The Babylonian Journal of Networking (EISSN: 3006-5372) focuses on cutting-edge advancements in networking technologies. This specialized publication invites researchers to share high-quality contributions covering computer networks, wireless communication, network security, and the Internet of Things (IoT). Through rigorous review, it aims to disseminate innovative insights and foster collaboration in this dynamic field
FedTrans6G: Federated Transformer Framework for Privacy-Preserving Resource Management in 6G-Enabled Consumer IoT Ecosystems
The rise of 6G technologies and the proliferation of consumer IoT devices introduce critical challenges in achieving scalable, low-latency, and privacy-preserving intelligence at the network edge. Traditional federated learning (FL) frameworks, while decentralized, often fall short in addressing heterogeneity, communication efficiency, and data privacy under 6G constraints. To overcome these limitations, we propose FedTrans6G, a novel Federated Transformer Framework designed for secure and adaptive resource management in 6G-enabled IoT ecosystems. FedTrans6G integrates lightweight transformer-based models with hierarchical federated learning, enhanced by differential privacy and homomorphic encryption to ensure end-to-end confidentiality. The framework features an adaptive resource allocation mechanism that leverages transformer attention scores for real-time optimization across edge, fog, and cloud layers. We validate FedTrans6G through extensive simulations using real-world and synthetic IoT datasets. Empirical results show that FedTrans6G outperforms state-of-the-art baselines in accuracy (+6.2%), latency (−43.8%), and energy efficiency (−30%), while significantly reducing privacy leakage. Ablation studies further confirm the effectiveness of each architectural component. The proposed system demonstrates practical viability for next-generation, privacy-aware, and resource-efficient edge intelligence. FedTrans6G offers a paradigm shift for 6G IoT, bridging the gap between intelligent model design and federated privacy guarantees—paving the way for secure, scalable, and sustainable edge computing infrastructures.
Copyright (c) 2026 Ghada Al-Kateb https://creativecommons.org/licenses/by/4.0
Real-Time Sentiment Analysis Over Social Network Streams Using Deep Learning Models in Edge-Cloud Architectures
This paper describes a real-time sentiment analysis system dedicated to social media streams, specifically from Twitter, using deep learning methods in an edge-cloud infrastructure. The recommendation system we develop aims to the classification of textual information through a hybrid deep model, consisting of both Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. At first, a large-scale data collection approach was carried out to retrieve tweets about different domains, where emphasis was placed on real-world, high volume and noisy datasets. Our classification was composed of two stages: the first of which filters tweets according to the relevance of their topic (given a set of topics), and the second of which classifies the polarity of sentiment in the tweet (positive, negative, neutral, irrelevant). This method extends standard three-class sentiment classification to a four-label model that includes a fourth category to indicate topic irrelevance, resulting in more context-aware analysis of streaming data. In order to support real-time processing and reduce the end-to-end delay, the system was unfolded with a hybrid edge-cloud architecture, that performs initial filtering and light computation at the edge meanwhile deep learning inference and storage. Experiments show that the proposed CNN-LSTM model is superior to some baseline models and is robust with unstructured and noisy social network text. The framework is established by experimentation on a real-world use case, that visualizes Twitter data insights into near-real time, proving its practical benefits for smart observation and public opinion observation. Although encouraging results have been achieved, more improvements are still desired in dynamic visualization and sensitivity to topic drift, indicating that real-time sentiment analysis is still a challenging and growing research topic.
Copyright (c) 2026 Memory Ayebare , Simon Mugisha, Asiimwe Julius https://creativecommons.org/licenses/by/4.0
Blockchain and Deep Q-Learning for Trusted Cloud-Enabled Drone Network in Smart Forestry: A Survey
The convergence of drone technology, cloud computing, and intelligent decision-making is revolutionizing precision forestry. However, deploying large-scale drone networks in smart forestry faces challenges such as trust, security, data integrity, and autonomous coordination. This survey examines how combining Blockchain technology with deep Q-learning (DQL) can address these issues within cloud-enabled drone networks. Drawing on 102 peer-reviewed sources published between 2022 and 2025 from reputable platforms such as ACM Digital Library, Frontiers, Wiley Online Library, PLoS, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, and Google Scholar, this work highlights recent advancements in secure and intelligent drone ecosystems. Blockchain provides a decentralized, tamper-resistant framework for validating transactions and securing data exchange among autonomous drones, ensuring the integrity, confidentiality, and authenticity of environmental data. This is critical in forestry, where data manipulation and unauthorized access pose significant risks. Complementing this, DQL enables drones to make autonomous decisions by interpreting real-time environmental data and learning from past experiences, allowing drones to adjust their flight paths, optimize resource utilization, and enhance data collection in dynamic forest environments, such as wildfires or illegal logging operations. Together, Blockchain and DQL create a resilient, scalable architecture that supports secure, real-time, and intelligent forest monitoring. This framework lays the groundwork for developing autonomous and trustworthy drone networks that promote sustainable and climate-smart forestry management.
