The integration of blockchain into industrial environments promises secure and verifiable data exchange; however, existing permissioned blockchain (PBC) frameworks, such as Hyperledger Fabric and Quorum, impose overheads that are unsuitable for resource-constrained systems. This article introduces LowCapChain (LCC), a lightweight PBC designed for manufacturing nodes, such as programmable logic controllers, robotic arms, and smart sensors. LCC integrates Merkle ledger compression, elliptic-curve cryptography-based proof-of-membership, and delegated fault-tolerant consensus to enable secure operations without full ledger replication. Implemented in a robotic metal stamping facility using Raspberry Pi edge nodes, LCC achieves 964 transactions per second with 108 ms consensus latency and memory usage below 55 MB. Compared with Hyperledger Fabric, LCC reduces mean consensus latency by 61% and cryptographic overhead by up to 81%. Compared with Quorum, the reductions are 54% and 76%, respectively. Scalability tests confirmed near-linear throughput growth across 10–200 nodes, and fault tolerance experiments verified block finalization under validator failure. These findings establish LCC as an efficient architecture for embedded industrial systems, offering a pathway toward scalable Industry 4.0 adoption with energy savings inferred from reduced CPU cycles rather than directly measured power.

As next-generation communication networks increasingly rely on AI-driven automation, ensuring robust and secure intrusion detection becomes critical, especially under limited labeled data. In this context, we introduceMulti-Space Prototypical Learning(MSPL), a few-shot intrusion detection framework that improves prototype-based classification by fusing complementary metric-induced spaces (Euclidean, Cosine, Chebyshev, and Wasserstein) via a constrained weighting mechanism. MSPL further enhances stability through Polyak-averaged prototype generation and balanced episodic training to mitigate class imbalance across diverse attack categories. In a few-shot setting with as few as 200 training samples, MSPL consistently outperforms single-metric baselines across three benchmarks: on CICEVSE Network2024, AUPRC improves from 0.3719 to 0.7324 and F1 increases from 0.4194 to 0.8502; on CICIDS2017, AUPRC improves from 0.4319 to 0.4799; and on CICIoV2024, AUPRC improves from 0.5881 to 0.6144. These results demonstrate that multi-space metric fusion yields more discriminative and robust representations for detecting rare and emerging attacks in intelligent network environments.

Internet of Things (IoT) devices are increasingly susceptible to botnet threats, with Mirai-based attacks posing significant security challenges. To address these concerns, we present a lightweight anomaly detection model based on a Gaussian Mixture Model (GMM), tailored for detecting Mirai botnet traffic. By leveraging anomaly detection techniques, our approach identifies malicious activity with optimized latency and computational efficiency, making it suitable for resource-constrained IoT environments. Trained specifically on Mirai-related traffic, the proposed model achieves comparable detection accuracy while significantly improving latency compared to traditional decision tree-based models. These results underscore the effectiveness of the GMM approach in balancing detection performance and real-time responsiveness for IoT applications.

The rapid expansion of IoT and edge networks has increased vulnerability to security threats, notably Distributed Denial-of-Service (DDoS) attacks, which target the application layer and can overwhelm networks. This paper introduces a novel two-stage DDoS detection pipeline for IoT and edge networks that utilizes large language model (LLM) embeddings for textual protocol fields along with numeric features like TCP handshake metrics. The first stage accurately identifies traffic as Normal or DDoS, while the second stage classifies confirmed DDoS flows into specific types (UDP, ICMP, TCP, HTTP), correcting any initial false positives. Comprehensive testing on a specialized IoT dataset demonstrated perfect detection rates of 100% in the first stage and 99.91% in subclass classification, highlighting the effectiveness of combining LLM-based textual data with traditional numeric indicators for advanced intrusion detection.

The healthcare industry has experienced remarkable growth in data generation and revenue, making it the most rapidly expanding sector. To enhance security measures, our study explores the adoption of blockchain technology, leveraging its various aspects such as decentralization, consortiums, Ethereum, and Hyperledger. This paper proposes a Secured Healthcare Framework utilizing blockchain technology, with a focus on securing Electronic Health Records (EHR) through smart contracts. This approach ensures end-to-end security and non-repudiation. By integrating IoT devices such as RFID and Arduino, our method not only enhances security but also stream-lines data management within healthcare settings. The results demonstrate significant efficacy in the secure transmission and management of patient medical records. Moreover, experimental findings indicate an 89.88 % difference in latency between the MetaMask and Ganache environments, and a 13.04 % difference in gas usage for transactions in the Remix and Ganache environments. These findings highlight the potential of our proposed framework to improve security and efficiency in healthcare data management.

