Next-generation networks, including IoT, edge, vehicular, and 6G systems, require cryptographic schemes that not only resist attacks but also withstand channel degradation without disrupting secure data flows. This paper presents a novel adaptive encryption architecture for quantum-resilient networks that integrates real-time attack detection in quantum channels with per frame key management. A lightweight controller has been designed, which tracks the quantum bit error rate (QBER) via a sliding-window hysteresis and buffer thresholds, deciding per frame whether to derive symmetric keys from Quantum Key Distribution (QKD) or from a post-quantum cryptographic fallback, while maintaining a unified AES–GCM AEAD dataplane. Moreover, a deterministic HMAC-based key derivation function (HKDF) schedule binding eliminates key reuse across modes, retransmissions, and re-encapsulations. Experimental evaluation on image payloads shows: (i) timely QBER-spike detection with 9.5-frame latency and zero misses; (ii) full decryptability and tamper detection across both key sources; (iii) symmetric throughput of ≈ 1.1GB/s with negligible adaptation overhead; and (iv) near-ideal ciphertext metrics including entropy 8.02bits/pixel, NPCR ≈ 99.6%, and UACI ≈ 30.7%. The framework achieves secure, continuous encryption by preventing unsafe QKD use and maintaining authenticated operation under quantum-channel degradation.

ÐThis paper presents a split-trust architecture for confidential natural language processing inference that secures preprocessing inside a Trusted Execution Environment and performs classification under CKKS homomorphic encryption. The design addresses the preprocessing exposure gap present in existing encrypted NLP systems, where tokenization and embedding are performed in plaintext prior to encryption. A linear Support Vector Machine is evaluated homomorphically using logarithmic slot-rotation optimization, enabling CPU-only inference without bootstrapping. On 10,000 IMDB test samples, encrypted inference achieves 89.65% accuracy and reproduces plaintext predictions exactly across all samples. Performance analysis across batch sizes of 500 to 2000 samples shows a minimum per-sample latency of 0.01297 seconds, with ciphertext expansion of approximately 1000× relative to plaintext features. Evaluation on SST-2, Movie Reviews, and Amazon Polarity confirms identical plaintext and encrypted predictions under distribution shift. The results demonstrate that confidential NLP inference is practical when secure preprocessing is combined with depth-aware homomorphic evaluation.

Anomaly Detection (AD) is an important area of research because it helps identify outliers in data, enabling early detection of errors, fraud, and potential security breaches. Machine Learning (ML) can be utilized for distinct AD systems, and Generative Adversarial Networks (GANs) have emerged as a promising technique due to their ability to generate new data that closely resembles a given dataset, allowing for the creation of realistic images, videos, audio, text, and other types of synthetic data. This paper explores state-of-the-art approaches in AD using GANs. The paper starts by providing a comprehensive overview of ML techniques for AD, including supervised, unsupervised, and semi-supervised approaches. This survey also explores various AD approaches based on GANs and provides an application-based classification of GANs-based AD approaches in the Internet-of-Things (IoT), Industrial IoT, Digital Healthcare, Energy Management Systems, and Cellular Network domains. Moreover, the paper discusses several datasets used in evaluating the performance of GANs-based AD techniques such as BOT-IoT, TON-IoT, CIC-IoT, CIC-IDS, and NSL-KDD. These datasets serve as valuable resources for researchers and practitioners to develop and test AD systems, particularly in the context of IoT and network security. Furthermore, the paper discusses the challenges and limitations of GANs-based AD techniques and proposes future research directions to address these challenges.

With the increasing reliance on the biometric-based authentication systems, such as face recognition, in applications within the IoT and edge networks, guaranteeing proper service functionality while safeguarding individual biometric privacy has become a critical concern. However, most existing face privacy protection approaches mainly focus on preserving the machine-recognizable identity information, inadvertently compromising individual privacy. To tackle this challenge, a novel Face Privacy-Enhancing Network (FPE-Net) is proposed, which consists of two primary stages: biometric encryption and face reconstruction. Specifically, a linear encryption module is designed in the first stage for obfuscating the original identity information, which is later integrated into the depth features of the target face via an identity injector. Notably, the identity encryption process operates independently of the deep generative network, enabling greater flexibility and efficiency for key configuration. Then in the second stage, a face decoder is utilized to synthesize the photo-realistic face. Moreover, such face not only prevents cross-matching with biometric databases but also preserves recognition utility, owing to the linear encryption mechanism and loss design. Extensive quantitative and qualitative experimental results demonstrate the feasibility of FPE-Net model, which outperforms existing state-of-the-art approaches in terms of privacy protection.

