Babylonian Journal of Internet of Things
The Babylonian Journal of Internet of Things focuses on advancing IoT technologies. It offers a platform for sharing cutting-edge research and applications in sensor networks, architectures, data analytics, security, and various IoT fields. Committed to fostering innovation, it pushes the boundaries of IoT research and practical implementations
An Enhanced Hybrid Memetic mRMR–FOA Framework for Feature Selection in IoT Intrusion Detection Systems
The rapid expansion of the Internet of Things (IoT) has enlarged the attack surface of connected systems, increasing their exposure to sophisticated cyber-attacks and creating a strong demand for effective Intrusion Detection Systems (IDS). High-dimensional network traffic, however, degrades both the accuracy and the efficiency of IDS classifiers. This paper proposes a hybrid feature-selection framework, Memetic mRMR-FOA, that couples the Minimum Redundancy Maximum Relevance (mRMR) filter with a memetic extension of the Fossa Optimization Algorithm (FOA). Within the framework, mRMR plays a single, well-defined role: it ranks features by an information-theoretic relevance-redundancy score and this ranking is used both to seed a high-quality initial population and to guide the memetic local search. FOA performs the global search, while a Hamming-neighbourhood local search refines elite solutions. The objective is a minimization fitness that jointly rewards classification accuracy and low redundancy while penalizing subset size. On the RT-IoT2022 benchmark, the proposed method attains the best feature-reduction ratio of 68.3 percent, selecting only 13 features at the lowest fitness value of 0.038, and improves detection accuracy to 98.43 percent. Comparative analysis against Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), standard FOA, a memetic symmetrical-uncertainty variant, and standalone mRMR confirms consistent gains in accuracy, precision, and false-alarm rate. The results indicate that the Memetic mRMR-FOA framework is an efficient and scalable feature-selection strategy for protecting resource-constrained IoT environments.
Copyright (c) 2026 Yusra Sh Ajaj https://creativecommons.org/licenses/by/4.0
AI-Powered Trust Management and Cryptographic Access Control in IoT Systems
The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and innovation across various sectors such as healthcare, smart cities, and industrial automation. However, this pervasive integration also introduces significant security, privacy, and trust challenges, primarily due to the decentralized, dynamic, and resource-constrained nature of IoT ecosystems. Ensuring secure and reliable interactions among heterogeneous devices and users is paramount for the successful deployment and adoption of IoT technologies. This paper comprehensively reviews AI-powered trust management and cryptographic access control mechanisms designed to address these critical issues in IoT systems. We explore the fundamental principles, advanced techniques, and the synergistic integration of AI and cryptography to enhance security, privacy, and trustworthiness. Drawing upon recent research from 2023 to 2026, we analyze the strengths, limitations, and practical implications of various solutions, including AI-driven anomaly detection, federated learning, homomorphic encryption, and blockchain-based trust models. Furthermore, we identify emerging trends, future directions, and persistent challenges such as scalability, interoperability, and ethical considerations, offering a holistic perspective on the evolving landscape of secure IoT. The aim is to synthesize current knowledge and highlight key research avenues for developing more robust, adaptive, and context-aware security frameworks for future IoT environments.
Copyright (c) 2026 Dhuha Al-Adhami, Hamza Gharsellaoui, Olfa Belkahla Driss https://creativecommons.org/licenses/by/4.0
Hybrid Deep Learning-Based Predictive Maintenance Framework for Industrial Internet of Things Systems
The widespread introduction of the Industrial Internet of Things (IIoT) has allowed smart sensors to be connected to industrial equipment, which means their working state can be monitored at all times, resulting in an increasing amount of operational data that could be utilized for predictive maintenance using intelligent algorithms. The traditional maintenance methods such as corrective maintenance, preventative maintenance can cause unnecessary maintenance expenses, unforeseen machine breakdowns, and lower productivity because of the lack of accurate prediction of machine degradation. In order to address these problems, this paper suggests a Hybrid CNN–LSTM framework for predictive maintenance combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The proposed framework consists of the following steps: industrial sensor data acquisition, data preprocessing, development of a hybrid deep learning model, and intelligent fault prediction. The model was tested with the AI4I 2020 Predictive Maintenance Dataset and was compared to the traditional machine learning and deep learning models, such as Support Vector Machine (SVM), Random Forest (RF), CNN, and LSTM. The performance was evaluated based on the following metrics: Accuracy, Precision, Recall, and F1-score. The experimental results showed that the proposed Hybrid CNN–LSTM framework achieved an Accuracy of 97.12%, Precision of 96.74%, Recall 96.39% and F1-score of 96.56% compared to the baseline models with stable convergence during training. The obtained results show that the suggested framework is accurate and reliable solution to intelligent predictive maintenance and early fault diagnosis in Industrial Internet of Things environment.
Copyright (c) 2026 Ilyass mzili , Zakaria benaliali , otmane houdaif https://creativecommons.org/licenses/by/4.0
Deep Learning-Based Malware Detection and Classification Using Memory Behavioral Features
As malware evolves and grows more sophisticated, it remains a major challenge for modern cybersecurity solutions to detect and prevent. The traditional approaches for malware detection, which are mostly based on signature-based detection and manually engineered features, are no longer effective against new variants of malware and the ones that are highly obfuscated. Considering these drawbacks, this research aims to propose an effective lightweight hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) for detecting and categorizing malware based on its memory behavioral features. The proposed framework can automatically learn the representative behavioral patterns that exist in the CIC-MalMem-2022 benchmark dataset using a systematic pipeline that includes data preprocessing, feature normalization, CNN-based feature extraction, LSTM-based sequential learning, and Softmax classification. Model performance is assessed on common performance metrics: Accuracy, Precision, Recall, F1-score, Confusion Matrix, Training Time, Inference Time. The experimental results show that the proposed CNN–LSTM framework not only performs well in terms of classification accuracy but also has a low computational complexity, which can be applied to practical cybersecurity applications. The hybrid architecture is able to effectively incorporate both spatial and sequential behavioural aspects to be able to discriminate between benign and other multiple malware families. In conclusion, the suggested approach offers a promising and effective deep learning method for detecting and categorizing malware using memory behavior analysis, which helps advance the creation of intelligent cybersecurity systems.
