We propose SKYLOCK, a unified multi-layer authentication and key management framework for 6G non-terrestrial networks (NTNs) spanning space–air–ground segments. SKYLOCK comprises a quantum-resistant authentication protocol (QRAP), a hierarchical key management system (HKMS), and a fast handover security protocol (FHSP) that jointly deliver low-latency, formally reasoned security under NTN constraints. Evaluation indicates up to 42% lower authentication latency in LEO, 38% in MEO, and 42% in GEO compared to 5G-AKA, and 56% lower handover latency with 79% fewer packet losses. We provide a threat model, formal properties, and implementation guidance for standardisation and deployment.

Blockchain stores data immutably across distributed network servers, creating replicated copies. Each block is linked in sequence, so data searches require inspection from the newest block back to the first. This linear search method becomes prohibitively expensive for any large blockchain. To alleviate this cost, the Enhanced Append-only Skip List (EASL) indexing technique was designed to introduce binary search while being efficiently stored within a blockchain. However, the append-only skip list-based EASL suffered from a bias toward more recent data, resulting in an increasing search time cost for more historical indexed data. Consequently, a ternary tree was introduced to EASL by adapting the known inverse relationship between a skip list and a binary tree. The Ternary Tree Enhanced Append-only Skip List (TEASL) indexing technique produced more consistent data retrieval times than EASL, and as a composite index, was now available on the blockchain client device. Thus, it introduces efficient access to blockchain data where maintaining a local blockchain copy is not feasible. However, smart cards and microcontroller-based Internet of Things (IoT) devices with limited bandwidth require extremely low compute and storage use. To address this, we propose TEASLe, a flexible index class optimized for such constraints. We empirically validate TEASLe’s efficiency on smart cards and constrained IoT devices, using a real-world public blockchain, demonstrating practical blockchain data integration. The code supporting this indexing technique is publicly available on GitHub {https://github.com//jarednewell/TEASLe/}.

Information stored via blockchain’s incremental method enforces data immutability on distributed computer storage. However, storing data incrementally in blocks introduces a temporal constraint, namely, a linear time cost to search for historical blockchain data. Indexing computer data is a well-known technique to quicken retrieval times, with successful blockchain-specific techniques being derived from the skip list data structure, because redundant and obsolete index fragments are not stored. However, upon evaluation of these append-only deterministic skip list indexes, it is evident that a most recent version bias is introduced. Once again, it impairs the retrieval of historical blockchain data. To address this problem, we introduce the Ternary Tree Enhanced Append-only Skip List (TEASL) index. This composite index exploits the known inverse relationship between a binary tree and a deterministic skip list and extends this relationship to utilise a ternary tree. Empirical analysis and comparison of existing indexing techniques demonstrates enhanced search query traversal performance, regardless of where the data is located in the blockchain. Furthermore, TEASL does not introduce any additional storage cost. The TEASL indexing technique is proven to be the fastest append-only skip list-based indexing technique for blockchain. The code supporting this indexing technique is publicly available on GitHub https://github.com/jarednewell/TEASL/ . • TEASL’s transformational algorithm constructs a ternary tree search index from an append-only skip list, elaborating on the known inverse relationship between a binary tree and a deterministic skip list and applies it to a ternary search tree. • The ternary tree is constructed from the append-only skip list data structure sequentially and in real-time with blockchain block updates. Moreover, no index data duplication exists. That is, all previously constructed updates are required to form a complete index. • The TEASL index has superior search traversal performance which is 85.71 % faster than the current fastest append-only skip list indexing technique for blockchain and is equally as storage-efficient. TEASL has more constant blockchain data retrieval times while adapting by remaining optimal and current as the blockchain increases in size. • TEASL is suitable for a distributed client-server model which is blockchain. In this distributed configuration, index traversal occurs in two steps: First, the local ternary tree in the TEASL index is accessed on the client device, thus reducing the search space size. Second, data is retrieved from the blockchain server via the EASL index, refined by the results of the ternary tree. This reduces the cost of data retrieval via the network. • The TEASL construction algorithm allows the granularity of the ternary tree to be increased or reduced, allowing a trade-off between TEASL’s query performance times and the storage performance. This allows for the adaptation to different resource-constrained client devices, such as IoT hardware types.

Vision-Language models (VLMs) like Contrastive Language-Image Pre-Training (CLIP) have been extensively adapted for few-shot classification. Most few-shot methods rely on randomly selected samples from the dataset. However, since only a few samples are used, the sample selection process can significantly impact the performance of the downstream classification task. In this work, we propose a reinforcement learning-based policy gradient technique that employs a diversity and informativeness-based reward function to optimise the sample selection process. We evaluate various sample selection techniques based on downstream classification accuracy across three benchmark datasets, where the proposed method demonstrates promising results.