The security of image data in the Internet of Things (IoT) and edge networks is crucial due to the increasing deployment of intelligent systems for real-time decision-making. Traditional encryption algorithms such as AES and RSA are computationally expensive for resource-constrained IoT devices and ineffective for large-volume image data, leading to inefficiencies in privacy-preserving distributed learning applications. To address these concerns, this paper proposes a novel Feature-Aware Chaotic Image Encryption scheme that integrates Feature-Aware Pixel Segmentation (FAPS) with Chaotic Chain Permutation and Confusion mechanisms to enhance security while maintaining efficiency. The proposed scheme consists of three stages: (1) FAPS, which extracts and reorganizes pixels based on high and low edge intensity features for correlation disruption; (2) Chaotic Chain Permutation, which employs a logistic chaotic map with SHA256-based dynamically updated keys for block-wise permutation; and (3) Chaotic chain Confusion, which utilises dynamically generated chaotic seed matrices for bitwise XOR operations. Extensive security and performance evaluations demonstrate that the proposed scheme significantly reduces pixel correlation— almost zero, achieves high entropy values close to 8, and resists differential cryptographic attacks. The optimum design of the proposed scheme makes it suitable for real-time deployment in resource-constrained environments.

This paper introduces a robust security framework for IoT networks that leverages hashchain-based authentication and Merkle tree-based data integrity verification to ensure secure and reliable communication. Specifically, during initialization, each node employs a one-way hash function to generate a unique hashchain of a predefined length where the hashchain root serves as the node’s identifier. The network then constructs a Merkle authentication tree using these root hashes. In the operational phase, recipients validate message authenticity by exchanging specific data, verifying the hash sequence order, and reconstructing the Merkle root. The framework ensures node authenticity and resistance against blended Sybil and Flooding attacks in RPL-based IoT networks attacks. Security analysis and a proof-of-concept implementation within the 6LoWPAN/RPL IoT stack, using Contiki OS and TelosB motes, demonstrate the effectiveness of our framework in mitigating such attacks with minimal impact on network performance.

RPL, the standard routing protocol for low-power and lossy networks, offers two modes for handling downward traffic: storing and non-storing. Each has significant limitations - the storing mode struggles with router memory constraints, potentially making destinations unreachable when a router's storage capacity is reached. Conversely, the non-storing mode mitigates this issue by employing source routing, but at the expense of increased network overhead. To address these challenges, this paper introduces the Leaf-Centric Mode (LCM), a novel approach that dramatically reduces storage requirements by enabling nodes to maintain routing information only for leaf nodes within their sub-networks, rather than all nodes. This optimized approach offers key advantages for IoT applications, including reduced storage footprint, improved reliability, lower network overhead, and enhanced overall performance. Through comprehensive experimental evaluation, we demonstrate the practical effectiveness of the LCM and establish its viability for IoT applications.

From the first attack in 1989, to date, it is evident that ransomware is highly destructive. Today the vast majority of research on ransomware detection is focused on the use of AI techniques. While the use of these techniques is very effective, they should not be considered an infallible solution for ransomware detection. As with any solution, AI implementations do have shortcomings of their own; compute resource constraints, collation of training data, data poisoning, and data privacy, to name a few. This paper aims to identify whether traditional methods can still effectively detect ransomware in scenarios where AI solutions may not be viable. Typically, there are three main categories of detection; signature-based, behaviour-based, & deception-based. This paper focuses on deception-based detection, using honey files. Three detection solutions have been implemented on two isolated VMs, one running Windows 10, the other Linux Mint. The solutions include RansomwareLocker, for the Linux VM, R-Locker and 4663 Windows event monitoring on the Windows 10 VM. With these solutions implemented, ransomware samples were executed in turn, up to three times, allowing an initial ‘out of the box’ test run and two subsequent tests after necessary configuration changes were made. Overall, from the ransomware samples chosen and detection solutions implemented, deception-based detection proves to be a promising approach. Testing resulted in two of the three solutions ultimately achieving a 100% detection rate. However, throughout the experiment, it is evident that this approach is not a silver bullet, and very dependent on the configuration of the solutions. Therefore, whether AI-based or traditional, a defence-in-depth approach remains best.