Copyright (c) 2026 Mohammed Basil Abdulkareem, Kassem Hamze, Mohammad Abdullah Abbas https://creativecommons.org/licenses/by/4.0
Blockchain-Enabled Trust Management Framework for Internet of Things Environments
The growth of the Internet of Things (IoT) has made it increasingly important to have secure and reliable trust management mechanisms that can support dynamic and decentralized network environments. Traditional trust management solutions often need centralized architectures that are vulnerable to a single point of failure, manipulation of trust and low scalability. While blockchain technology is a recently promising approach to providing decentralized IoT security, many current frameworks emphasize only the authentication or data integrity aspects of security, and offer limited assistance in evaluating the trust of a system continuously, and in updating it efficiently. In this paper, the authors introduce a new Blockchain-Enabled Trust Management Framework (BETMF) that combines the blockchain, smart contract and a lightweight trust evaluation mechanism to a consistent architecture in decentralized IoT environments. It proposes a framework that will continuously monitor device interactions, confirm trust updates by smart contracts and save validated trust records in the immutable trust ledger of the blockchain. In contrast to the other methods which involve merging several computational methods or complicated consensus protocols, BETMF implements a single consensus process for trust management, which enhances transparency, trust integrity, and scalability, with minimal computational costs. The suggested framework has been established in Python, and tested with a private Ethereum blockchain that was deployed using Ganache. Experimental results showed that the Trust Decision Accuracy was 97.8%, Precision was 97.3%, Recall was 96.9%, F1-score was 97.1%, the average latency of trust update was 18.6 ms, the average latency of access decision was 31.4 ms and the blockchain throughput was 132 transactions per second. The results show that the proposed framework can achieve a good balance of security, decentralization and operation efficiency in heterogeneous IoT environments. The prominent contribution of this work is the development of a lightweight blockchain based trust management framework that reduces the difficulty of trust evaluation, offers immutable trust storage, automated smart-contract based verification and generates efficient decentralized trust management for the next generation IoT applications.
Copyright (c) 2026 Aaron Mogeni oirere , Ali Rachini https://creativecommons.org/licenses/by/4.0
Internet of Bio-Nano Things (IoBNT) Security: A Comprehensive Survey of Threat Models, Protocols, Mitigation Strategies, Technological Integrations, Tools, and Performance Metrics
The Internet of Bio-Nano Things (IoBNT) constitutes a transformative paradigm to support applications in precision medicine, environmental monitoring, and bio-hybrid cyber-physical systems. However, it faces a complex security landscape. Existing research typically examines isolated threats, individual protocol layers, or specific communication modalities, rather than providing a unified perspective on security vulnerabilities, threat models, and mitigation strategies across the entire IoBNT stack. This study provides a comprehensive, structured, and security-centric overview of IoBNT systems by identifying and classifying security threats; analyzing existing and emerging security protocols and mitigation techniques; examining enabling technological integrations that support secure IoBNT operations; and establishing coherent performance metrics and evaluation criteria. The study adopted a comprehensive review methodology, examining 153 research papers retrieved from Frontiers, ACM Digital Library, Wiley Online Library, Nature, Springer Nature, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar, with a focus on publications from 2023 to 2026 that addressed security aspects of the IoBNT. The survey examines contemporary security challenges and defenses in the IoBNT ecosystem. It synthesizes state-of-the-art mitigation strategies alongside reinforcing technological integrations for predictive bio-cyber risk assessment and quantum-safe mechanisms that support long-term resilience. The analysis also surveys tools, simulation frameworks, testbeds, and experimental platforms used to model, validate, and benchmark IoBNT security solutions, while consolidating standardized performance metrics. Finally, it maps the current research landscape, identifies open challenges, and outlines future directions for developing secure, reliable, and ethically aligned IoBNT systems. This survey offers important implications for both researchers and practitioners by delivering a holistic security perspective that informs the design of robust, interoperable, and trustworthy IoBNT systems.
Copyright (c) 2026 Guma Ali, Bosco Apparatus Buruga https://creativecommons.org/licenses/by/4.0
Hybrid Privacy-Preserving Federated Learning Framework for Secure IoT Applications Using Differential Privacy and Homomorphic Encryption
Internet of Things (IoT) applications have expanded quickly and are producing massive amounts of data from a diverse range of smart devices. Federated Learning (FL) allows collaborative model training without data transfer of the actual images but suffers from privacy concerns as sensitive information might be leaked through the model parameters. Most of the current privacy-preserving FL methods use DP or HE separately which leads to a compromise between privacy protection, model accuracy and computation efficiency. In response, this paper introduces Hybrid Privacy-Preserving Federated Learning (HPPFL), a comprehensive privacy-preserving learning system that combines Differential Privacy (DP) and Homomorphic Encryption (HE) for secure IoT applications. The proposed design allows for local training of the model on IoT devices, followed by the addition of noise (adaptive Differential Privacy) to the model updates, then encrypting the noisy model using Paillier Homomorphic Encryption before sending it to the federated server. The model updates are then securely aggregated with FedAvg without making sensitive information public and the aggregated model is sent back to the global model for next training round. TON-IoT was used to test the proposed framework, and it was compared to the conventional FL, DP-FL, and HE-FL approaches based on classification accuracy, precision, recall, F1-score, communication cost, encryption overhead, and training time. The experimental results demonstrate that the proposed HPPFL framework achieved classification accuracy of 97.46% compared to other FL frameworks, conventional FL, DP-FL, HE-FL with the highest level of privacy protection and moderate computational overhead. The results indicate that the proposed framework is capable of preserving privacy, ensuring effective communication and maintaining the prediction performance, which shows it is a potential solution to secure large-scale IoT applications.