Copyright (c) 2025 Guma Ali, Wamusi Robert , Maad M. Mijwil, Hassan A. Hameed Al-Hamzawi , Ali S. Abed Al Sailawi , Ayodeji Olalekan Salau https://creativecommons.org/licenses/by/4.0
Smart Hybrid Intrusion Detection for IoT Networks Using Machine Learning and Neural Networks
With the fast growth of Internet of Things (IoT), there is an increasing security impact, e.g., IoT environments has become the advanced target of complex cyber-attack like Distributed Denial of Service (DDoS) attack. Most conventional Intrusion Detection Systems (IDSs) fail to scale with the heterogeneity, scale, and dynamism of IoT networks. To overcome these challenges, in this paper, we develop a smart hybrid intrusion detection framework that can effectively utilize the power of Machine Learning (ML) and Neural Network (NN) models, namely: cascades backpropagation neural network (CPBNN) and convolutional neural network (CNN) to improving accuracy and adaptability of detection in IoT environments. The proposed system employs a two-layer detection structure where the CPBNN model is used to detect abnormal patterns in the network packet level and identify the abnormal behaviors of packet sequences, whereas the CNN model performs deep feature extraction and classification to predict the abnormality as well as the nature of the abnormality. We develop this hybrid architecture to work well under IoT level large-scale deployments without incurring software overhead. The system has been evaluated on the KDDTest-21 benchmark dataset with the usual metrics, including accuracy, precision, recall and F1-score. Experiments results prove the CNN’s performance with 90% and CPBNN with 82% which valid the efficiency of the proposed method in detecting IoT related security threats. These results suggest the ability of intelligent hybrid IDS systems not only to defend in a pro-active manner using ML and neural networks in synergy, but also to use the adversary's strength against itself.
Cascade Backpropagation Neural Network (CPBNN),
Copyright (c) 2025 Klodian Dhoska , Panagiotis Kyratsis , Marek Dudek , Phani Praveen Surapaneni , Aaron Mogeni oirere https://creativecommons.org/licenses/by/4.0
Optimized Solutions for Robust and Efficient Two-Factor Authentication in Networking Environments
With the rise of escalating cyber threats to the present-day networking environment, the traditional two-factor authentication (2FA) mechanisms remain ineffective in mitigating the sophisticated attack vectors like phishing, SIM-swapping and social engineering. These loops holes, specifically in SMS- and email-based 2FA, are opening users and network infrastructure up to substantial danger. This paper presents optimized and robust enhancements to 2FA technology, and points out cryptographic technologies like ECC, the addition of X.509 digital certificates, and biometric and behavioral authentication solutions. Then, the performance of these distributed trust models is compared, in terms of security efficiency, usability, and deploy ability, with a complete comparative study of these models in dynamic networking. The paper also includes real-world examples of implementing such multi-layered 2FA schemes being tensioned between a strong security protection and what is deemed to be user acceptable. The results showcase best practices and challenges in building secure and efficient as well as future-proof authentication systems that match the requirements of complex network environments.
Copyright (c) 2025 Hussein Alkattan , Raad S. Alhumaima, Amr Badr , Peter Mwangi https://creativecommons.org/licenses/by/4.0
Enhancing Wireless Sensor Network Lifetime through Fuzzy-Based Execution of the LEACH Protocol
Introduction Wireless Sensor Networks Wireless Sensor Networks (WSNs) consist of a number of physically disperse sensor nodes which use wireless communication to do sensing and retrieving information from the physical world. In a network of battery driven and energy limited nodes, the network lifetime is an important concern. Driven by the above mechanism, as a cluster of nodes is frustrated, the function of the entire network drops dramatically or even becomes inefficient. Low-Energy Adaptive Clustering Hierarchy (LEACH) LEACH is one of the most well-known energy-efficient clustering protocols for making wireless sensor networks more sustainable by grouping nodes and moderating the group head. However, in LEACH protocol, the operation of cluster head is selected randomly, which might lead to the waste of energy in the node. Here, this paper proposes an enhanced LEACH that integrates fuzzy logic-based decision-making with cluster head selection. The fuzzy-centric LEACH versions improve significantly in terms of energy consumption and network lifetime through the involvement of some parameters such as residual energy, distance to BS, and the density of sensor nodes. Simulation results confirms that the proposed fuzzy implementation of LEACH outperforms the existing classical variant and gives a feasible solution for WSN deployment environment.