The Internet of Things (IoT) has garnered significant attention from both industries and the research community. The diverse nature of IoT devices makes them a prime target for cybercriminals. An intelligent intrusion detection system (IDS) can quickly identify multiple types of cyberattacks within an IoT system. However, operational efficiency and safety become challenging issues when managing limited labeled data in IoT networks. This article proposes a novel cyberattack detection scheme using few-shot learning (FSL). The proposed scheme employs a self-attention mechanism with a Siamese network that learns to recognize normal and malicious network traffic patterns through FSL. The Siamese network architecture facilitates efficient sample comparison by learning a shared representation. Incorporating an attention mechanism further enhances its ability to focus on discriminative features, improving attack detection accuracy for rapidly evolving intrusion patterns. The effectiveness of the proposed framework is evaluated through extensive experiments on the latest Edge-IIoTset dataset. The experimental findings demonstrate that the proposed IDS achieved the best accuracy of 99.75% with the complete dataset. In a few-shot performance evaluation, the designed architecture achieved the best accuracy of 78.69%, 81.19%, and 81.83% for 1 shot, 5 shots, and 10 shots, respectively.

The rapid growth of IoT devices requires robust intrusion detection systems (IDS) to address data privacy, communication efficiency, and adaptability to dynamic attack patterns. Traditional centralized IDS architectures face challenges with non-IID data distributions, high communication overhead, and the limitations of conventional neural networks in noisy IoT environments. This paper proposes a Federated Random Neural Network-based IDS (FedRaNN-IDS) framework to overcome these challenges. The proposed architecture integrates biologically inspired Random Neural Networks (RaNN) with federated learning (FL), utilizing RaNN’s stochastic neurons, dual excitatory-inhibitory synaptic pathways, and adaptive firing thresholds to enhance anomaly detection in non-IID data. A lightweight compression strategy selectively transmits significant model updates, reducing communication costs by 14−15% while maintaining over 85% efficiency. Federated aggregation harmonizes heterogeneous client contributions, achieving stable convergence. Evaluations across 10 − 50 clients demonstrate high detection accuracy (98.2−98.6%), lower inference latency (0.07ms−0.08ms per sample), and scalable training times (19−34 seconds per round). The results validate FedRaNN-IDS as an effective and communication-efficient solution for securing decentralized IoT networks against evolving cyber threats.

As electric vehicles (EVs) and Intelligent Transportation Systems (ITS) become more connected through IoTbased vehicular components and V2X communication, ensuring robust cybersecurity against adversarial attacks is critical for the safety and reliability of transportation networks. Vehicle networks are vital to smart cities, but they face increasing security threats, especially adversarial attacks. This work demonstrates a hybrid deep learning model, Inception + Skip Connection + BiGRU + Attention, for detecting such threats in IoT-based VANET datasets. The novel architecture presents a compact and efficient approach to detect misbehavior by employing an integrated approach that consists of multi-scale feature extraction, temporal sequence learning, and attention-based refinement. Global Average Pooling (GAP) enhances efficiency while preserving key features. Novelty of the proposed model lies in detecting diverse patterns from misbehavior dataset and overcoming the single model approach. Experiments on binary classification across five attack types, the model achieves F1-Scores up to 99.93% (Type-4) and maintains strong performance on others like Type-1 (97.25%) and Type-8 (98.74%). In the multi-class scenario, the model achieves 91.21% accuracy and a weighted F1 score of 90.3%. However, the recall for the type-16 attack is substantially lower, likely due to the subtle nature of this adversarial attack.