Copyright (c) 2026 Maan Nawaf Abbood, Zahraa A. Abdalkareem https://creativecommons.org/licenses/by/4.0
Hybrid Neuro-Fuzzy and Swarm Optimization-Based Energy-Efficient Routing for Large-Scale IoT Networks
The proliferation of large-scale Internet of Things (IoT) networks has raised various concerns about energy usage, routing efficiency, scalability and lifetime. However, traditional routing protocols like LEACH, HEED, PEGASIS, and RPL can face challenges in maintaining optimal performance in dense IoT networks, as they tend to consume significant amounts of energy, have high communication overheads, and are less flexible in adapting to changing network conditions. For overcoming these issues, the current paper presents Hybrid Neuro-Fuzzy and Swarm Optimization (HNF-SO) routing protocol that combines Neuro-Fuzzy intelligence with Whale Optimization Algorithm (WOA) to improve routing decision making and global route optimization. The proposed architecture is based on hierarchical clustering network in which a Neuro-Fuzzy engine is used to calculate the energy efficiency of all considered routing candidates, to calculate the distance to the sink, to calculate the node density and to calculate the traffic load, while the WOA is used to identify the most energy-efficient cluster heads and routing paths. A large-scale IoT network scenario was used throughout the extensive simulations, and the protocol was assessed based on the energy consumption, network lifetime, residual energy, packet delivery rate (PDR), throughput, delay, and scalability. The results show that the proposed HNF-SO significantly improves the packet delivery performance, lowers the energy consumption, provides the long lifetime of the network, and can operate without any disruption even with the increase of the network size compared with the traditional routing protocols. The key contribution of this work is the development of an intelligent, scalable and energy-efficient solution for large scale deployments of IoTs by combining the algorithms of Neuro-Fuzzy decision making and swarm based optimization into a single routing framework.
Copyright (c) 2026 Mostafa Abdulghafoor Mohammed, Mohammed Rashid Subhi https://creativecommons.org/licenses/by/4.0
An Explainable Hybrid Deep Learning Framework for Unsupervised Network Intrusion Detection Using Isolation Forest and Bidirectional Long Short-TermMemory Autoencoder
The increasing proliferation of Internet of Things (IoT) devices has introduced significant cybersecurity vulnerabilities due to huge data these devices generate, and the growing sophistication of cyberattacks. Traditional signature-based intrusion detection systems are often ineffective against zero-day and evolving threats, particularly in dynamic IoT environments where labeled datasets are scarce. This paper proposes an explainable hybrid unsupervised deep learning framework that integrates Isolation Forest and Bidirectional Long Short-Term Memory (BiLSTM) autoencoder models for intelligent network intrusion detection. The framework combines statistical anomaly isolation with deep temporal sequence learning to enhance the detection of abnormal network behaviors without relying on labeled data. Network traffic data obtained from the Kaggle network traffic dataset were preprocessed through data cleaning, feature engineering, normalization, and temporal sequence generation. A weighted ensemble mechanism was employed to combine anomaly scores from both models, while SHapley Additive exPlanations (SHAP) and LIME (Local Interpretable Model-Agnostic Explanations) techniques were integrated to improve interpretability and transparency of detection decisions. Experimental results demonstrated strong convergence of the BiLSTM Autoencoder with extremely low reconstruction losses and effective discrimination between normal and malicious traffic patterns. The hybrid framework successfully detected anomalous traffic bursts and suspicious communication behaviors, with “packets_per_time_unit” identified as the most influential anomaly indicator. The proposed framework provides an efficient, scalable, and explainable solution for adaptive IoT cybersecurity and intrusion detection in heterogeneous network environments.
Copyright (c) 2026 Aerkera Kumawuese Daniel-Beston, Aamo Iorliam https://creativecommons.org/licenses/by/4.0
Network-Based Audience Influence Analysis Using Multiple Regression Models
The increase of diffusion speed by online media makes information publication and audience shaping in heterogeneous communication network more complex. Thus, a key challenge in network analysis and media-analytics research is to investigate how network structure impacts the size of audience reach. In the present work, we constructed a directed network among media outlets based on data from multiple sources on their interaction, and in which nodes represent media outlets and edges encode diverse interactions between media outlets with associated weights. Structural network properties, such as in-degree, out-degree, weighted degree measures and ranks (PageRank), betweenness centrality and closeness centrality were calculated to measure the positional importance of every node in the network. To analyses how audience size could be related to these properties of the network Ridge Regression with leave-one-out cross-validation and Ordinary Least Squared (OLS) Multiple Linear Regressions were applied. The results show that the network-based features can convey relevant explanatory power towards predicting audience size, which emphasizes the involvement of centrality and connectivity in medium influence. The Performance of the Regression The regression performance analysis reveals good prediction capacity, 0.72 and 0.74 for Ridge and OLS models' R2, respectively, as well as low error measures which ensure that the proposed framework is robust. Furthermore, influence diagnostics indicate the existence of a small subset of highly influential nodes, thus highlighting the necessity to address network heterogeneity in regression type models. In conclusion, the method introduced in this paper provides a systematic and interpretable tool to study audience influence through network-aware regression model that has applications outside traditional areas of media studies and beyond, including information diffusion analysis and digital communication research.
