International Journal of Wireless and Microwave Technologies @ijwmt
Journal articles - International Journal of Wireless and Microwave Technologies
All articles: 648
Scientific article
Cloud computing is a rapidly growing technology in this new era. Cloud is a platform where users get charged on the basis of the services and resources they have used. It enables its users to access the cloud resources from the remote locations i.e. from anywhere at any time. It needs only a working internet connection to access the cloud services. Cloud users have always been victim to the security issues and attacks which leads to the data loss. The data is not saved on the hard disk of the computer so it is highly prone to security risks. Identifying the attacks on cloud platform is a difficult task because everything on cloud is in virtual form. EDoS (Economic Denial of Sustainability) attack is a form of DDoS attacks; carried out for a long span of time and intended to put a financial burden and cause economical loss to the users of cloud. Such attacks do not exhaust the bandwidth of the user; their main aim is to put a huge financial loss or burden on the user. A technique named as SPART (Supervised Pattern Attack Recognition Technique) implemented to mitigate the EDoS attacks in cloud computing which consumes lesser energy as compared to the existing models. The experimental results have shown the less energy consumption in proposed model.
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Scientific article
Unmanned Aerial Vehicles (UAVs) have become an effective solution for establishing emergency communication in post-disaster environments where conventional infrastructure is damaged. However, limited UAV battery capacity and unstable connectivity significantly reduce communication reliability and operational coverage. To address these challenges, this paper proposes an energy-efficient UAV-assisted communication framework based on Weighted Global Search Matrix Level (WGSML) clustering and optimal trajectory optimization for device-to-device (D2D) communication. The proposed WGSML method performs energy-aware cluster formation and cluster-head selection using residual energy, signal-to-noise ratio, and neighbourhood density. A Hidden Markov Model (HMM) is employed for routing optimization, while Q-learning-based resource allocation is utilized to determine optimal UAV trajectories and maximize residual energy utilization. Simulation results demonstrate that the proposed approach improves energy harvesting performance, reduces outage probability, minimizes computational runtime, and enhances spectral efficiency compared with existing clustering methods. The proposed framework provides reliable and sustainable communication support for post-disaster emergency response scenarios.
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Scientific article
With the extensive adoption of edge computing and IoT infrastructure, the vulnerability landscape has expanded significantly along with stringent constraints concerning computation, energy efficiency, and data privacy. Traditional centralized IDS solutions tend to be less than ideal for such conditions, as they are highly dependent on centralized data labeling, large-scale computation, and constant traffic sharing. This paper presents FedSSL-IDS, a novel privacy-preserving IDS framework leveraging Federated Learning (FL) and Self-Supervised Learning (SSL), specifically designed for the needs of edge-based network architectures. The solution applies autoencoder-based self-supervised learning to extract informative latent feature representations of unlabeled network traffic, after which federated learning is performed on the lightweight classifier with supervised learning without any raw data sharing. In order to facilitate the implementation of the system on resource-limited edge devices, the system employs advanced model optimization methods, such as magnitude-based pruning and post-training quantization. Performance evaluations of the FedSSL-IDS framework were conducted using the CICIDS2017 dataset in a simulated federated edge environment with class-imbalanced and non-IID client distributions. According to the experimental results, the full precision model reached an average detection accuracy of 96.90% across all classes, whereas the major attack classes, like DDoS and PortScan, achieved impressive class-wise accuracy rates. Moreover, the combination of pruning and FP16 quantization greatly decreases the size of the model and computational cost during inference without compromising its near-native accuracy in detecting intrusions. Nevertheless, aggressive INT8 quantization leads to a substantial reduction in the detection performance of rare classes of attacks such as SQL injection attacks, showing that a compromise must be made between efficient compression and reliable detection in edge scenarios. Even though the presented framework increases the privacy level since there is no raw traffic exchange in federated training, sophisticated privacy-preserving techniques like differential privacy and secure aggregation are not part of the current design.