The increasing connectivity of Internet of Things (IoT) devices and networks has significantly raised security concerns. Intrusion detection systems (IDSs) serve as a firstline defense mechanism to detect and identify various cyber threats. However, traditional IDSs frameworks come with their own challenges, such as high computational costs, limited generalization to evolving variants of cyberattacks, increasing complexity, and vulnerability to adversarial attacks. This paper proposes a meta-learning-based IDS framework using Model-Agnostic Meta-Learning (MAML) to particularly combat adversarial attacks in IoT networks. The designed architecture optimizes model initialization by performing inner-loop updates using adversarially perturbed data. It aggregates gradients from these adversarially adapted models in the outer loop to achieve a resilient initialization that generalizes well against adversarial attacks. The proposed approach is rigorously evaluated by training and testing the model on three major adversarial attacks: Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and DeepFool. The experimental outcomes indicate the promising performance of the proposed architecture with an average attack detection accuracy of 95.97%. The compact model size of 0.06MB makes it suitable for deployment on resource-constrained IoT devices and networks. Furthermore, the lower inferencing time ensures the timely detection of intrusion, which is significant for real-time IDSs.

Vehicular ad hoc networks (VANETs)— a technology to connect autonomous vehicles to enhance the safety and decision-making on the road by enabling wireless communication and sharing of traffic information between sensors, vehicles, and other infrastructure. The dynamic nature of VANETs makes them vulnerable to several security threats, including false message attacks from within the network. Traditional misbehavior detection methods often fail in vehicular security due to the dynamic movement of vehicles. This research paper presents an efficient and accurate transformer-based approach for intrusion detection in VANETs to identify false positional information transmitted by misbehaving nodes and to analyze safety messages utilizing the Vehicular Reference Misbehavior (VeReMi) extension dataset. Moreover, the proposed approach utilises SHAP, an Explainable Artificial Intelligence (XAI) technique, to enhance model transparency by providing insights into feature importance, making the model’s predictions more interpretable and trustworthy for practical use in VANET environments. Performance analysis using both multi-class and binary classification demonstrates that the model outperforms various deep learning and machine learning-based intrusion detection systems, achieving 96.15% accuracy in multi-class and 98.28% in binary classification. The model excels in metrics like Accuracy, F1 Score, Recall, and Precision. In addition, the reliability parameters, i.e., Matthews Correlation Coefficient and Cohen’s Kappa coefficient, have been calculated to assess the quality of the classification and to measure the agreement between the predicted and original classifications, validating the model’s effectiveness in practical scenarios.

The integration of Unmanned Aerial Vehicles (UAVs) with the Internet of Things (IoT), also known as IoUAVs, facilitates real-time data transmission and coordinated operations in critical applications such as smart agriculture, disaster response, and infrastructure monitoring. The growing development of IoT has, however, made IoUAVs vulnerable to emerging cyberattacks that could disrupt these essential services. Deep learning can detect hidden attack patterns, but power and processing constraints make it challenging for resource-constrained IoUAVs. Edge computing offloads real-time analysis tasks, but optimizing workloads with unpredictable connectivity and high latency requirements for intrusion detection remains challenging. To address these challenges, this paper proposes a novel Edge-Based Intrusion Detection System (EIDS) that introduces two key innovations. We developed a metaheuristic task optimization technique for the IoUAV edge environment to efficiently manage computational loads and resources. Second, a Deep Transfer Learning (DTL) technique optimized for intrusion detection minimizes training time and computational overhead. Our novel EIDS-DTL technology synergistically incorporates these components for powerful intrusion detection. Our method optimizes feature extraction from IoUAV network traffic by purifying, filtering, and normalizing data. By fine-tuning pre-trained models, the system achieves high accuracy in identifying malicious activity while ensuring optimal performance in resource-constrained environments. Experimental results on two benchmark datasets demonstrate classification accuracies of 98.95% and 99.27%, outperforming existing approaches by up to 5% in accuracy while maintaining high precision, recall, and F1 scores. The proposed method enhances accuracy and efficiency, providing an effective solution for IoUAV security and edge optimization.