Copyright (c) 2026 Ruaa Azzah Suhail, Bashar Talib Al-Nuaimi, Oluwaseun A. Adelaja https://creativecommons.org/licenses/by/4.0
Comprehensive Analysis and Anomaly Detection of Network Traffic Using Isolation Forest Modeling
Network traffic analysis is indeed in high value and importance, because analysis appears to our communication behavior, we can make better use of our web portal for the performance of what we are executing, additionally it helps us identify certain facilitates that can put our environment at a peculiar point for others to make attacks. The study gives an overview of a real use case network traffic captured using Wireshark and available in the excel file. The records include packet-level details such as timestamps, source and destination addresses, and protocols, and the packet lengths. First, we perform exploratory data analysis to capture the traffic behavior in terms of protocol distribution and variation in packet size, traffic metrics and communication characteristics. Temporal analysis has shown the highly bursty behavior of traffic and with the packet and byte rates changing in bursts over time. Next, traffic data are clustered into fixed time periods and converted into behavioral features that which encode packet counts, byte counts, and entropy of observed hosts. We use an unsupervised Isolation Forest model to detect abnormal traffic patterns to bypass labeled attack data. The model is able to identify abnormal time windows associated with extreme traffic spikes and abnormal communication patterns. We find that the integration of statistical traffic characterization and machine learning-based anomaly detection results in an efficient and scalable network monitoring and cybersecurity framework.
Copyright (c) 2026 Ali Subhi Alhumaima, Osama Salim Hameed, Hussein Alkattan https://creativecommons.org/licenses/by/4.0
Factor-Based Unsupervised Network Traffic Anomaly Analysis Using Density, Spectral, and Hierarchical Clustering
Anomaly detection in network traffic is crucial for the nurturance of today’s communication systems with soundness and robustness, particularly in situations where labeled data are sparse or not available. In this paper, we propose an unsupervised approach in which factor analysis combined with multiple clustering methods is used to locate anomalous patterns on network traffic. First, the numerical traffic characteristics are standardized and then factor analysis is applied to obtain a reduced set of latent factors composed of the mean statistics with 85% cumulative variance. The resultant factor scores are then modeled by Agglomerative Hierarchical Clustering, Gaussian Mixture Models, DBSCAN and Spectral Clustering to reveal intrinsic traffic patterns. Validity of the clustering results is also evaluated through internal validation measures such as Silhouette Coefficient (SC), Calinski–Harabasz Index (CHI), and Davies–Bouldin Score (DBS), along with a post-hoc comparison to ground-truth labels for interpretability judgment. The proposed method has been tested on simulated and real data–sets of gene expression profiles, and Student t-distributed Stochastic Neighbor Embedding for dimensionality reduction applied to Spectral Clustering produces the best separation in reduced factor space, with Agglomerative Clustering failing between clustering into stable and interpretable groups. In addition, DBSCAN has a strong ability to find rare and irregular traffic cases that anomalous samples highly concentrate in certain clusters. These findings confirm the efficiency of parallel factor analysis followed by various unsupervised clustering methods in order to improve robustness, interpretability, and scalability in network traffic anomaly detection, thus validating the proposed approach for practical network reliability and cybersecurity.
Copyright (c) 2026 Wisam Hayder Mahdi , Waleed Khalid Al-Zubaidi, Isam Bahaa Aldallal, Abdullahi Abdu Ibrahim https://creativecommons.org/licenses/by/4.0
An Adaptive Data Security Frame Using Federated Learning and Blockchain for Privacy Protection in Smart Environments
The rapid expansion of smart environments, including smart cities, healthcare systems, and intelligent energy grids, has resulted in the generation of massive volumes of distributed and privacy-sensitive data. Conventional centralized security architectures are increasingly inadequate to guarantee confidentiality, integrity, and trust under adversarial and resource-constrained conditions. This paper proposes an Adaptive Federated Blockchain Security Framework (AFBSF) that integrates federated learning (FL) with a lightweight blockchain layer and a dynamic trust-driven cryptographic control mechanism. Federated learning enables collaborative model training without sharing raw data, while a Proof-of-Authority (PoA) blockchain provides tamper-resistant verification and transparent auditability of model updates. In addition, an adaptive trust model dynamically adjusts encryption strength and node participation according to behavioral reliability and data integrity, allowing real-time isolation of malicious or unreliable devices. Extensive experiments conducted on smart healthcare, energy, and transportation datasets demonstrate that the proposed framework outperforms conventional FL-based, blockchain-based, and existing hybrid approaches in terms of accuracy, privacy preservation, communication efficiency, and energy consumption. The results confirm that AFBSF achieves high learning performance with enhanced privacy protection, reduced attack success rate, and lower system overhead, making it a scalable and reliable security paradigm for next-generation decentralized IoT ecosystems.
Copyright (c) 2026 Najwan Abed Hasan https://creativecommons.org/licenses/by/4.0
Smart IoT Attack Detection Through AI-Optimized Routing Methods
The Internet of Things (IoT) model possesses much flexibility and mobility, hence the IoT system becomes more prone to security threats such as Distributed Denial-of-Service (DDoS) attacks due to its decentralized control, dynamic node moving, energy scarces and bandwidth limited. This development has demonstrated the capabilities of AI in providing improvements to performance of IoT network in terms of achieving faster rate, provide more throughput and achieve higher packet delivery ratio. Utilization of AI-based analytics technology with the use of adaptive methods can improve metrics such as End to End delay (E2E) and Average Received Packets (ARP), by improving response time and increasing intelligence in Intrusion Detection Systems in IoT based setups. In this paper, we use Feedforward Neural Networks (FFNN) and Convolutional Neural Networks (CNN) to detect malicious activities which can strengthen the capabilities of IDS in IoT routing. The suggested AI-optimized routing model outperforms existing models in detection accuracies, which are improved up to 82% and 85% with respective processing time of 18s and 17s. These findings demonstrate the potential to significantly improve security of IoT systems along with increasing overall network robustness and efficiency using this framework.