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Enhanced Image Encryption Scheme Utilizing Charlier Moments and Modified Chaotic Mapping
Scientific article
The integration of chaos theory and orthogonal moments has gained significant traction in contemporary image analysis. This paper presents a novel approach to image encryption and decryption, leveraging a modified logistic chaotic map and discrete orthogonal moments. The coefficients derived from Charlier polynomials and the image function are utilized to obfuscate the plaintext image. Furthermore, to bolster security measures, the pixel values of the obfuscated image are shuffled employing a modified logistic chaotic map. The encryption key is constructed from the parameters of both the chaotic map and Charlier polynomials, enhancing the robustness of the encryption scheme. Extensive experimental validation is conducted to assess the security of the proposed image encryption algorithm. Results demonstrate a considerable deviation in pixel values following diffusion via Charlier moments’ coefficients. Statistical tests and comprehensive security analyses affirm the resilience of the proposed algorithm against data loss attacks. The experimental result with Pearson correlation coefficient is almost 0, key space is greater than 2^210, and information entropy can reach 7.8404, which establish its superior security posture relative to existing algorithms within the domain of image encryption. The findings underscore the efficacy and reliability of the proposed scheme, positioning it as a viable solution for safeguarding sensitive image data in various applications.
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Enhanced Phishing URLs Detection using Feature Selection and Machine Learning Approaches
Scientific article
Phishing threats continue to compromise online security by using deceptive URLs to lure users and extract sensitive information. This paper presents a method for detecting phishing URLs that employs optimal feature selection techniques to improve detection system accuracy and efficiency. The proposed approach aims to enhance performance by identifying the most relevant features from a comprehensive set and applying various machine learning algorithms, including Decision Trees, XGBoost, Random Forest, Extra Trees, Logistic Regression, AdaBoost, and K-Nearest Neighbors. Key features are selected from an extensive feature set using techniques such as information gain, information gain ratio, and chi-square (χ2). Evaluation results indicate promising outcomes, with the potential to surpass existing methods. The Extra Trees classifier, combined with the chi-square feature selection method, achieved an accuracy, precision, recall, and F-measure of 98.23% using a subset of 28 features out of a total of 48. Integrating optimal feature selection not only reduces computational demands but also enhances the effectiveness of phishing URL detection systems.
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Enhanced Techniques for Filtering of Wall Messages over Online Social Networks (OSN) User Profiles
Scientific article
Online Social Networks enables various users to connect and share their messages publicly and privately. On one hand it provides advantages to the users to connect and share but on the other hand it provides disadvantage of being attacks or post messages which contains negative or abuse words. Hence OSN provides various filtering rules for security against these wall messages. Although there are various filtering rules and classifiers implemented for the filtering of these users wall messages in popular OSN such as Twitter and Facebook. But in the proposed methodology not only filtering of these wall messages is done but the categorization of normal or negative messages are identified and hence on the basis users can be blacklisted. The proposed methodology is compared with FCM and SVM for clustering and classification of messages. This approach efficiently categorizes the messages but restricts for generating filtering rules and blacklist management. Thus the approach with FCM and J48 first initializes clustering using FCM followed by generation of rules using J48 based decision tree. Hence on the basis of the rules generated message are classified and message which doesn't contain attacks is then filtered on the basis of dictionary which contains a list of abuse words. The methodology is implemented by applying FCM and SVM and a comparison is done with FCM and J48 for the performance on the basis of accuracy to detect abnormal messages.
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Scientific article
Quantum cryptography is the most convenient resolution for information security systems that presents an ultimate approach for key distribution. Today, the most viable key distribution resolutions for information security systems are those based on quantum cryptography. It is based on the quantum rules of physics rather than the assumed computational complexity of mathematical problems. But, the initial BB84 quantum key distribution protocol which is the raw key exchange of S13 quantum key distribution protocol has weakness of disclosure of large portion of secrete key or eavesdropping. Also, it cannot make use of most of the generated random bit. This paper enhanced S13 quantum key distribution protocol by employing polarization, secrete key disclosure and non-repudiation. The use of biometric or MAC address ensures non-repudiation. The row key exchange part of the S13 quantum key distribution which is the same as BB84 is enhanced by employing polarization techniques to make use of most of the generated random bit. Then, the tentative final key generated at the end of error estimation phase should be divided into blocks, padding, inverting the last bit of each block and XORing the block to generate a totally different key from the tentative one. Also, the random bits will be from biometric or serve MAC address respectively. The enhanced S13 quantum key is evaluated using cryptanalysis which shows that the enhanced protocol ensures disclosures of large portion of secrete key to prevent eavesdropping, utilization of most of the chosen binary strings to generate strong key and safeguarding against impersonation attack.