Copyright (c) 2025 Zakaria Benlalia , Toufik Mzili , Mustapha Hankar , Ahmed abatal , Mourad Mzili , Nebojsa Bacanin , Momina Shaheen , Musaria Karim Mahmood https://creativecommons.org/licenses/by/4.0
Leveraging Artificial Intelligence to Address Network Congestion Challenges in IoT Systems
The explosion of Internet of things (IoT) devices has led to pretty much saturated network infrastructures, thus congestion often becomes severe, especially in applications where users require low latency, high throughput and real-time responses. Hence, traditional congestion control mechanisms like static routing protocols, Active Queue Management (AQM) and TCP variants are not suitable for this dynamic and heterogeneous IoT environment as they are reactive, rigid and cannot adapt to dynamic changes. The contribution is to investigate the potential of AI paradigms—such as ML, DL, RL and hybrid models- to provide Intelligent and proactive congestion Control in IoT systems. The paper compares the strengths and weaknesses of each method, such as RL’s generalizability and DL’s ability to capture patterns, as well as limitations in terms of scalability, interpretability, and computational resource requirements. Moreover, it emphasizes important research gaps in model generalization process, evaluation criteria and cross-platform fusion. Next steps in research discussions on the future of research take into account lightweight AI architectures, Explainable AI (XAI) frameworks, scalability of FL, and standard benchmarking data sets. The hybrid-AI congestion prediction model built in this work is validated across simulation tools and real-data sets and is observed to result in a significant decrease in latency, packet loss and energy consumption. This paper paves the way for scalable, intelligent and secure AI-based congestion management solutions for various IoT networks.
Explainable Artificial Intelligence (XAI).
Copyright (c) 2025 Fredrick Kayusi , Harshit Mishra , Petros Chavula , Kassem Hamze https://creativecommons.org/licenses/by/4.0
Recognizing Node Intrusion Tendencies in IoT Environments via Deep Learning and Network-Level Feature Analysis
The IoT environment is becoming more and more susceptible to intrusion attacks due to its decentralized infrastructure, fewer security boundaries and heterogeneity and dynamism of the network. Discovering malicious parties in these environments is a challenging task, mainly due to the mobility of the nodes and the intermittent contact between them. This study introduces a deep learning-based approach to detect intrusion tendencies on the node level based on the analysis of essential network-level features. The link duration, self-healing latency and number of packets that potential attacker nodes receive were obtained from an analytic network profiling model. These were then employed in the training and testing of the three deep learning models: Feedforward Neural Network (FFNN), Cascade Backpropagation Neural Network (CBPNN), and Convolutional Neural Network (CNN). Of those, the CNN showed the best performance, obtaining intrusion detection accuracy of 85.5%. The proposed approach emphasizes the importance of combining network behavior analytics with deep learning technologies to increase the security of IoT environments.
Copyright (c) 2025 Ioannis Adamopoulos , Aida Vafae Eslahi, Harshit Mishra , Niki Syrou , Tirus Muya https://creativecommons.org/licenses/by/4.0
Optimizing 3D Model Visualization over Wireless Networks for Responsive Web Applications
In the era of interactive experience and immersive technology, rendering 3D model in responsive web applications must be made efficient, especially over wireless networks with varying bandwidth and latency restrictions. Older platforms, in particular, tend to have a hard time rendering high compression rate 3D polylines (such as point clouds and geometric meshes) that result from sophisticated processes such as LiDAR scanning, digital photogrammetry, and reverse engineering. Such models are getting larger and more detailed and can put heavy computing pressure on rendering engines and network transmissions. In this paper, we present a lightweight, low-cost 3D visualization framework for web-based application based on the procedural modeling. The Engine is based on MATLAB and makes use of rule-based algorithms for the generation of 3D structures, and an own CGA (Computer Generated Architecture) module, which allows dynamic geometry creation. Our approach efficiently preprocesses and streams 3D data, enhancing its rendering quality and user experience. In addition, it overcomes the restrictions in existing visualization pipelines through adaptive rendering according to client-side capabilities and network conditions. The developed system is a precursor to web-based GIS, CAD and remote visualization in low resource environments.
Copyright (c) 2025 Mohammed Almaayah, Rami Shishakly https://creativecommons.org/licenses/by/4.0
ACO-Based Path Optimization Inspired by TSP for Routing Efficiency in Communication Networks
Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem, and it is generally concerned with searching for this shortest tour that passes through each of a collection of cities and returns to the point of departure. In recent years the properties of TSP have proved to be very transferable to network routing problems where the ability to select optimal paths is imperative for maintaining low congestion and efficient communication. In this paper, we have studied the application of the established swarm-based optimization algorithm, Ant Colony Optimization (ACO), for solving routing problems in communication networks under a basic TSP-inspired model. The research covers the critical issues such as network congestion, routing delay and slow convergence scenario that are often present in dynamic network scenario. In order to improve the performance of the ACO, the proposed algorithm combines candidate set to assess path quality and applies the mechanism of adaptive adjustment of parameters to enhance the search and accelerate the convergence. The approach is suitable for routing in various simulated network scenarios and is shown to be more efficient and stable.