The Internet of Things (IoT) enhances the healthcare industry 5.0 by enabling connected devices and data-driven treatments, but it also introduces cyber threats such as data breaches, and unauthorized access. Mobile Edge Computing (MEC) improves security by reducing reliance on cloud transmissions. However, challenges such as Non-Independent and Identically Distributed (Non-IID) data and device intermittency affect security models in healthcare that require real-time analytics and reliable automation. These limitations are crucial in sensitive medical applications that require real-time analytics and reliable automation. This paper proposes TFedSec-HI, a Transformerdriven Federated Learning (TFL) for improving threat detection in the healthcare industry 5.0. Network traffic data is converted to grayscale and multi-channel RGB images using Local Binary Patterns (LBP) and Sobel edge detection. A lightweight mobile Vision Transformer (ViT) is used for effective feature extraction on edge devices, reducing computational load while retaining high performance. The Federated Proximal (FedProx) algorithm is used during the model aggregation phase to address issues with non-IID data distribution, resulting in consistent and effective learning. The global model is then shared with clients for realtime threat classification. The proposed method is evaluated on two real-world datasets, CICIoT2023 and CICIoMT2024, resulting in exceptional classification accuracies of 99.18% and 99.74%, respectively. TFedSec-HI addresses the Non-IID data challenges in Industry 5.0 healthcare by utilizing TFL to enable private, and adaptive threat detection across medical IoT devices.

With the widespread use of the Internet of Things (IoT), securing the storage and transmission of multimedia content across IoT devices is a critical concern. Chaos-based Pseudo-Random Number Generators (PRNGs) play an essential role in enhancing the security of image encryption algorithms. This paper introduces a novel 1-dimensional cosine-modulated-polynomial chaotic map to be used as a PRNG in image encryption algorithms. The proposed map utilizes a cosine function to modulate the outcome of a polynomial expression, resulting in complex chaotic behaviour. The designed map acts as a self-modulating system and offers a larger chaotic range, reduced structural complexity, and enhanced chaotic properties, such as aperiodicity, unpredictability, ergodicity, and sensitivity to control parameters and initial conditions, in comparison to the traditional 1-dimensional chaotic maps. An extensive evaluation is performed to gauge the chaotic behaviour of the proposed map, including bifurcation diagrams, chaotic trajectory analysis, fixed point and stability analysis, Lyapunov Exponent, Kolmogorov Entropy and NIST SP800-22 tests demonstrating its effectiveness to be used as a secure PRNG in image encryption algorithms.

The integration of Industrial Internet of Things (IIoT) technology into the industrial sector has produced numerous significant advantages. However, the notable concern remains the absence of robust security and privacy measures in these interconnected critical environments. To secure IIoT networks, several researchers and experts employ intrusion detection systems (IDS) for detecting cyberattacks. The current systems exhibit efficient performance when handling a few categories of attack classes, even in the presence of slight imbalances. However, these models face challenges when confronted with vast categories of attack classes and highly imbalanced data. To tackle these issues, this study introduces an attention-based hybrid deep learning (AB-HDL) model designed to monitor network traffic and predict cyberattacks within the network. The proposed model comprises an attention mechanism and a hybrid deep learning model that integrates convolutional neural networks (CNN) and an autoencoder (AE). The effectiveness of the proposed AB-HDL is assessed using publicly accessible datasets: Edge-IIoTset and X-IIoTID. To ascertain the efficacy of AB-HDL, a comparative analysis is conducted with various other machine learning (ML) and deep learning (DL) algorithms. The outcome analysis indicates that the proposed AB-HDL surpasses the performance of the other algorithms and exhibits optimal efficiency in detecting cyber attacks within IIoT networks.