Network Intrusion Detection Systems face increasing challenges from sophisticated evasion techniques that manipulate traffic timing patterns. This paper presents a Temporal Evasion Generation Algorithm (TEGA) for creating adversarial examples by exploiting temporal vulnerabilities, and an Adaptive Temporal Defense System (ATDS) to counter these attacks. We formalize temporal evasion mathematically and evaluate both systems using the CIC-IDS2018 dataset. TEGA achieves evasion success rates of 72.4%, significantly outperforming conventional techniques such as standard delay injection (45.6%) and burst pattern manipulation (53.2%). Conversely, ATDS demonstrates robust defense capabilities, with detection accuracy reaching 95% after adaptation and false positive rates reduced to 1.5%. Our comparative analysis reveals that sequence-based feature extraction combined with SVM classification provides optimal resilience against temporal evasion. The adaptive framework rapidly responds to new attack patterns, typically requiring only 2-3 update cycles to achieve over 90% detection accuracy. This research contributes to network security by addressing an emerging attack vector while offering promising directions for developing next-generation intrusion detection systems.

This paper proposes a novel approach to adversarial attacks against machine learning-based network intrusion detection systems (NIDS). Unlike conventional methods that apply feature-space perturbations, we implement realistic problem-space attacks by manipulating network traffic through advanced packet modification techniques using the Scapy framework. Our methodology targets multiple attack vectors, including recon-naissance, data exfiltration, and command and control communications. Experiments evaluate four popular machine learning models (GBDT, DNN, Random Forest, and SVM) against both legitimate and adversarially manipulated traffic. Results demonstrate significant detection performance degradation across all models, with evasion rates reaching 72% for reconnaissance traffic. We observe strong transferability of adversarial examples between different model architectures and analyze the underlying mechanisms through feature space visualization. Our findings highlight fundamental vulnerabilities in ML-based intrusion detection and emphasize the need for defensive techniques specifically addressing realistic traffic manipulations rather than abstract feature perturbations.

This research paper presents an attack framework between NIDS and CAN-based intrusion detection that secures vehicle-to-everything (V2X) environments. The connection of vehicles to external networks exposes critical security concerns because public network attacks can penetrate into vehicle systems. Our methodology performs three steps to enable crossdomain attacks through network security domain and automotive security domain integration: (1) ensemble feature selection discovering common vulnerabilities, (2) V2X-Aware calibrated gradient descent that follows protocol constraints, and (3) CAN-Deep PackGen produces valid network-based CAN frames. Our attacks achieve evasion rates of up to 87.9% during experiments on KDDCup99 and UNSW-NB15 for NIDS and CICIoV2024 for CAN IDS. The transfer success rate for timing-related features surpasses 85% due to their vulnerability. The attack success rate receives 24.7% improvement from ensemble feature selection which exhibits strong correlation with feature importance in predicting attack transferability. The diagnostic CAN messages demonstrate the maximum vulnerability to attacks by achieving a$\text{9 2. 3 \%}$evasion rate. These findings highlight critical security implications for connected vehicles and demonstrate the need for cross-domain defensive strategies in automotive cybersecurity, providing a foundation for understanding and mitigating adversarial threats in interconnected transportation.

With the increasing adoption of Vehicle-toEverything (V2X) communication in next-generation intelligent transportation systems, ensuring cybersecurity in vehicular networks has become a critical challenge. Machine Learning (ML)-based Intrusion Detection Systems (IDS) have been widely proposed to detect anomalous and malicious activities in V2X networks. However, these systems are vulnerable to adversarial attacks, where an attacker subtly manipulates input data to evade detection. This study investigates the impact of adversarial attacks on ML-based IDS using the CICIoV2024 dataset, which includes diverse cyber threats targeting V2X networks. We evaluate Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Carlini & Wagner (CW), and DeepFool attacks against ML models such as Random Forest (RF), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Transformer-based IDS. Our results demonstrate that adversarial attacks significantly degrade IDS performance, reducing detection accuracy by up to 40%, thereby exposing critical vulnerabilities in V2X cybersecurity frameworks. Furthermore, we assess defence mechanisms, including adversarial training and feature squeezing, and analyze their effectiveness in mitigating adversarial threats. The findings highlight the urgent need for robust, adversarially-aware IDS to safeguard V2X networks from evolving cyber threats. Future research directions include realworld deployment, federated learning-based IDS, and adversarially robust AI architectures for V2X security.