Predicting oil prices is challenging due to factors beyond supply and demand, such as geopolitical events and public sentiment. Traditional models often fail to integrate real-time sentiment data, a growing market influence driven by social media. This study introduces a novel approach to oil price forecasting by combining social media sentiment from X (formerly Twitter) with advanced natural language processing and a long short-term memory (LSTM) neural network. Analyzing sentiment from millions of tweets, we capture public sentiment’s potential impact on West Texas Intermediate crude prices. Our LSTM-based model, designed to handle sequential data, achieved an R-squared of 0.831121, outperforming traditional econometric models and demonstrating a strong correlation between social sentiment and price trends. These results highlight the value of the social media sentiment in enhancing forecasting accuracy, offering actionable insights for investors and policymakers. Future research could extend this method to other commodities, further advancing economic forecasting through social sentiment analysis.

Distributed Denial of Service (DDoS) attacks remain a persistent and formidable threat, often overwhelming targets by saturating network bandwidth or exhausting server resources with massive volumes of malicious traffic. Traditional detection methods typically rely on signature-based approaches or large labeled training sets, posing challenges in rapidly changing attack landscapes where novel or variant DDoS vectors emerge frequently. To address this gap, we propose ProtoMAML, a hybrid meta-learning framework that integrates Prototypical Networks and Model-Agnostic Meta-Learning (MAML) to facilitate robust few-shot DDoS detection. By combining prototype-based clustering with fast, gradient-driven adaptation, ProtoMAML can accurately detect new attacks from only a handful of labeled flows per class. Experiments on a large-scale flow dataset (nearly half a million flows) demonstrate that ProtoMAML achieves a recall of up to 99.4% under severe data scarcity. Extended discussions on computational overhead, adversarial resilience, and real-world deployment provide insights into how meta-learning can offer a powerful, agile defense against evolving cyber threats.

Glaucoma, projected to impact over 110 million individuals by 2040, remains a significant challenge in low-resource settings due to limited access to diagnostic tools and physicians, delaying early detection. Automated glaucoma diagnosis systems have emerged as potentially scalable solutions but often rely on single-modality imaging and lack integration with real-world patient interactions. To bridge these gaps, DeepRet, an AI-based system, leverages multimodal data—retinal imaging, age, and intraocular pressure measurements—to achieve 96.34% accuracy, a 93% AUROC, and a 17.30% improvement over traditional methods. Designed for low-resource environments, DeepRet features an affordable, portable retinal imager with a manufacturing cost under $200, 3D printed materials, and low-cost electronics. Its intuitive design minimizes training requirements for health workers while offering real-time, patient-friendly feedback, enhancing accessibility and the patient experience.

Distributed Denial of Service (DDoS) attacks continue to escalate in both frequency and sophistication, often overwhelming critical network infrastructures. While deep learning methods excel at recognizing malicious patterns, their lack of transparency undermines trust and hampers effective mitigation. This paper introduces a unified, on-premise pipeline that integrates an advanced flow based attack classifier with a local large language model (LLM) to deliver explainable, real-time DDoS defense. The proposed approach detects threats at the flow level, rapidly fags suspicious traffic, and then generates human-readable analyses and device specific countermeasures ranging from firewall rules to intrusion prevention system signatures all without transmitting data of-site. Through comprehensive testing on diverse, large scale network traces, we demonstrate that this framework not only achieves near-perfect detection accuracy but also considerably reduces operational costs and privacy risks associated with external cloud services. Furthermore, evaluators confirm the clarity and correctness of the automatically generated mitigation strategies, highlighting the system’s practicality in enterprise environments. Overall, our results validate on-premise, LLM-enhanced DDoS defense as a robust, transparent, and economical solution for safeguarding modern network ecosystems.