Copyright (c) 2025 Nabaa Ahmed Noori, Atheel Sabih Shaker, Rana Abdulrahman Lateef , Hiba Alzubaidi https://creativecommons.org/licenses/by/4.0
Investigating the Role of Deep Learning in Enhancing Communication Efficiency in 5G IoT Networks: A Comprehensive Survey
This analysis primarily examines the compatibility of IoT security and DL in offering attack prevention services for IoT devices. Consequently. The field of Internet of Things (IoT) is seeing significant growth. Consequently, there is an increasing amount of data being exchanged between cloud technologies and wireless networks to ensure smooth data flow among linked devices. Simultaneously, the Internet of Things (IoT) proves to be an exceedingly susceptible ecosystem that is susceptible to a wide range of threats. These entities has the capacity to cause extensive destruction to a country and also pose the most significant immediate economic threat. This survey essay aims to thoroughly examine the integration of 5G, IoT, and security. It is crucial to prioritise the requirement for security systems. The introduction discusses the infiltration of web vulnerabilities into devices, based on the provided information about attacks and threats. Subsequently, it demonstrates the crucial requirement for efficient security protocols. Consequently, it proceeds by examining the interdependence between DL and security, highlighting the security enhancements provided by DL to IoT. This study examines several endorsements of the DL-based technique for detecting many assaults across a diverse range of IoT environments, such as DSOS, DDoS, probing, user-to-root, remote-to-local, botnet, spoofing, and man-in-the-middle attacks, among others. In addition, the work provides a comprehensive description of deep learning techniques and the difficulties that arise when integrating deep learning-based security solutions in the context of the Internet of Things (IoT). Furthermore, the effectiveness of (DL) in enhancing IoT security has been confirmed by detailed case studies and real-life experiences. The poll also conducts a thorough analysis of security requirements in 5G IoT networks, emphasising the importance of understanding the vulnerabilities that may arise during times of crisis and chaos. This survey focuses on the comprehensive analysis of the delicate relationship between IoT, DL, and security. It provides numerous options for gaining a deeper knowledge and effectively managing the difficult security challenges in the IoT ecosystem.
Copyright (c) 2025 Zainab Ali Abbood, Haider D. Albonda, Nabaa Ahmed Noori https://creativecommons.org/licenses/by/4.0
Enhancing Digital Infrastructure Security in Education: Securing Access and the Moodle Platform
This study is to analyse the implementation of a Learning Management System (LMS), and its improvement the (LMS) at the Islamic University of Lebanon, with a particular focus on improving the security of the (LMS) as regards of keeping sensitive data and maintaining a secured reliable e-learning environment. The paper discusses best practices, and what both the technical and organizational security measures that must be adhered to in order to secure either of these platforms, should be.Technically, the research provides cloud hosting services for hosting Moodle platform, such as Microsoft Azure, which provides advanced features in security, including safeguarding against cyber threats, advanced data encryption and so on. Additionally, It showed that encrypting data-in-transit, as it moves from the source to the destination, with secure protocols helps to mitigate the risk of data breaches or interception by unauthorized third parties.The two-factor authentication is provided as the primary user account protection mechanism, according to the research recommendation from a user perspective. The guide also suggests making passwords that are a combination of upper and lower case letters and symbols, as well as not including any personal information within the password. As well as that, he also promotes application (mobile phones or email) authentication to improve the level of security of the login solution. In this study, we want to present a systematic and practical guide to enhance the security of e-learning platform in academic settings, integrating cloud-based technologies with right security mechanisms at the level of devices and the level of users.
Copyright (c) 2025 Husham Abd AL-Kareem, Hadeel M Saleh https://creativecommons.org/licenses/by/4.0
Enhancing Cyber security in Smart Cities: Challenges and Solutions
Leveraging the advanced technologies such as Internet of Things (IoT), Big Data, Artificial Intelligence (AI) and so on, smart cities are a new type of urban model to improve people's quality of life and resource allocation. By all accounts, smart cities — no matter how shiny and hyper connected they may be are on a cyber-odyssey. A lot of threats to data integrity and availability of critical infrastructure are originated from this end like privacy leakage so on. As research objective, this paper mainly intended to investigate the underlying cyber challenges in smart cities comprising vulnerabilities of IoT devices, data privacy issues and no legal codes for governing IT/communications infrastructure. It further suggests generalist solutions, such as the utilization of hybrid security hardware smart secrecy algorithms and machine learning controlled systems for video surveillance/spy camera’s or the use of block chain processes used more commonly across all fields. The study provides valuable advice for policy makers, entrepreneurs and practitioners to facilitate the development of smart cities for a safer environment and people as well as plants and animals.