Copyright (c) 2025 Mohammed Amin, Mohamed Chahine Ghanem https://creativecommons.org/licenses/by/4.0
Securing Distributed IoT Routing Networks Against DDoS Attacks Using Intelligent Machine Learning Techniques
The rampant deployment of the Internet of Things (IoT) has increased the data traffic in the interconnected devices, which has also raised the cybersecurity concerns in IoT networks, especially the DDoS attacks targeting IoT. Conventional security approaches like password encryption/authentication to be broken are unsuitable for twarthmanaging these sophisticated, changing network threats, particularly in a distributed computing-based routing structure. This paper presents a holistic ML-driven framework that detects and mitigates DDoS attacks aimed at distributed IoT routing systems. The solution uses SVM, RF and DT supervised machine learning algorithms to classify malicious network features and increase the ability to detect intrusion mechanisms in real time. The models are based on historical network traffic data which are used to identify anomalous patterns and forecast future attack vectors. Performance evaluation is performed using important classification matrices such as confusion matrix, F1-score and AUC-ROC in order to ensure effective treatment for imbalanced datasets. Experimental results: The results were said to have been used on the Random Forest algorithm that gives ac-curacy of 99.2%, with 0.8% false positive rate, and 0.997 concordance index which is equivalent to the AUC-ROC score. The results substantiate the efficiency of self-learning intelligent machine learning based approach in hardening IoT routing networks for counteracting complex form of DDoS threats in the distributed domain.
Copyright (c) 2025 Mohammed Almaiah, Udit Mamodiya https://creativecommons.org/licenses/by/4.0
Network-Based Intrusion Detection in IoT Environments Using Hybrid Machine Learning Techniques
The recent proliferation of the Internet of Things (IoT) has emerged many security threats, especially on network-based communication platforms. With the increase of cyber-attacks against critical systems, in particular DDoS, e-fraud type or other, the demand for intelligent, adaptive IDS is clearly important. In this context, this paper proposes a hybrid machine learning model integrating traditional algorithms like Support Vector Machines (SVM) and emerging deep neural networks to improve the precision and robustness of detection in IoT systems. The performance of the proposed framework is tested on various benchmark datasets which includes NSL-KDD, DS2OS, and IoT Botnet using various performance parameters such as Accuracy, Precision, Recall, F1-score. The suggested approach yielded a high detection rate of 96.38%, indicating the capability of our system to pinpoint sophisticated intrusion instances on multi-type IoTNs. Experimental results demonstrate that the hybrid solution has the potential for false positives reduction as well as for enhancing response against the threat in real environment. This study is an attempt to bring cyber-security solutions for the emerging landscape of IoT infrastructures, which are scalable, adaptive networked based and intelligent.
Copyright (c) 2025 Mansour Obeidat, Rami Shehab https://creativecommons.org/licenses/by/4.0
Applying Keras-Based Deep Learning for Intelligent Analysis in Network Security and Monitoring Systems
With the advent of digital age, network access and protection of sensitive data from unauthorized access or use has been a great challenge. Face detection and recognition is becoming a prevalent method in network security system by utilising the biometric principles. In this survey, we use Convolutional Neural Networks (CNNs) and the Keras deep learning framework to improve network security by building efficient face detection systems. A high-level and user-friendly API implemented by Keras (over TensorFlow), which makes it very easy to use deep learning models for tasks such as face detection. Being multi-GPU ready and distributed training friendly alongside compatibility with OpenCV and TensorFlow enables it to be used in developing reliable, secure, real-time face authentication systems. In this paper, we review the statistics of CNN models for face detection, compare performance of the Keras models on multiple datasets, and show applications of securing a network such as login authentication and access control, as well as real-world uses for surveillance system. In addition, the survey details applications of face detection where the possibility of face spoofing is an issue and current research directions on the topic. Utilizing CNNs with Keras can enable the development of more flexible, performative, and accurate biometric authentication systems, and these systems play a critical role in the global cybersecurity ecosystem.
Copyright (c) 2025 Syfian Yousef https://creativecommons.org/licenses/by/4.0
Securing Virtual Private Networks Against Cyber Threats Using Feedforward Neural Networks
The virtual private communication channels are now commonly used over the internet and the overall trend of internet growth is also used as a server to carry the clients of VPN. Virtual Private Networks (VPN's) are central to secure and encrypted communication over both public and private networks. Nonetheless, conventional VPNs lack the ability of detecting and coping with advanced cyber-attacks. In this paper, we propose a new architecture that combines with FFNN architecture in the VPN system to improve VPN based on cyber-event real-time detection and reaction to the threat and intrusion detection VPN system. The model is capable to learn Input: Network traffic patterns from which the proposed FFNN based model identifies anomalous and intrusion attacks, and specifically within HTTP payload. The performance of the model is evaluated in terms of the key performance measures such as throughput, latency, and detection accuracy. Experimental results show that FFNN-VPN framework can achieve a detection rate of 98% with acceptable latency and network efficiency. Integrating deep learning with the VPN infrastructure can provide a forward-leaning approach in cybersecurity that is more flexible as the threat scene evolves.