This study explores advancements in AI-generated image detection, emphasizing the increasing realism of images, including deepfakes, and the need for effective detection meth-ods. Traditional Convolutional Neural Networks (CNNs) have shown success but face limitations in generalization and accu-racy, particularly with newer technologies like Diffusion Models. With the evolution of AI image generation models, from CNNs to Generative Adversarial Networks (GANs) and Diffusion Models, detecting synthetic images has become more challenging. Issues include dataset diversity, adversarial attacks, and inconsistencies in pre-processing methods. While state-of-the-art models like CNNs, Vision Transformers (ViTs), and hybrid approaches exist, their accuracy in detecting increasingly sophisticated fake images remains suboptimal. This research proposes a novel hybrid detection model combining CNNs and ViTs with an additional attention mechanism layer. This structure aims to improve the interaction between local and global features, enhancing detection accuracy. The model was trained using the CIFAKE dataset, which contains 120,000 real and AI -generated images. The added attention mechanism enhances feature extraction, addressing limitations in existing models when faced with next-generation synthetic images. The hybrid CNNNiT +Attention model demonstrated improved detection accuracy, achieving 99.77%, surpassing previous methods. This research lays a foundation for stronger AI -generated image detection, helping to mitigate the risks of synthetic image fraud.

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness.

In the dynamic field of the Industrial Internet of Things (IIoT), the networks are increasingly vulnerable to a diverse range of cyberattacks. This vulnerability necessitates the development of advanced intrusion detection systems (IDSs). Addressing this need, our research contributes to the existing cybersecurity literature by introducing an optimized Intrusion Detection System based on Deep Transfer Learning (DTL), specifically tailored for heterogeneous IIoT networks. Our framework employs a tri-layer architectural approach that synergistically integrates Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and bootstrap aggregation ensemble techniques. The methodology is executed in three critical stages: First, we convert a state-of-the-art cybersecurity dataset, Edge_IIoTset, into image data, thereby facilitating CNN-based analytics. Second, GA is utilized to fine-tune the hyperparameters of each base learning model, enhancing the model’s adaptability and performance. Finally, the outputs of the top-performing models are amalgamated using ensemble techniques, bolstering the robustness of the IDS. Through rigorous evaluation protocols, our framework demonstrated exceptional performance, reliably achieving a 100% attack detection accuracy rate. This result establishes our framework as highly effective against 14 distinct types of cyberattacks. The findings bear significant implications for the ongoing development of secure, efficient, and adaptive IDS solutions in the complex landscape of IIoT networks.

Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates the use of Wi-Fi channel state information (CSI) as a novel method of ambient sensing that can be employed as a contactless means of recognizing human activity in indoor environments. These methods avoid additional costly hardware required for vision-based systems, which are privacy-intrusive, by (re)using Wi-Fi CSI for various safety and security applications. During an experiment utilizing universal software-defined radio (USRP) to collect CSI samples, it was observed that a subject engaged in six distinct activities, which included no activity, standing, sitting, and leaning forward, across different areas of the room. Additionally, more CSI samples were collected when the subject walked in two different directions. This study presents a Wi-Fi CSI-based HAR system that assesses and contrasts deep learning approaches, namely convolutional neural network (CNN), long short-term memory (LSTM), and hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental results indicate that LSTM surpasses current models and achieves an average accuracy of 95.3% in multi-activity classification when compared to CNN and hybrid techniques. In the future, research needs to study the significance of resilience in diverse and dynamic environments to identify the activity of multiple users.

Blockchain (BC) and Information for Operational and Tactical Analysis (IOTA) are distributed ledgers that record a huge number of transactions in multiple places at the same time using decentralized databases. Both BC and IOTA facilitate Internet-of-Things (IoT) by overcoming the issues related to traditional centralized systems, such as privacy, security, resources cost, performance, and transparency. Still, IoT faces the potential challenges of real-time processing, resource management, and storage services. Edge computing (EC) has been introduced to tackle the underlying challenges of IoT by providing real-time processing, resource management, and storage services nearer to IoT devices on the network’s edge. To make EC more efficient and effective, solutions using BC and IOTA have been devoted to this area. However, BC and IOTA came with their pitfalls. This survey outlines the pitfalls of BC and IOTA in EC and provides research directions to be investigated further.