Creating an immutable repository for vital robot and environmental data, ensuring long-term accessibility, and functioning in the absence of GPS or mapping are crucial for visual homing navigation systems. We focus on the intersection of blockchain and robotics, particularly in visual homing. Our research involves an in-depth analysis of various blockchain consensus mechanisms, highlighting their suitability for visual homing applications. The heart of blockchain functionality lies in its consensus mechanism, which facilitates agreement among network nodes. In our first study part, we conduct a comprehensive comparative analysis of key consensus algorithms, emphasizing visual homing’s decentralization, fault tolerance, latency, and throughput requirements. This analysis serves as a valuable reference for researchers and developers, emphasizing the importance of aligning the chosen consensus mechanism with specific blockchain application needs. The second part of our work involves extensive experiments exploring the connection between blockchain and visual homing. We assess prominent consensus mechanisms like Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Proof of Authority (PoA) within a virtual environment in Gazebo, leveraging wide area visual navigation (WAVN). Our research implementation is grounded in the ROS framework and the Gazebo simulation environment.

Detecting unknown cyberattacks remains an open research problem and a significant challenge for the research community and the security industry. This article tackles the detection of unknown cybersecurity attacks in the Internet of Things (IoT) and traditional networks by categorizing them into two types: entirely new classes of unknown attacks (type-A) and unknown attacks within already known classes (type-B). To address this, we propose a novel multi-stage, multi-layer zero trust architecture for an intrusion detection system (IDS), uniquely designed to handle these attack types. The architecture employs a hybrid methodology that combines two supervised and one unsupervised learning stages in a funnel-like design, significantly advancing current detection capabilities. A key innovation is the layered filtering mechanism, leveraging type-A and type-B attack concepts to systematically classify traffic as malicious unless proven otherwise. Using four benchmark datasets, the proposed system demonstrates significant improvements in accuracy, recall, and error classification rates for unknown attacks, achieving an average accuracy and recall ranging between 88% and 95%. This work offers a robust, scalable framework for enhancing cybersecurity in diverse network environments.

Detecting and mitigating Denial-of-Service (DoS) attacks is crucial for ensuring the availability and security of online services. While various machine learning (ML) models have been utilized for DoS attack detection, there is a need for innovative approaches to improving their performance, especially for the more challenging multi-class detection problem. In this article, we propose adopting a cutting-edge approach called Combinatorial Fusion Analysis (CFA), which leverages a recently developed framework to combine multiple ML models for improved DoS attack detection. Our methodology involves advanced score combination, rank combination, weighted combination techniques, and the diversity strength of scoring systems. Through rigorous performance evaluations, we showcase the efficacy of the combinatorial fusion approach. Our evaluations encompass key metrics such as detection precision, recall, and F1-score, providing comprehensive insights into the interpretability and effectiveness of our approach. We highlight the challenge faced by individual models in classifying low-profiled attacks, while excelling in other attack types. To overcome this limitation, model fusion techniques were used to create a comprehensive model capable of addressing both low-profiled attacks and other traffic types. Furthermore, our findings highlight the potential of this approach for enhancing DoS attack detection capabilities and contributing to the development of more robust defense mechanisms.

Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. In this paper, we introduce a new deep learning-based technique for detecting DDoS attacks, a paramount cyber-security challenge with evolving complexity and scale. Specifically, we propose a new dual-space prototypical network that leverages a unique dual-space loss function to enhance detection accuracy for various attack patterns through geometric and angular similarity measures. This approach capitalizes on the strengths of representation learning within the latent space (a lower-dimensional representation of data that captures complex patterns for machine learning analysis), improving the model’s adaptability and sensitivity towards varying DDoS attack vectors. Our comprehensive evaluation spans multiple training environments, including offline training, simulated online training, and prototypical network scenarios, to validate the model’s robustness under diverse data abundance and scarcity conditions. The Multilayer Perceptron (MLP) with Attention, trained with our dual-space prototypical design over a reduced training set, achieves an average accuracy of 94.85% and an F1-Score of 94.71% across our tests, showcasing its effectiveness in dynamic and constrained real-world scenarios.

This demo paper examines the susceptibility of Federated Learning (FL) systems to targeted data poisoning attacks, presenting a novel system for visualizing and mitigating such threats. We simulate targeted data poisoning attacks via label flipping and analyze the impact on model performance, employing a five-component system that includes Simulation and Data Generation, Data Collection and Upload, User-friendly Interface, Analysis and Insight, and Advisory System. Observations from three demo modules: label manipulation, attack timing, and malicious attack availability, and two analysis components: utility and analytical behavior of local model updates highlight the risks to system integrity and offer insight into the resilience of FL systems. The demo is available at https://github.com/CathyXueqingZhang/DataPoisoningVis.