Connected and Autonomous Vehicles (CAVs) rely on vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication for navigation and collision avoidance, yet ensuring the authenticity and integrity of shared trajectory data is a significant challenge. Traditional trust models, which depend on centralised or pre-established trust, are susceptible to cyberattacks, such as trajectory spoofing and misinformation propagation. This paper introduces a Zero Trust-based Lightweight Trajectory Endorsement Model (ZT-LTE) that combines Zero Trust principles with a lightweight verification mechanism, employing cryptographic identity verification, Bloom Filter-based trajectory lookup, and a federated trust consensus mechanism to ensure reliable trajectory endorsement in real time. Simulation results indicate that ZT-LTE reduces unauthorised trajectory endorsements by 85% and improves real-time threat detection by 72% compared to traditional models, based on a comparison of the False Acceptance Rate and detection accuracy using a dataset of 10,000 simulated trajectory transactions. These findings advance the development of secure, efficient, and scalable communication frameworks for CAVs.

The weakness of blockchain is widely recognised as the linear, temporal cost required for retrieving data due to the sequential structure of data blocks. To address this, conventional approaches have relied on database indexing techniques applied to each individual replica copy of a blockchain . However, this only partially addresses the problem, because if the index is not distributed it is not available for devices in the blockchain network. If an index is to be incorporated and distributed within blockchain, the unique attribute of immutability necessitates a more innovative approach. To that end, we propose an Enhanced Append-only Skip List (EASL). This specialised indexing technique utilises binary search with skip lists in blockchain, resulting in a sublinear cost for data retrieval. The EASL indexing technique is maintained by each newly appended blockchain block and offers enhanced readability and robustness using an explicitly recorded index structure. Our proposed technique is 42% more efficient in computing and 60% more efficient in storage consumption than its predecessor, the Deterministic Append-only Skip List (DASL) indexing technique. This is achieved through agile data retrieval, resulting in energy cost savings from less computational effort to maintain the index, and less network bandwidth to retrieve blockchain data. The code for the proposed technique is publicly available on GitHub { https://github.com/jarednewell/EASL/ }, to expedite future research and encourage the practical application of this effectual data index.

Network Intrusion Detection Systems (NIDS) are increasingly vulnerable to sophisticated packet-mutation attacks that evade traditional detection methods. This paper presents a runtime adaptive machine-learning strategy to combat such adversarial attacks. We introduce an Adaptive Layered Mutation Algorithm (ALMA) for generating advanced adversarial examples and a runtime adaptive learning framework for real-time detection and response. Our approach integrates these components to create a robust, self-evolving NIDS. Experiments comparing various feature extractors and machine learning classifiers demonstrate that our adaptive approach achieves up to $\mathbf{9 8 \%}$ detection accuracy, significantly improving the identification of mutated packets over static models. The integrated system rapidly adapts to new attack patterns, achieving over $\mathbf{9 0 \%}$ detection accuracy for novel attacks within 2-3 update cycles. This research contributes to network security by presenting an adaptive, high-performance approach to intrusion detection that effectively addresses challenges posed by evolving packet-mutation attacks, offering promising directions for next-generation NIDS development.

Integrating autonomous vehicles (AVs) within our transportation ecosystem presents exciting possibilities and introduces intricate challenges. Systemic failures due to cyberattacks on AVs could have far-reaching consequences, impacting safety and financial stability. This paper emphasizes the interdependence of stakeholders, particularly within the financial and insurance sectors. We propose a proactive approach, focusing on three objectives: (1) Systemic Risk Mitigation, (2) Premortem Analysis with a focus on cascading failures, and (3) Integration of Cybersecurity Standards tailored for autonomous electric vehicles (EVs). By addressing EV-specific vulnerabilities in charging infrastructure and telematics systems, we aim to significantly enhance the security of safety-critical cyber-physical systems, strengthen the resilience of interconnected industries, and contribute to broader economic stability.