Copyright (c) 2025 Wisam K. Jummar , Osama Khamees Ali , Hadeel M. Saleh https://creativecommons.org/licenses/by/4.0
GAM: Introducing a Novel Algorithm for Advanced Ultralightweight Cryptography for IoT devices
This paper introduces the Generic Algorithmic Security (GAM) algorithm, a groundbreaking advancement in ultralightweight cryptography tailored for the quantum computing era. As quantum computing chal-lenges traditional cryptographic methods, GAM emerges as a robust and adaptive solution. It employs innovative features such as Self-Healing Cryptographic Keys (SHCK) and Energy-Aware Dynamic En-cryption Scaling (EDES), ensuring resilience and optimising encryption based on real-time energy and computational constraints. Our study reveals that GAM outperforms existing algorithms like SIMON and SPECK in speed, energy efficiency, and update flexibility. Designed for resource-constrained environments like IoT devices and wearable technology, GAM offers quantum-proof security while maintaining opera-tional efficiency. We delve into its technical architecture, detailing its encryption and decryption processes, and conduct a thorough security analysis showcasing GAM's exceptional adaptability to current and future cyber threats. The results demonstrate GAM’s superior performance, achieving a 20% increase in encryp-tion speed and a 15% improvement in energy efficiency compared to SIMON and SPECK. Additionally, GAM’s update flexibility scored 30% higher, indicating its proactive adaptability to evolving threats. These findings highlight GAM’s potential as a future-proof solution, providing robust protection and operational efficiency in diverse digital environments. GAM represents a significant leap forward in cryptography, offering a future-proof solution to safeguard digital information against the challenges posed by quantum computing, underscoring the importance of innovative cryptographic solutions in securing tomorrow's digital landscape.
Copyright (c) 2025 Ghada Al-Kateb https://creativecommons.org/licenses/by/4.0
Integrating AI-Driven Deep Learning for Energy-Efficient Smart Buildings in Internet of Thing-based Industry 4.0
The integration of Industry 4.0 technologies has paved the way for rapid advancements in smart, energy-efficient buildings. This research focuses on optimizing energy consumption in IoT-enabled infrastructures through the application of data-driven modeling techniques. A comparative analysis is conducted using several machine learning and deep learning models, including Random Forest (RF), Gradient Boosting (GB), Deep Neural Networks (DNN), and Artificial Neural Networks (ANN). These models are trained and validated using real-world datasets, with appropriate preprocessing methods applied to enhance data quality. Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Scatter Index (SI) are used to measure performance. The findings suggest that RF and GB models strike a practical balance between accuracy and computational efficiency, while DNN delivers the highest predictive accuracy but demands significantly more processing power.
Copyright (c) 2025 Mohamed C. Ghanem , Said Salloum https://creativecommons.org/licenses/by/4.0
Artificial Intelligence of Things: A Review
The integration of Artificial Intelligence (AI) and the Internet of Things (IoT), referred to as the Artificial Intelligence of Things (AIoT), has rapidly evolved into a transformative technological paradigm influencing sectors from smart cities to healthcare, agriculture, and beyond. This review explores the convergence of AI and IoT, highlighting the benefits, challenges, and socio-technical implications of their fusion. While AIoT systems promise operational efficiency, sustainability, and enhanced decision-making capabilities, they also pose pressing ethical concerns, especially around data privacy, security, and the societal impact of automation. Through a structured review of thirty recent scholarly contributions, this paper identifies major research themes, gaps in the literature, and opportunities for interdisciplinary innovation. The study emphasizes the need for balanced development—ensuring that technological advancements are accompanied by robust policy frameworks and a nuanced understanding of their social consequences. Ultimately, the paper argues for a more holistic and human-centered approach to AIoT innovation to ensure its responsible and equitable integration into society..
Artificial Intelligence of Things (AIoT)
Copyright (c) 2025 Khder Alakkari , Bushra Ali https://creativecommons.org/licenses/by/4.0
Interoperability in Internet of Things: Taxonomies and Open Challenges
This research investigates the existing taxonomies of interoperability within the Internet of Things (IoT), focusing on the critical gaps and challenges that impede seamless integration across various IoT systems, particularly in the healthcare sector. It addresses the key issue of disparate standards and protocols that complicate interoperability, utilizing a comprehensive analysis of qualitative and quantitative data, including case studies, technical specifications, and user experiences. The research identifies significant barriers such as data silos, inconsistent protocol adoption, and the lack of standardized frameworks, highlighting a need for cohesive solutions that foster interoperability. The findings reveal that enhancing interoperability can lead to improved patient outcomes, greater operational efficiency, and reduced costs, thereby underscoring the importance of standardized communication between devices and systems in healthcare. The implications of this study extend beyond technical advancements, suggesting that addressing interoperability challenges could catalyze a paradigm shift towards more integrated, patient-centered healthcare delivery models. By proposing actionable solutions, this research not only contributes to the academic discourse on IoT interoperability but also offers practical insights for stakeholders aiming to optimize the use of IoT technology in health-related applications.
Copyright (c) 2025 Toufik MZILI, Mourad Mzili, Saif Islam Bouderba, Ahmed Abatal, Widi Aribowo, Adarsh Kumar Arya https://creativecommons.org/licenses/by/4.0
An Extensive Examination on IOT and Industrial IOT: Attacks, Security, Detection Methods, Blockchain Solutions and Challenges
The Internet of Things (IoT) has consistently expanded in both the quantity of devices implemented and the variety of applications in which these devices are used. They exhibit significant variations in terms of their dimensions, processing capabilities, storage capacity, and energy consumption. The IoT has facilitated the ongoing digitization of society in several ways during the last decade. The IoT is an extensive network of interconnected smart gadgets that exchange data via web channels. The security aspect of IoT is of utmost importance due to its fast proliferation as a new technological paradigm, since it may include safety-critical operations and the digital storage of confidential information. Regrettably, the foremost obstacle that arises when using IoT technology is security. Consequently, enhancing the security of IoT devices is now the primary focus for manufacturers and researchers. There is a significant amount of literature that covers various challenges related to the topic and offers viable solutions. However, the majority of current research lacks a comprehensive viewpoint on assaults inside the IoT. Therefore, the purpose of this survey is to create a framework that will assist researchers in classifying attacks in a taxonomy based on different factors, including attack domains, threat types, execution methods, software surfaces, IoT protocols, device properties, adversary locations, and levels of information damage caused by the attacks.