Copyright (c) 2025 Thaker Nay https://creativecommons.org/licenses/by/4.0
Advanced 6G Network Protection Using Quantum Key Distribution: A Systematic Review
With the advent of 6G communication systems on the horizon, ensuring that they are secure from quantum computing threats, in a post-quantum era, is of paramount importance. Quantum attacks, computational hardness-based classical cryptographic algorithms are becoming more susceptible to quantum attacks. Quantum Key Distribution (QKD) has been proposed to address this challenge, which can provide unconditional security based on quantum mechanics for establishing security cryptographic keys. In this work, we review how QKD could fit into the 6G design as part of the general siytematic vision of security exploring its potential implementations, the technical feasibility, and the applications considering both fiber and wireless scenarios. We provide a comparative investigation of performance indicators of QKD systems in simulated 6G scenarios: secret key generation rates, quantum bit error rates (QBER), resistance to noise and user mobility. In addition, hybrid security models between classical post-quantum cryptography (PQC) and QKD to achieve multilayered security protocols are demonstrated. Furthermore, the contribution that could be made by AI/ML to improve QKD performance is analyzed, with particular attention to smart error correction, adaptive key management, anomaly detection, and dynamic routing. Despite significant progress, a number of critical challenges are still open in terms of scale, standardization, interoperability and deployment. In Section VI, we offer a forward-looking research direction, highlighting the importance of the AI empowered QKD, protocols global standardization, and creation of realworld testbeds to drive the QKD from the testbed to the 6G network. It is hoped that the insights provide here will inspire research community and industry partners to build quantum-safe communication infrastructure for the future.
Quantum-Resilient Communication Infrastructure
Copyright (c) 2025 Bassma M. Kamil https://creativecommons.org/licenses/by/4.0
Artificial Intelligence for Medical Diagnostics in IoT-Based Healthcare Networks: Foundations and Future Trends
AI is quickly transforming the landscape of medical diagnostics, leading to remarkable gains in accuracy, speed, and availability. We perform a systematic review on the fundamental strategies, tools, applications, and challenges of AI enabled diagnostic in medicine with focus on medical diagnostics in IoT-based healthcare networks. The paper presents the use of machine learning algorithms, deep learning models, including CNN, and NLP to interpret clinical documentation. It also investigates the usage of smart computing infrastructures such as edge systems and the Internet of Medical Things (IoMT) that facilitates real-time, data-driven clinical decision-making consistently matching or exceeding human-level perception, notably in medical imaging, pathology and biosignal analysis. Nevertheless, there exist great challenges, such as data heterogeneity, lack of high-quality labeled datasets, model interpretability and ethics, such as algorithmic bias and patient privacy. In addition, questions on standards, clinical confidence, and regulation are often considered less important than technical performance but are central to the effective deployment of AI within health systems. This paper presents the comparison of AI-combated diagnostic approaches with the traditional ones, a recent literature review and some research gaps for future work. The study seeks to underpin the need to advance AI systems that can be understood, are clinically applicable and ethically justifiable. It promotes interdisciplinary cooperation and uniformed evaluation methodologies for the safe, efficacious and equitable utilization of AI for healthcare diagnostics.
Copyright (c) 2025 Raghad Tariq Al-Hassani https://creativecommons.org/licenses/by/4.0
Leveraging AI in Mixed Hierarchical Topologies to Improve WSN: A Survey
The Internet of Things (IoT) as Wireless Sensor Networks (WSNs) holds a significant role in various areas: Military surveillance, Industrial automation, Smart housing, Security Systems, Intelligent Vehicular Traffic control, as well as Healthcare monitoring. So, the sensor nodes performance and life-time with limited power, memory and processing capabilities are significant issues to face in the network. To overcome these limitations, a wide range of energy-aware packet forwarding mechanisms and routing protocols have been proposed to optimize network throughput and lifetime. A WSN's performance is strongly dictated by its node deployment approaches, energy consumption profiles, communication latency, and data aggregation techniques. Furthermore, as the aspects of the network topology (chain-based or cluster-based hierarchical) and the criteria for choosing aggregator nodes to further transmit the sensed data to the sink node strongly affect delivery time and energy balance. Existing hierarchical routing protocols are well studied by several surveys, for example LEACH, PEGASIS, PDCH, CHIRON, CCBRP, CCM, TSCP, DLRP and DCBRP. To do so, it underlines why the addition of Artificial Intelligence (AI) to mixed hierarchical topologies is improving decision-making processes, and adaptive clustering while making WSNs more efficient, scalable and resilient in their performance. We further provide a comparative analysis, primarily emphasizing the advantages and limitations of different chain-based and cluster-based AI-assisted routing solutions.