Smart tourist destinations are increasingly using technology to manage interactions with tourists. Chatbots, supported by artificial intelligence and natural language processing, have demonstrated greater capability and effectiveness in various conversational scenarios, offering assistance to tourists before, during, and after their visits. However, the effectiveness of chatbots can be improved, as these applications only cover some tourism functionalities. This article analyzes chatbots, accompanied by an exhaustive study of their evolution. We used the Web of Science and Scopus databases to gather relevant papers using a specific search query. This study provides a comprehensive overview of advances and trends in tourism chatbots over the past ten years (2013-2023), highlighting current failings and opportunities for future developments.

The rapid growth of the stock market has attracted many investors due to its profit potential. However, accurately predicting stock prices is challenging due to the complexity and volatility of financial markets, especially in the cryptocurrency sector. This study presents a machine learning approach to predict cryptocurrency prices using technical indicators like Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) with an XGBoost regressor model. Focusing on Bitcoin’s closing prices, we evaluate the model’s performance through simulations, demonstrating promising results that suggest its potential to assist cryptocurrency traders and investors in dynamic market conditions.

This work addresses the challenge of classifying visually similar bird species, a task complicated by subtle interspecies variations. We focused on ten bird species, assembling a dataset of approximately 8000 images from Google Images. These species were specifically chosen for their high degree of similarity, presenting a unique challenge for classification algorithms. To enhance our dataset and improve classification accuracy, we employed Generative adversarial networks (GANs), a state-of-the-art generative adversarial network, to augment our original dataset with synthetic yet realistic images. This augmentation aimed to provide a more prosperous, diverse training environment for our deep learning model. Subsequently, we developed a specialized multi-classification model tailored to recognize and differentiate these closely related bird species. Integrating GANs like StyleGAN3-augmented data into our training process represents a novel approach to ecological image analysis, potentially setting a new standard for accuracy and efficiency in classifying highly similar species. This study demonstrates the effectiveness of advanced generative models in complex classification tasks and contributes a valuable methodology to ecological research and species identification.

This study delves into the challenge of classifying visually similar bird species, an area of significant interest in the field of fine-grained image classification. Utilizing a substantial dataset comprising images of ten bird species which was selected carefully to challenge the model to classify species of extreme similarities. To achieve this, we were keen to collect the data with subtle visual dissimilarities and of different positions taken for these birds. The research explores the potential of deep learning techniques to differentiate species based on subtle inter-species variations. This task is particularly demanding due to the minimal yet critical differences between these closely related species. Our research leveraged a unique deep learning model using convolutional neural networks (CNNs) to accurately classify birds with minimal visual differences. This innovative approach marks a significant step forward in machine learning for biological classification, with implications for biodiversity and ecological conservation. Our study demonstrates the effectiveness of our deep learning model in accurately classifying bird species, showcasing the potential of advanced techniques in complex Classification tasks. This research enhances the use of computational methods in biodiversity and ecological conservation. Additionally, it underscores the importance of birds as indicators of environmental changes, such as climate shifts, aiding in early detection of potential ecological issues.

Denial of Service (DoS) attacks pose a significant threat to AI systems security, causing substantial financial losses and downtime. However, AI systems’ high computational demands, dynamic behavior, and data variability make monitoring and detecting DoS attacks challenging. Nowadays, statistical and machine learning (ML)-based DoS classification and detection approaches utilize a broad range of feature selection mechanisms to select a feature subset from networking traffic datasets. Feature selection is critical in enhancing the overall model performance and attack detection accuracy while reducing the training time. In this paper, we investigate the importance of feature selection in improving ML-based detection of DoS attacks. Specifically, we explore feature contribution to the overall components in DoS traffic datasets by utilizing statistical analysis and feature engineering approaches. Our experimental findings demonstrate the usefulness of the thorough statistical analysis of DoS traffic and feature engineering in understanding the behavior of the attack and identifying the best feature selection for ML-based DoS classification and detection.

This article outlines the architecture of autonomous driving and related complementary frameworks from the perspective of human comfort. The technical elements for measuring autonomous vehicle (AV) user comfort and psychoanalysis are listed here. At the same time, this article introduces the technology related to the structure of automatic driving and the reaction time of automatic driving. We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies. The function of the sensor is affected by various factors. Since the sensor of automatic driving mainly senses the environment around a vehicle, including “the weather” which introduces the challenges and limitations of second-hand sensors in AVs under different weather conditions. The comfort and safety of autonomous driving are also factors that affect the development of autonomous driving technologies. This article further analyzes the impact of autonomous driving on the user’s physical and psychological states and how the comfort factors of AVs affect the automotive market. Also, part of our focus is on the benefits and shortcomings of autonomous driving. The goal is to present an exhaustive overview of the most relevant technical matters to help researchers and application developers comprehend the different comfort factors and systems of autonomous driving. Finally, we provide detailed automated driving comfort use cases to illustrate the comfort-related issues of autonomous driving. Then, we provide implications and insights for the future of autonomous driving.