Spatial crowdsourcing (SC) systems have emerged as an advanced crowdsourcing paradigm to revolutionise the efficient development of smart city services. SC engages participants and their sensitive data to accomplish spatiotemporal tasks on platforms. However, revealing sensitive data in SC for smart cities exacerbates cybersecurity and privacy concerns, especially Membership Inference Attacks (MIA). To address the problems, this research proposes a Federated Learning (FL) and Incremental Learning (IL) based framework in SC that integrates advanced privacy-preserving techniques. By leveraging FL and adaptive differential privacy, sensitive data remains in decentralised devices while local models are trained without exchanging raw data to a server. We integrate additive secret sharing, a secure multi-party computation technique to protect data during transmission and aggregation. IL enhances the framework using a generative replay approach to ensure continuous adaptation to new data without forgetting existing knowledge to overcome catastrophic forgetting. We broadly evaluate our work against MIA and catastrophic forgetting using Yelp datasets. Compared with other baseline approaches, our experimental results demonstrate that the proposed framework significantly mitigates the risk of MIAs by around 50% and improves forgetting accuracy by up to 13%, thereby providing robust privacy-preserving mechanisms.

In lightweight block cipher design, generating permutation boxes (P-boxes) is critical to security and efficiency. This paper introduces an innovative approach to P-box generation by integrating nonlinear feedback shift registers (NFSRs) to enhance cryptographic strength. NFSRs are known for their capacity to generate obscure and unpredictable sequences, making them a promising candidate for improving P-box design. This research investigates the intricacies of this novel method, highlighting its potential benefits and implementation challenges. The proposed NFSR-based P-box generation method offers improved diffusion properties, presenting an attractive option for creating secure and efficient lightweight block ciphers.

One of the biggest criticisms of the Bitcoin Core blockchain technology lies in its consensus algorithm, known as Proof-of-Work (PoW). This compute-intensive consensus algorithm is resulting in an ever-increasing electrical cost per transaction, this is compounded by its data storage limitations. Consequently, this phenomenon determines the maximum limit in the transaction processing rate. This limitation is causing many industries to refrain from using Blockchain technology. This paper proposes a Proof-of-Enough-Work (PoEW) consensus algorithm for a BC blockchain configuration to get rid of this obstacle. We present parallel block processing through the reallocation of computing resources, specifically used to add blocks to the blockchain, also called mining. This is explicitly done with an extension to the key BC mining parameters, called the block difficulty or the amount of computing effort needed to process a block. The introduction of an efficient use of computing resources or effort significantly increases the transaction processing rate.Through simulations we demonstrate a significant increase in transaction processing rate compared to conventional PoW. It produces transaction processing comparable to centralised transaction networks or a high transaction throughput solution. Moreover, the proposed consensus algorithm reduces the cost per transaction in terms of electricity usage.

An efficient logical topology helps wireless sensor networks (WSNs) minimizing different constraints. For large-scale WSNs, chain oriented logical topologies are shown to be more energy conservative than other logical topologies. Chain construction is the main challenge to create a chain oriented logical topology. In this paper, we propose a chain construction scheme, which creates several chains for the topology using Voronoi tessellation. The main idea of this scheme is to divide the target field into a number of small areas (i.e., Voronoi cells) so that in each cell, a chain is constructed. To construct a chain in a Voronoi cell, we use a protocol, which guarantees the summation of square to the distances would be the lowest. We compare our chain construction algorithm with other similar algorithms. Simulation results show that proposed Voronoi diagram based algorithm saves more energy, lengthens lifetime of the network, and reduces data collection latency.

Energy consumption and lifetime of networks are the two most important constraints for wireless sensor networks (WSNs). Moreover, for perceiving events with respect to time, WSNs need to maintain coverage quality. This paper focuses on coverage problem with a number of sensors selected from deployed sensor nodes, which develop chain oriented communication. To mitigate the constraints of sensor nodes researchers employ a variety of logical topologies, among which chain oriented topology is one of the significant contender. Moreover, choosing only a selected number of sensors, instead of choosing all of them saves more energy and lengthens lifetime of the network. The main criterion to select a sensor node we propose in this paper is the use of shared sensing region of a sensor node with its neighbours. Simulation results show that our protocol saves a large number of sensor nodes while maintaining significant amount of coverage ratio.

In this paper we present a new token based protocol for group mutual exclusion in distributed systems. The protocol uses one single token to allow multiple processes to enter the critical section for a common session. One of the significant characteristics of the protocol is - concurrency, throughput and waiting time can be regulated adjusting the time period for which a session is declared. The minimum and the maximum number of messages to enter the CS is 0 and ( n + 2) respectively where n is the total number of processes in the system. Moreover, simulation results show that the protocol, on average case, considerably reduces the number of messages per entry to the CS and also requires much lower waiting times. The maximum concurrency the protocol supports is n. The protocol also ensures no starvation in the system. Furthermore, this algorithm works out for the Extended Group Mutual Exclusion problem as well.