In the era of Industrial IoT (IIoT) and Industry 4.0, ensuring secure data transmission has become a critical concern. Among other data types, images are widely transmitted and utilized across various IIoT applications, ranging from sensor-generated visual data and real-time remote monitoring to quality control in production lines. The encryption of these images is essential for maintaining operational integrity, data confidentiality, and seamless integration with analytics platforms. This paper addresses these critical concerns by proposing a robust image encryption algorithm tailored for IIoT and Cyber-Physical Systems (CPS). The algorithm combines Rule-30 cellular automata with chaotic scrambling and substitution. The Rule 30 cellular automata serves as an efficient mechanism for generating pseudo-random sequences that enable fast encryption and decryption cycles suitable for realtime sensor data in industrial settings. Most importantly, it induces non-linearity in the encryption algorithm. Furthermore, to increase the chaotic range and keyspace of the algorithm, which is vital for security in distributed industrial networks, a hybrid chaotic map, i.e., logistic-sine map is utilized. Extensive security analysis has been carried out to validate the efficacy of the proposed algorithm. Results indicate that our algorithm achieves close-to-ideal values, with an entropy of 7.99 and a correlation of 0.002. This enhances the algorithm's resilience against potential cyber-attacks in the industrial domain.

The increasing trend of the Industrial Internet of Things (IIoT) within industrial environments magnifies the risk of security breaches and vulnerabilities. Maintaining confidentiality is a pivotal requirement for effectively establishing the IIoT environment. To promptly detect malicious endeavors, integrating an intrusion detection system (IDS) becomes imperative for continuously monitoring IIoT activities. The sophisticated automated IDSs are built upon the foundation of machine learning (ML) and deep learning (DL). However, these algorithms encounter challenges related to heavily imbalanced training data and the need for accurate predictions in a short timeframe. This paper introduces an attention-based deep neural network (ABDNN) designed to tackle these challenges for intrusion detection within the IIoT environment. The attention mechanism plays a pivotal role in determining the significance of each attribute in the input data. Subsequently, the deep neural network (DNN) comes into play, leveraging the previously determined attribute importance to predict network behaviors. This process yields the advantage of predicting network behaviors more efficiently in less time. The performance of the proposed ABDNN model was evaluated using the X-IIoTID dataset. To validate its effectiveness, a comparison was made between the performance of the proposed model and that of state-of-the-art approaches. This comparative analysis serves to validate the superior performance of the proposed ABDNN model.

Wireless sensor networks have become incredibly popular due to the Internet of Things’ (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for low-power and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm’s effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

The Internet of Things (IoT) is a global network that connects a large number of smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to-machine communication, widely adopted in IoT networks. Various smart devices within these networks are employed to handle sensitive information. However, the scale and openness of IoT networks make them highly vulnerable to security breaches and attacks, such as eavesdropping, weak authentication, and malicious payloads. Hence, there is a need for advanced machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS). Existing ML-based IoT-IDSs face several limitations in effectively detecting malicious activities, mainly due to imbalanced training data. To address this, this study introduces a transformer neural network-based intrusion detection system (TNN-IDS) specifically designed for MQTT-enabled IoT networks. The proposed approach aims to enhance the detection of malicious activities within these networks. The TNN-IDS leverages the parallel processing capability of the Transformer Neural Network, which accelerates the learning process and results in improved detection of malicious attacks. To evaluate the performance of the proposed system, it was compared with various IDSs based on ML and DL approaches. The experimental results demonstrate that the proposed TNN-IDS outperforms other systems in terms of detecting malicious activity. The TNN-IDS achieved optimum accuracies reaching 99.9% in detecting malicious activities.

Nowadays, developing environmental solutions to ensure the preservation and sustainability of natural resources is one of the core research topics for providing a better life quality. Using renewable energy sources, such as solar energy, is one of the solutions that can reduce the overuse of natural resources. This research aims to boost the efficiency of solar energy plants by proposing a novel approach to optimize the total flying time of battery-based drone systems to enhance the performance of solar plant systems. The contribution of the proposed approach is to solve scheduling problems based on timing constraints to monitor the solar plant. The main objective of the proposed approach is to maximize the drone’s minimum total flying time, which will increase the availability and reliability of the solar plant monitoring system. Time to empty values is calculated based on battery degradation rates. This problem is proven to be NP-hard. Four categories of enhanced algorithms were developed to solve drones’ scheduling problems in handling various tasks within multiple errands in the extent of solar parks in the monitored power plant to achieve the desired objective. Experimental results of the presented algorithms showed that the$M2S$algorithm has a stable performance behavior in all conducted experiments.