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Enhancing Cloud Storage Security Using Multi-User Biometric Encryption with Threshold Access Control
Scientific article
Cloud computing has become an essential platform for business data storage and application management due to its scalability, accessibility, and cost-effectiveness. However, ensuring the security and privacy of sensitive cloud data remains a major challenge because cloud users do not have direct control over their stored information. Traditional single-biometric encryption approaches often suffer from issues such as biometric variability, spoofing risks, and single-point failure. To address these limitations, this paper proposes a multi-user multi-modal biometric encryption framework integrated with threshold-based access control for securing business data stored in cloud environments. In the proposed approach, fingerprint and audio biometric modalities are pre-processed to extract privacy-preserving feature vectors, which are fused to generate individual user-keys using a Hash-Based Key Derivation Function (HKDF). Subsequently, a master encryption key is generated through Shamir’s Secret Sharing and Lagrange interpolation mechanisms, where only a predefined threshold number of valid user-keys can reconstruct the master key for decryption. AES symmetric encryption is employed to secure the business data before cloud storage. Experimental evaluation was performed using the FVC2002 fingerprint dataset and Mozilla CV-Corpus audio dataset. The generated biometric master-key successfully passed Shannon Entropy, Chi-Square, Monte Carlo Pi, and decryption validation tests, demonstrating improved randomness, reliability, and resistance against unauthorized access. The proposed framework effectively eliminates single-point failure issues and enhances secure cloud data access through threshold-based biometric authentication.
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Enhancing Cybersecurity through Bayesian Node Profiling and Attack Classification
Scientific article
Due to the epidemic, the majority of users and businesses turned to the internet, necessitating the necessity to preserve the populace and safeguard their data. However, after being attacked, the expense of data protection runs into the millions of dollars. The phrase "Protection is better than cure" is true. The paper deals with profiling the node for safeguarding against the cyberattack. There is a lot of research on network nodes. Here, we address the requirement to profile the node before utilizing machine learning to separate the data. In order to scan the nodes for risks and save the nature of threat as a database, node profiling is being investigated. The data is then classified using a machine learning algorithm utilizing the database. This research focuses on the application of machine learning methods, specifically Gaussian Naive Bayes and Decision Trees, for the segmentation of cyberattacks in streaming data. Given the continuous nature of cyberattack data, Gaussian Naive Bayes is introduced as a suitable approach. The research methodology involves the development and comparison of these methods in classifying detected attacks. The Bayesian method is employed to classify detected attacks, emphasizing the use of Gaussian Naive Bayes due to its adaptability to streaming data. Decision Trees are also discussed and used for comparison in the results section. The research explores the theoretical foundations of these methods and their practical implementation in the context of cyberattack classification. After classification, the paper delves into the crucial task of identifying intrusions in the streaming data. The effectiveness of intrusion detection is highlighted, emphasizing the importance of minimizing false negatives and false positives in a real-world cybersecurity setting. The implementation and results section presents empirical findings based on the application of Gaussian Naive Bayes and Decision Trees to a dataset. Precision, recall, and accuracy metrics are used to evaluate the performance of these methods. The research concludes by discussing the implications of the findings and suggests that Gaussian Naive Bayes is a suitable choice for streaming data due to its adaptability and efficiency. It also emphasizes the need for continuous monitoring and detection of cyberattacks to enhance overall cybersecurity. The paper provides insights into the practical applicability of these methods and suggests future work in the field of intrusion detection.