This paper introduces a novel approach to GPS-denied visual navigation of a robot team over a wide (i.e., out of line of sight) area which we call WAVN (Wide Area Visual Navigation). Application domains include small-scale precision agriculture as well as exploration and surveillance. The proposed approach requires no exploration or map generation, merging, and updating, some of the most computationally intensive aspects of multi-robot navigation, especially in dynamic environments and for long-term deployments. In contrast, we extend the visual homing paradigm to leverage visual information from the entire team to allow a robot to home to a distant location. Since it only employs the latest imagery, the approach can be resilient to the current state of the environment. WAVN requires three components: identification of common landmarks between robots, a communication infrastructure, and an algorithm to find a sequence of common landmarks to navigate to a goal. The principal contribution of this paper is the navigation algorithm in addition to simulation and physical robot results characterizing performance. The approach is also compared to more traditional map-based approaches.

The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers has paved the way for artificial intelligence (AI) and big data in industries. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, big data, Blockchain, and 5G communications in support of smart manufacturing and the dynamical processes in modern industries. In this article, we provide a comprehensive overview of different aspects of AI and big data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective toward deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and big data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and big data solutions in smart industrial applications with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. Finally, we explore the significance of AI and big data toward Industry 4.0 applications through panoramic reviews and discussions. This work is expected to provide a baseline for future research in the domain.

To support latency-sensitive applications (e.g., emergency response) in Software-Defined Networking (SDN) environments, reliable Quality of Service (QoS) mechanisms are needed to ensure the minimization of end-to-end (E2E) latency and control response time. In this research, we design a double-queue system with feedback, named QoSP, to improve data-control communication performance and impose efficient queueing control in SDN. We further study a priority-based queueing scheme to achieve differentiated QoS provisioning via maintaining multiple OpenFlow queues with different priorities at the switch ports of a data plane. The prototype of QoSP is implemented and evaluated on the NSF-sponsored GENI testbed.

Software-defined networking (SDN) is an emerging networking technology and allows for a separation of data and control planes traffic in order to enhance the Quality of Service (QoS) for traffic and services delivery. Latency metric is regarded as a critical parameter by service providers and end users alike, where inter-link delays can be measured using end-to-end (E2E) probing packets. Moreover, to support latency-sensitive applications in SDN such as emergency response, we need a comprehensive QoS mechanism to ensure the minimization of E2E latency, the efficacious calculation of forwarding paths, and the minimization of control response time. We first distinguish between a flow’s admission latency and a flow’s packet forwarding latency. The former is decided by its controller’s response time, and the latter by its data plane queue delay. We then design a two-queue system with feedback, named QoSP, that aims to control overall latency performance in SDN through improving data-control communication performance and imposing efficient queueing control. We specifically introduce a priority-based queueing discipline in the data plane to achieve differentiated QoS provisioning via maintaining multiple OpenFlow queues with different priorities at each switch port. We implement the proposed QoSP using a Floodlight SDN controller and conduct emulation studies on the Global Environment for Networking Innovations. Our evaluation shows that QoSP can optimize the E2E delay and significantly reduce the control response time for priority traffic.

The centralized control capability of Software Defined Networking (SDN) presents a unique opportunity for enabling Quality of Service (QoS) routing. For delay sensitive traffic flows, a QoS mechanism requires efficiently computing path latency and minimizing controller's response time. At the core of the challenges is how to handle short term network state fluctuations in terms of congestion and latency while guaranteeing the end-to-end latency performance of networking services. In this paper, we present LatencySmasher, a systematic framework that considers active link latency measurements, efficient statistic estimate of network states, and fast adaptive path computation. We first implement LatencySmasher as an SDN controller application and then conduct extensive experimental studies on the Global Environment for Network Innovations (GENI), a real-world distributed network testbed. Our performance evaluation shows that the proposed framework can find optimal end-to-end paths with minimum latency and significantly reduce the control overhead.