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N × M × 3 to P × Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3 . The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, and so on. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption.

Abstract In many cases, images contain sensitive information and patterns that require secure processing to avoid risk. It can be accessed by unauthorized users who can illegally exploit them to threaten the safety of people’s life and property. Protecting the privacies of the images has quickly become one of the biggest obstacles that prevent further exploration of image data. In this paper, we propose a novel privacy-preserving scheme to protect sensitive information within images. The proposed approach combines deoxyribonucleic acid (DNA) sequencing code, Arnold transformation (AT), and a chaotic dynamical system to construct an initial S-box. Various tests have been conducted to validate the randomness of this newly constructed S-box. These tests include National Institute of Standards and Technology (NIST) analysis, histogram analysis (HA), nonlinearity analysis (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), bit independence criterion strict avalanche criterion (BIC-SAC), bit independence criterion nonlinearity (BIC-NL), equiprobable input/output XOR distribution, and linear approximation probability (LP). The proposed scheme possesses higher security wit NL = 103.75, SAC ≈ 0.5 and LP = 0.1560. Other tests such as BIC-SAC and BIC-NL calculated values are 0.4960 and 112.35, respectively. The results show that the proposed scheme has a strong ability to resist many attacks. Furthermore, the achieved results are compared to existing state-of-the-art methods. The comparison results further demonstrate the effectiveness of the proposed algorithm.

The Internet of Things (IoT) devices, networks, and applications have become an integral part of modern societies. Despite their social, economic, and industrial benefits, these devices and networks are frequently targeted by cybercriminals. Hence, IoT applications and networks demand lightweight, fast, and flexible security solutions to overcome these challenges. In this regard, artificial-intelligence-based solutions with Big Data analytics can produce promising results in the field of cybersecurity. This article proposes a lightweight dense random neural network (DnRaNN) for intrusion detection in the IoT. The proposed scheme is well suited for implementation in resource-constrained IoT networks due to its inherent improved generalization capabilities and distributed nature. The suggested model was evaluated by conducting extensive experiments on a new generation IoT security dataset ToN_IoT. All the experiments were conducted under different hyperparameters and the efficiency of the proposed DnRaNN was evaluated through multiple performance metrics. The findings of the proposed study provide recommendations and insights in binary class and multiclass scenarios. The proposed DnRaNN model attained attack detection accuracy of 99.14% and 99.05% for binary class and multiclass classifications, respectively.

In recent years, the Internet of Things (IoT) technology has led to the emergence of multiple smart applications in different vital sectors including healthcare, education, agriculture, energy management, etc. IoT aims to interconnect several intelligent devices over the Internet such as sensors, monitoring systems, and smart appliances to control, store, exchange, and analyze collected data. The main issue in IoT environments is that they can present potential vulnerabilities to be illegally accessed by malicious users, which threatens the safety and privacy of gathered data. To face this problem, several recent works have been conducted using microservices-based architecture to minimize the security threats and attacks related to IoT data. By employing microservices, these works offer extensible, reusable, and reconfigurable security features. In this paper, we aim to provide a survey about microservices-based approaches for securing IoT applications. This survey will help practitioners understand ongoing challenges and explore new and promising research opportunities in the IoT security field. To the best of our knowledge, this paper constitutes the first survey that investigates the use of microservices technology for securing IoT applications.

Threat information sharing is considered as one of the proactive defensive approaches for enhancing the over-all security of trusted partners. Trusted partner organizations can provide access to past and current cybersecurity threats for reducing the risk of a potential cyberattack—the requirements for threat information sharing range from simplistic sharing of documents to threat intelligence sharing. Therefore, the storage and sharing of highly sensitive threat information raises considerable concerns regarding constructing a secure, trusted threat information exchange infrastructure. Establishing a trusted ecosystem for threat sharing will promote the validity, security, anonymity, scalability, latency efficiency, and traceability of the stored information that protects it from unauthorized disclosure. This paper proposes a system that ensures the security principles mentioned above by utilizing a distributed ledger technology that provides secure decentralized operations through smart contracts and provides a privacy-preserving ecosystem for threat information storage and sharing regarding the MITRE ATT&CK framework.