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Scientific article
In the dynamic landscape of cybersecurity, safeguarding computer networks against persistent malicious threats is paramount. Intrusion Detection Systems are crucial in this context by monitoring network traffic for unau-thorized access. While the integration of Machine Learning and Deep Learning has significantly advanced intrusion detection, the persistent challenge lies in effectively detecting minority attack classes. This study introduces an innovative approach that combines SHapley Additive exPlanations(SHAP) for feature selection and Deep Neural Networks(DNN) to enhance the performance of intrusion detection systems, particularly focusing on minority attack classes in the NSL-KDD dataset. Applied to a Random Forest classifier using a balanced dataset, SHAP provides valuable insights into feature importance, refining the feature set for seamless integration into a DNN architecture. Employing the NSL-KDD dataset, the research concentrates on elevating the detection accuracy for User-to-Root attack and Root-to-Local attacks. The results showcase a notable improvement in performance along with a reduction in computational time compared to using all the available features. A key emphasis of the study is on detecting all attack types without compromising the F1-score. An in-depth analysis of the initial set of 41 features identifies 30 as crucial for effective intrusion detection. On the imbalanced dataset, SHAP-based feature reduction improved the overall F1-score in multiclass classification from 86% to 91% by reducing training time by 8.86%, confirming that SHAP can lower complexity without sacrificing accuracy. However, several minority attacks remained undetected due to their extremely low representation. Additional experiments with oversampled data confirm that SHAP continues to provide efficiency gains while enabling robust detection of rare attack classes. These findings demonstrate that SHAP-based feature selection improves efficiency in IDS and has strong potential for minority attack detection if data scarcity is addressed. This research not only contributes to the enhancement of IDS capabilities but also highlights the importance of meticulous feature selection in achieving comprehensive and efficient intrusion detection.
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Scientific article
In the realm of Wireless Sensor Networks (WSN), approaches to managing power are generally divided into two main strategies: reducing power consumption and optimizing power distribution. Power reduction strategies focus on creating a path for data packets between the sink and destination nodes that minimizes the distance and, consequently, the number of hops required. In contrast, power optimization strategies seek to enhance data transfer efficiency without splitting the network into disconnected segments. Adjusting the data path to balance power often leads to longer routes, which can shorten the network's lifespan. Conversely, opting for the shortest possible path tends to result in a densely packed network structure. The newly proposed Adaptable Power Allocation Framework (APAF) aims to improve energy-efficient routing by simultaneously addressing both power balance optimization and the management of the data packet path. Unlike conventional routing methods, which primarily focus on the shortest path, APAF designs the data pathway by taking into account both the least amount of data transmission and the equilibrium of power distribution and balancing. Through a focus on power balance optimization and intelligent data path management, it demonstrates its effectiveness in improving energy-efficient routing. This study introduces the Adaptable Power Allocation Framework (APAF), which improves energy-efficient routing in WSNs by balancing power consumption and optimizing the data path. APAF is compared with traditional methods (LEACH, Swarm Optimization), showing a 20-30% improvement in data loss reduction and extending network lifespan.
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Enhancing the Cloud Security through RC6 and 3DES Algorithms while Achieving Low-Cost Encryption
Scientific article
Cloud computing is a cutting-edge system that's widely considered the future of data processing, making cloud computing one of the widely used platforms worldwide. Cloud computing raises problems around privacy, security, anonymity, and availability. Because of this, it is crucial that all data transfers be encrypted. The overwhelming majority of files stored on the cloud are of little to no significance while the data of certain users may be crucial. To solve the problems around security, privacy, anonymity, and availability, so we propose a novel method for protecting the confidentiality and security of data while it is being processed by a cloud platform. The primary objective of this study is to enhance the cloud security with RC6 and 3DES algorithms while attained low cost encryption, and explore variety of information safety strategies. Inside the proposed system, RC6 and 3DES algorithms have been used to enhance data security and privacy. The 3DES has been used to data with a high level of sensitivity to encrypt the key of RC6 and this method is significant improve over the status quo since it increases data security while reduce the amount of time needed for sending and receiving data. Consequently, several metrics, such as encryption time, false positive rate, and P-value, have been determined by analyzing the data. According to the findings, the suggested system attained less encryption time in different file size by securely encrypting data in a short amount of time and it gives outperformance as compared to other methods.
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Enlightenment on Computer Network Reliability From Transportation Network Reliability
Scientific article
Referring to transportation network reliability problem, five new computer network reliability definitions are proposed and discussed. They are computer network connectivity reliability, computer network time reliability, computer network capacity reliability, computer network behavior reliability and computer network potential reliability. Finally strategies are suggested to enhance network reliability.