Copyright (c) 2025 Karthik Kumar Vaigandla https://creativecommons.org/licenses/by/4.0
Developing IoT Performance in Healthcare Through the Integration of Machine Learning and Software-Defined Networking (SDN)
In this era of rapid growth of the Internet of Things (IoT) in healthcare, the networking solution must be robust and efficient for smooth data transfer and the network. It is very well established that Deep Learning (DL) is a subset of Machine Learning (ML) and has shown great promise in enhancing network performance through intelligent data analysis and making smart decisions. The wide deployment of Wireless sensor networks and Mobile ad hoc networks (MANET) is why they are used in healthcare applications, as they offer low-cost deployment and real-time data exchange. Nevertheless, WSNs suffer limitations in energy availability due to their inherent constraints. However, the energy consumption in a WSN routing is a significant problem because of resource limitations. Integrating ML in a Software Defined Networking (SDN) framework shows the possibility of network resource optimization and a sustainable network architecture. Typically, conventional routing annoys most about shortest path algorithms, where nodes are used unevenly, and critical nodes die early from battery depletion. This work proposes a new DRL-based algorithm for maximizing traffic distribution and maintaining energy consumption equilibrium across the nodes in WSNs to improve their lifetime. The model proposes dynamically changing routing paths to preserve the nodes’ loading with a limited power supply system to persist the network lifespan and thus improve efficiency. However, the approach adds some hops to the average packet transmissions per hop. Still, it delays the first node’s energy depletion long enough to provide sustained network operability in IoT-driven healthcare environments. The experimental results validate the effectiveness of the proposed method in furthering the network functionality and reliability for data transmission, thereby making it a viable solution for the next generation of IoT healthcare networks.
Copyright (c) 2025 Murtadha Shujairi https://creativecommons.org/licenses/by/4.0
Integrated A Robust Intelligent System For Secure Network
An increasing number of assaults on network security that are related to the Internet are caused in malicious software. Firewalls, intrusion detection systems (IDS), and proxy servers that check for viruses are examples of safety technology that have been used by organizations that value safety. Not all high-priced safety features are easy to use. Having only one safety mechanism in place is insufficient to protect users' private information on privacy networks due to the fact that multiple safety schemes exhibit distinct traits, particularly hostile and DoS attacks. Integrating intrusion detection subsystems with different approaches to network security protection is detailed in this article. The subsystem employs AI techniques to detect unusual actions taken by known attackers and threats using the patterns recorded in the system's memory. Conversely, in order to prevent spyware documents from accessing the network, the proposed Intelligent Network Security System which contents intelligent web filter ,intelligent networks intrusion detection systems ,intelligent firewalls ;could detect the malicious URL and websites.
Intelligent networks-intrusion detection
Copyright (c) 2025 Tamara Saad Mohamed , Saad Mohammed khalifah https://creativecommons.org/licenses/by/4.0
Leveraging the Internet of Things, Remote Sensing, and Artificial Intelligence for Sustainable Forest Management
Sustainable forest management is vital for addressing climate change, biodiversity loss, and deforestation. Human-induced stresses on forest ecosystems demand innovative approaches to ensure long-term health and productivity. This study explores how cutting-edge technologies, including the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), enhance sustainable forest management practices. Researchers reviewed 196 studies published between 2021 and 2024 from IEEE Xplore Digital Library, MDPI, Taylor & Francis, ScienceDirect, Frontiers, Springer, SAGE, Hindawi, Nature, Wiley Online Library, and Google Scholar. The findings highlight IoT devices like drones, enabling real-time data collection on temperature, humidity, soil moisture, and tree growth, facilitating continuous forest monitoring. Remote sensing technologies, utilizing satellite imagery and aerial surveys, deliver high-resolution data for large-scale forest assessments, including forest cover changes, biomass estimation, and early detection of illegal logging. When integrated with AI, these tools enhance predictive modeling, data analysis, and decision-making, leading to more effective forest management strategies. The study also identifies challenges such as data security concerns, bandwidth limitations, interoperability issues, and high costs. Despite these barriers, IoT, remote sensing, and AI present transformative potential for improving forest resilience, carbon sequestration, and biodiversity conservation. These technologies are crucial in preserving forest ecosystems and mitigating climate change impacts by advancing real-time monitoring, optimizing resource allocation, and enabling data-driven decisions.
Copyright (c) 2025 Guma Ali , Maad M. Mijwil , Ioannis Adamopoulos , Jenan Ayad https://creativecommons.org/licenses/by/4.0
An Author-Centric Framework Error Minimization in Scholarly Recommender System (Acfemsr)
In the current landscape of computational innovations, the internet hosts a vast repository of publications sourced from various channels. Consequently, researchers encounter unprecedented challenges in identifying publications relevant to their research interests. Navigating through the multitude of options provided by search engines is not only impractical but also prone to selection errors – termed Recommendation Error (RE). These experiences underscore the need for novel research avenues. Previous studies have proposed numerous recommendation frameworks exclusively for scientific paper recommendations. However, many of these approaches have been plagued by RE, thereby compromising the integrity of recommendation systems. Recommendation Errors typically arise from underutilization of key features in the recommendation process. To mitigate Recommendation Errors, this study leverages publicly available metadata features, including Title, Abstract, Author(s), and keywords. Feature vectors for each candidate paper (CP) and the paper of interest (POI) are computed using CountVectorizer, and cosine similarity formula is employed to identify similar papers suitable for recommendations. The effectiveness of the proposed framework is evaluated using Error evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Finally, the proposed Framework is compared against two previous Baseline approaches. The evaluation results demonstrate that the proposed approach exhibits lower Recommendation Errors compared to its baseline counterparts. Additionally, this research highlights the Author(s) feature as the most influential among the four features utilized by the Proposed Framework.