Copyright (c) 2025 Wamusi Robert , Mariem Bounabi , Amr Badr https://creativecommons.org/licenses/by/4.0
A Comprehensive Review on OFDM, 5G and Various PAPR Minimization Techniques based on Machine Learning
The use of modulation methods in Fifth Generation (5G) wireless communication systems is essential for fulfilling the requirements of increased data rates, reduced latency, and enhanced connection. This entails the optimization of power spectral density (PSD), the improvement of data transmission reliability, and the reduction of bit error rates (BER) and interference. Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technique used in 4G and 5G wireless communication networks. OFDM results in a significant Peak to Average Power Ratio (PAPR) in the time domain because of the constructive interference between many subcarriers. This leads to increased complexity and expense of amplifiers, as well as higher costs and complexity of networks. Hence, it is essential to devise novel approaches to mitigate the PAPR in OFDM systems. ML has become a potential approach for addressing PAPR concerns. This study provides a thorough examination of ways for optimizing PAPR, with a specific emphasis on ML approaches.
Copyright (c) 2025 Karthik Kumar Vaigandla , Ranjith Kumar Siddoju https://creativecommons.org/licenses/by/4.0
Evaluation of Information Security through Networks traffic traces for machine learning classification
The classification's traffic is regarded as a significant study domain because to the rising demand among network users. In addition to improving the identification of network services and addressing difficulties related to the security of traffic networks, it also simplifies and improves the accuracy of a broad variety of Internet application modes and activities. Over the course of the last several years, a multitude of traffic classification algorithms have been developed alongside their effective implementation. This paper proposed evaluate the classification issues of information security which happened through networks traffic by using machine learning (ML) classification algorithm the RamdomForst by using NSL- KDD test and NSL-KDD train dataset to show the performance of testing data and training data.
Copyright (c) 2025 Tamara saad Mohamed, Saad mohammed khalifah, Ridwan Marqas , Saman M. Almufti , Renas R Asaad https://creativecommons.org/licenses/by/4.0
Enhanced PV Network Controller for Optimizing Performance of LFC Power Systems
Turkey has made national energy accessibility the top of its priorities. Less than 90% of the nation's 82 million citizens can access electricity today. Workers use solar power and wind energy to provide more electricity across the nation. Solar power wind power and power electronics help Turkey save money as it works to fix its energy issues. Load Frequency Control inveters help Turkey as a developing country reduce solar power costs through advanced power electronics technologies. IC-based electronics improvements let us make energy components work better at less cost compared to previous expensive hardware. LFC inverters help add renewable energy to existing grids according to latest research and can solve energy problems worldwide. The analysis sees solar power providing 35% of Turkey's power output by 2050. Wind energy stands out for its unmatched marginal fuel cost advantage. Utility-scale solar power beats other generation sources because it costs no fuel to produce power while releasing minimal greenhouse gases which supports global decarbonization efforts. The analysis shows that our updated energy model matches actual LFC metering data through similar energy output numbers and trends. Our predicted numbers using the standard Load Flow Control system proved less precise than actual measurements. Our simulation produced 3.9% additional renewable energy estimates than actual meter readings whereas the basic model showed 52% less output. Our revised energy model in MATLAB shows good precision when it uses 10% solar power and 9.90% wind power input. Our hybrid scheme produced precise predictions when combining both solar and wind systems with 57.16% solar and 57.20% wind percentages.
Copyright (c) 2025 Zainab Ali Abbood https://creativecommons.org/licenses/by/4.0
Network Security in AI-based healthcare systems
With the fast integration of artificial intelligence (AI) in healthcare, boosting diagnostics, treatment tailoring, and predictive analytics, securing patient data, and ensuring system integrity have become key challenges. This research examines the network security concerns particular to AI-based healthcare systems, attempting to uncover main vulnerabilities and assess viable protection strategies. Through a mix of systematic review and experimental validation, we tested numerous machines learning models, including convolutional neural networks (CNN), support vector machines (SVM), and random forests, against adversarial assaults that undermine model accuracy and data privacy. Results demonstrated that adversarial attacks might considerably impair model dependability, with accuracy decreases of up to 32% in CNN models under assault. However, adopting defensive strategies like adversarial training and defensive distillation dramatically increased model resilience, with post-defense accuracy rates returning by 15-25%. These results underline the necessity for strong network security policies suited to AI healthcare applications to guarantee both data protection and operational reliability. Our work adds useful insights on the adaptation of AI network security measures inside healthcare, identifying avenues for legislative updates and ongoing research to safeguard upcoming AI-driven health advances.
