duration over End-to-End Delay (E2ED) and Normalized Routing Overhead (NRO) in terms of control packets is analyzed and modeled for Mobile Ad-Hoc NETworks (MANETs) and Vehicular Ad-Hoc NETworks (VANETs) with the assumption that nodes (vehicles) are sparsely moving in two different road. Moreover, this paper contributes the performance comparison of one Proactive Routing Protocol; Destination Sequenced Distance vector (DSDV) and two reactive protocols; DYnamic Source Routing (DSR) and DYnamic MANET On-Demand (DYMO). A novel contribution of this work is enhancements in default versions of selected routing protocols. Three performance parameters; PDR, E2ED and NRO with varying scalabilities are measured to analyze the performance of selected routing protocols with their original and enhanced versions. From extensive simulations, it is observed that DSR outperforms among all three protocols at the cost of delay. NS-2 simulator is used for simulation with TwoRayGround propagation model to evaluate analytical results.

This paper proposes an optimum mechanism to select appropriate gateway based on multiple node metrics: remaining energy, mobility, and number of hops, for interconnected mobile ad hoc network (MANET) with infrastructured network. To choose an optimum gateway node, we use the multiple criteria decision making (MCDM) method called simple additive weighting (SAW) to outrank the optimum gateway node. SAW method calculates the weights of gateway node by considering these three metrics. Node with the highest weight will be selected as a gateway. Simulation results show that our scheme can improve throughput performance, gateway lifetime, and packet delivery ratio of MANET and infrastructured network.