Copyright (c) 2024 A.H. Zaharaddeen, Nurra Mukhtar https://creativecommons.org/licenses/by/4.0
Integrating IoT Technologies with Open Educational Resources (OER) for Enhanced Learning Experiences During the COVID-19 Pandemic
Consequently, this research focuses on the blended application of IoT with OER within college students learning in the context of the COVID-19 outbreak. As more education is moving to the online setting, this study examines if there is an improvement in learning achieved through the integration of open educational resources with Internet of Things-based educational appliances. Two groups of students were surveyed: those studying traditionally in face-to-face classes before the pandemic and those studying online during the pandemic. In the 2019 survey, out of the 912 students who responded, while in the 2021 survey, 1018 students responded. They found that an overwhelming number of students across the two groups felt that OER particularly the open online courses and the open-access textbooks were more advantageous during the pandemic. The current study revealed that distance learners benefited most from OER as supplementary sources of material as opposed to traditional learners where OER was deemed relevant for examinations and tests. Each group, however, mooted some concerns about the nature and range of OER that is available in the market. The findings show that IoT adaptation in combination with OER can solve the challenges students face in different learning conditions while increasing education access and quality during crises.
Copyright (c) 2023 Yasser Taha Alzubaidi , Safa Hussein Oleiwi https://creativecommons.org/licenses/by/4.0
Construction a virtual central network for all Iraq's fire station
Fire stations are crucial in Iraq for protecting human life, property, and vital economic and religious infrastructure. In this paper, a design for a centralized virtual network that brings together all fire stations in Iraq is proposed. The network consists of 18 stations, with each station representing a governorate in Iraq. When a fire occurs in a certain area, citizens can request the service by entering their personal information and the location of the event, which is sent to the centralized network that transfers it to the appropriate station in the area. If the station is not available in the area, the request is sent to the nearest other station to fulfil the request as quickly as possible. This virtual communication network allows for better coordination and simplifies data sharing and communication between different stations. The results showed that the centralized network design contributes to fast response times, rapid response to requests and rapid deployment of fire engines to the incident site. Fire stations can also increase overall efficiency by improving coordination, allocating resources more effectively and monitoring situations in real time. Thanks to the centralization of data, all stations have access to the same set of information, enhancing decision-making, especially in the case of large-scale incidents. Also, the network design is characterized by a high level of security.
Copyright (c) 2024 Wasnaa Hadi Ghasab, Ahmed Adnan Hadi , Alaa.G.K. Alshami , Ahmed Dheyaa Radhi, Rusul Mansoor Al-Amri, Ayad Hameed Mousa https://creativecommons.org/licenses/by/4.0
Revolutionizing IoT Security in the 5G Era with the Rise of AI-Powered Cybersecurity Solutions
This study involves a detailed analysis of how AI can be employed in improving IoT security. However, with the surge of IoT, these systems which require device data interchange through cloud and wireless connections have become vulnerable to various types of cyber threats. Storms, hacking attempts and similar contingencies create high risks, financial losses and even the impact on people. This survey investigates IoT, and 5G Technology along with Cybersecurity in terms of new emerging issues and challenges and states the necessity of strong security measures. From the case of the IoT environment, the paper explores the following cyberattacks; DDoS, probing, U2R, R2L, botnet, spoofing, and MITM attacks. It assesses Artificial Intelligence-based approaches such as ML and DL that enhance IoT security by identifying, responding, and blocking these threats. Further, this survey discusses the issues related to deploying AI-based security solutions in the IoT context, and the particular focus is made on aspects such as scalability, real-time reconfigurability, and privacy concerns. Real-life cases as well as research findings are provided to support the readiness of AI in protecting IoT environments. This paper also discusses the 5GIoT network security requirements and concerns identifying new methodologies for preventing new security threats in this advanced network. This survey provides a complex picture of the interconnection between IoT, AI, and cybersecurity to help researchers and practitioners design novel complex security solutions for the newest IoT systems.
Copyright (c) 2023 Juan H. Namdar, Janan Farag Yonan https://creativecommons.org/licenses/by/4.0
Wheelchair Control Using Finger Gestures IoT-Based Arduino
This paper presents the development of a wheelchair control system using Arduino and wireless finger gestures. The system incorporates various components such as Arduino Uno and Arduino Nano boards, ultrasonic and IR sensors, an LN298 motor driver module, motors, an IR flame/fire sensor, a buzzer, batteries, and HC12 wireless modules. The flex sensor is utilized to detect finger movements and gestures, while the ultrasonic and IR sensors provide obstacle detection capabilities. The Arduino boards process the sensor data and interpret the finger gestures, mapping them to specific commands for wheelchair control. The LN298 motor driver module interfaces with the motors to enable the execution of the desired motions based on the recognized gestures. IR flame/fire sensor monitors fire, and the buzzer provides audible feedback. The HC12 wireless modules facilitate wireless communication between the Arduino boards. The paper aims to create an intuitive and efficient control system for a wheelchair, allowing users to navigate their surroundings using wireless finger gestures while incorporating obstacle detection and air quality monitoring features.
Copyright (c) 2024 Rana Ali Salim https://creativecommons.org/licenses/by/4.0