Copyright (c) 2023 Bourair Al-Attar https://creativecommons.org/licenses/by/4.0
Utilizing Artificial Intelligence in Cybersecurity: A Study of Neural Networks and Support Vector Machines
The rapid evolution of network security threats necessitates the integration of advanced technologies, particularly artificial intelligence (AI), in the development of effective protection systems. This article reviews contemporary methodologies employing AI for enhancing computer network security, with a focus on neural networks (NN) and support vector machines (SVM). It begins by elucidating the architecture of neural networks, including the training and recognition phases essential for detecting malicious activities within a network. The effectiveness of NN in identifying patterns indicative of unauthorized access is highlighted, alongside the challenges associated with training datasets. Further, the article explores the application of SVM in classifying network traffic and detecting unwanted software through geometric interpretations of classification tasks. It also emphasizes the growing trend of AI technology in modern antivirus utilities and network security analysis programs, advocating for the integration of multi-layered protective measures that leverage AI's learning capabilities. Finally, the potential of AI methodologies to unveil new pathways for research and application in network security is discussed, underscoring the need for continued exploration of these promising technologies to safeguard digital infrastructures against evolving threats.
Copyright (c) 2025 MSC. Akram Kadhim Abed https://creativecommons.org/licenses/by/4.0
Protecting Communication Situations Using IPSec and IKE Essentials and Applications
With a particular focus on IPSec (Internet Protocol Security) and IKE (Internet Key Exchange), this article offers a thorough analysis of the technologies used to protect communication channels inside contemporary networks. The ideas are explained in detail in the article. elements, modes of operation, and real-world uses of these technologies. The combination of IPSec and IKE is also covered, along with related issues and concerns and potential advancements in the sector.
Copyright (c) 2024 Arkan Mahmood Albayati , Faouzi Zarai https://creativecommons.org/licenses/by/4.0
Securing Vehicle-to-Vehicle Communications: VANet Challenges
VANET is a new technology that has both exciting potential and significant obstacles, particularly in the area of security. Three sections make up this paper, all of which are devoted to VANET security frameworks. First, there's a comprehensive review of VANET security features, problems, and prerequisites. Enabling the creation of a secure VANET infrastructure with effective communication between parties requires careful consideration of these needs. Here, you will find information about popular security standards protocols and up-to-date security architectures. In the second, we give special attention to a new way of categorizing the various VANET assaults and the answers to them. In the third, we'll look at some of these solutions side by side using standard VANET security criteria. Afterwards, we highlight some unresolved concerns and technical hurdles concerning VANET security, which can assist researchers in their pursuit of future solutions.
Copyright (c) 2023 Mahdi Salah Mahdi AL-inizi, Omar Mohammed NSAIF https://creativecommons.org/licenses/by/4.0
An Analytical Study on Improving Target Tracking Techniques in Wireless Sensor Networks Using Deep Learning and Energy Efficiency Models
The work of Wireless Sensor Network (WSN) in goal tracking presentations, particularly in military, environmental, and surveillance operations, has garnered significant attention. In order to progress the accuracy, efficiency, and energy protection of these networks, a diversity of methods have been invented to resolve challenges such as real-time monitoring, multiple-target tracking, and resource optimization. This study examines a broad spectrum of algorithms and frameworks, such as energy-efficient methods, clustering methods, augmented state-based algorithms, and deep learning-based models, to address target tracking issues in WSNs. The outcomes emphasize the progress in the field, with a particular emphasis on enhancing tracking accuracy and reducing energy consumption, ranging from adaptive clustering methods to multi-sensor deep learning outlines. Recurrent neural networks (RNN), long short-term memory (LSTM), and prognostic models are instrumental in improving target detection, localization, and tracking. The examination also delves into the placement of WSN in a diversity of environments, including battlefields, intelligent transportation systems, and quantized areas, providing valuable visions into future trends and research orientations.
Copyright (c) 2023 Sahar Hamad Ahmed https://creativecommons.org/licenses/by/4.0
Gait Recognition by Computing Fixed Body Parameters
Biometrics is a very important science, especially in recent years. This science was used to identify people based on their physical and behavioral characteristics. Gaiting is a behavioral characteristic that arises from the feeling that a person close to us can be identified even from a distance simply by recognizing how the person walks. The main advantage of gait recognition is that they are hidden and unnoticed. People can be identified from a distance. This paper implement Using computing fixed Body Parameters in the gait recognition. Computing fixed Body Parameters techniques has the advantage of being less sensitive to gait variance. The data captured manually from 50 persons, and the system can identify about (50 )% from the number of persons.
Copyright (c) 2024 Kassim Mohammed Awad , Lamia Faris Tulaib , Hadeel M Saleh https://creativecommons.org/licenses/by/4.0
Optimizing Energy Efficiency in 6G Communication Networks Based on Data Transmission Rate Allocation
To progress communication and sensing in recent years, the Sixth-Generation (6G) network was developed. To transform the technological landscape of model transmission networks we use the 6G network, and it also used to enhance the speed, decrease the latency and improve the connectivity of network. Yet, in the 6G infrastructure with these developments it has severe challenge, that is the 6G network does not negate the benefits of energy consumption associated with network. In 6G communication the sensing performance of the smart device only relay on the available power of the network, and in the 6G communication energy efficiency becomes crucial. To progress the energy efficiency of data transmission in 6G communication networks, we deployed a Data Transmission Rate Allocation (DTRA) method. Moreover, to progress the data transmission of efficient nodes we deploy a Residual Energy Cluster Head (RECH) method. In addition, to improve the reliability and speed in the 6G network we use Dynamic Multipath Routing Protocol (DMRP) method, and it also mitigate the channel defects. Atlast, in the 6G communication networks we certify QoS through optimizing the transmission rate of the user equipment through low energy consumption, and quality of channel by the use of DTRA method. Finally, in this paper we evaluate the metrices performance of the existing methods and proposed method, for instance: efficiency, energy consumption, network lifetime, and transmission speed. After evaluate the performance metrices the DTRA method enhance network lifetime and energy efficiency of 95.3% based on 6G communication networks.
Copyright (c) 2024 R. Mageswaran , Jose P, Nithisha J, T. Rengaraj , M. Neeladri , R.Senthil Rama https://creativecommons.org/licenses/by/4.0
A Comprehensive Analysis of Keras: Enhancing Deep Learning Applications in Network Engineering
Python is currently one of the most popular programming languages that is used in the sector; it has surpassed many of its predecessors. There can be numerous reasons that make this programming language chosen by many developers – and much attention is paid to the availability of numerous libraries for various purposes. One of the major differences that Keras has when compared to other such libraries is the fact that it emphasises on usability according to the basic principles. First, Keras provides many options to choose from for deploying models in production, second, good performance is supported by multi-GPU, and third, distributed training is possible. Furthermore, it is very friendly to use and easy to comprehend as well as apply especially during model development. This makes it an excellent, highly interactive, open source program for using python in creating and analyzing deep learning models. This research aims at presenting a multiple perspective analysis of Keras. Keras, a deep Learning API in Python is built on TensorFlow. It smoothly integrates with PyTorch, TensorFlow, CODEEPNEATM, and Pygame so that the efficiency of deep learning models’ deployment across different sectors of application is enhanced. Some domains falling under this discipline include; Diagnosis of heart diseases, identification of Health problems, Training Graph Neural Networks, Identification of COVID-19, Skin Tumor Detection and Image recognition. This paper also seeks to refine the understanding of the specificity of Keras fueling reflection on its goals, challenges, milestones and lessons learned within its application.
Copyright (c) 2023 Noor Abdalkareem Lafta https://creativecommons.org/licenses/by/4.0
A Comprehensive Survey of Dynamic Hashing Techniques in Network Data Processing
Dynamic hashing techniques have become pivotal in managing and processing large-scale network data, where scalability, efficiency, and adaptability are paramount. This survey provides an extensive overview of dynamic hashing methodologies, emphasizing their applications within various network architectures and protocols. We explore the evolution of hashing algorithms, analyze their performance in real-time data processing scenarios, and examine their integration with modern network technologies such as software-defined networking (SDN) and distributed systems. Additionally, we discuss the challenges and future directions in optimizing dynamic hashing for enhanced data throughput, security, and fault tolerance in network environments. This study serves as a valuable resource for researchers and practitioners aiming to leverage dynamic hashing for efficient network data management.
Copyright (c) 2023 Noor Abdalkareem Lafta, Fadi Raafat Fadheel Al-fiskhaltom https://creativecommons.org/licenses/by/4.0
Exploring the Role of Block-chain in IoT-Driven Healthcare Solutions
Due to advances in database hacking, cloud computing, and communication network penetration, current methods are needed to combat IoT threats. This work will leverage blockchains. Blockchains improve transaction speed, accuracy, security, and transparency. The blockchain is expected to make trading stocks, processing payments, managing birth certificates and health information, processing property titles, monitoring digital supply chains, and coordinating the burgeoning IoT simpler, quicker, and cheaper. Blockchain used to protect IoT; research was conducted on blockchain and methods used in the development of data transfer and database security. This study clarifies this technique and what techniques are used with this technology and compares it to previous research that used different protection methods and different directions for investing blockchain in IoT.
Copyright (c) 2023 Abeer Mohammed Shanshool https://creativecommons.org/licenses/by/4.0