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Ensemble Learning-Based Intrusion Detection System for Modbus-Enabled Industrial Networks
Scientific article
Industrial Control Systems (ICS) and Modbus-enabled networks are facing escalating threats from sophisticated cyber-attacks, while current Intrusion Detection Systems (IDS) struggle to identify intricate and adaptive attacks. This study envisions an ensemble learning-based IDS for Modbus-enabled industrial networks using a real-like Modbus 2023 dataset for industrial networks. The proposed IDS combines four base classifiers, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Adaptive Boosting (AdaBoost), using the stack ensemble framework, where Logistic Regression acts as the meta-classifier. Preprocessing involved PCAP capture and attack log synchronization, feature normalization, and one-hot encoding for balanced and accurate model training. Experimental evaluation demonstrated that the ensemble model has a 99.78% detection accuracy while outperforming the base individual models in terms of precision, recall, and F1-score. The results indicate the efficiency of ensemble learning for enhanced accuracy detection and false-positive reduction for Modbus networks. Future research will consider real-time testing, feature elimination, and explainable AI for higher operational deployment and scalability.
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Enterprise Private Cloud Platforms: A Systematic Review of Key Vendors
Scientific article
Cloud computing has revolutionized the way organizations manage and deploy their Information Technology infrastructure. With an increasing emphasis on data security and residency, regulatory compliance, and the need for customization, private cloud platforms have emerged as a pivotal solution in the enterprise Information Technology landscape. This paper presents a comprehensive review of private cloud technologies, delineating their key features, advantages, and capabilities. Through an exhaustive research methodology, we explore various private cloud solutions, ranging from open-source offerings to proprietary systems. A reference architecture is formulated to provide a holistic understanding of the essential components and interactions inherent to a private cloud platform. Furthermore, 18 categories and 43 subcategories of features and capabilities for the 13 most popular private cloud solutions are identified to assist organizations in evaluating and selecting the most suitable platform based on their specific requirements. This study aims to offer valuable insights to enterprises navigating their cloud adoption journey, emphasizing the significance of making informed decisions in the rapidly evolving cloud computing domain.
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Scientific article
Wireless Sensor Network (WSN) is a group of sensors connected within a geographical area to communicate with each other through wireless media. Although WSN is very important in data collection in the world today, error may occur at any stage of data processing and transmission within WSNs due to its architecture. This study presents error detection and correction in WSNs using a proposed ‘pair wise’ Residue Number System (RNS) reverse converter in a health care delivery system. The proposed RNS reverse converter required (10n+3)_FAbit hardware resources for its implementation making it suitable for sensors. The proposed scheme outperformed Weighted Function and Base Extension algorithms and Field Programmable Analog Arrays using Kalman-filter algorithm schemes in terms of its hardware requirements.
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Estimate BER Distributions of Turbo Codes
Scientific article
Based on the union bound, formulas to estimate the BER distribution of channel codes are derived. By using these formulas, the BER for every position in the information sequence can be estimated. Appling the formulas to Turbo codes, several examples were given, and the results are also compared with simulation results. The results show that the derived formulas can give out good estimations of the BER distributions for Turbo codes. Therefore this would be helpful for the BER analysis, especially the unequal error protection analysis of Turbo codes.
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Scientific article
Dimensionality reduction is an essential ingredient of machine learning modelling that seeks to improve the performance of such models by extracting better quality features from data while removing irrelevant and redundant ones. The technique aids reduce computational load, avoiding data over-fitting, and increasing model interpretability. Recent studies have revealed that dimensionality reduction can benefit from labeled information, through joint approximation of predictors and target variables from a low-rank representation. A multiplicity of linear and non-linear dimensionality reduction techniques are proposed in the literature contingent on the nature of the domain of interest. This paper presents an evaluation of the performance of a hybrid deep learning model using feature extraction techniques while being applied to a benchmark network intrusion detection dataset. We compare the performance of linear and non-linear feature extraction methods namely, the Principal Component Analysis and Isometric Feature Mapping respectively. The Principal Component Analysis is a non-parametric classical method normally used to extract a smaller representative dataset from high-dimensional data and classifies data that is linear in nature while preserving spatial characteristics. In contrast, Isometric Feature Mapping is a representative method in manifold learning that maps high-dimensional information into a lower feature space while endeavouring to maintain the neighborhood for each data point as well as the geodesic distances present among all pairs of data points. These two approaches were applied to the CICIDS 2017 network intrusion detection benchmark dataset to extract features. The extracted features were then utilized in the training of a hybrid deep learning-based intrusion detection model based on convolutional and a bi-direction long short term memory architecture and the model performance results were compared. The empirical results demonstrated the dominance of the Principal Component Analysis as compared to Isometric Feature Mapping in improving the performance of the hybrid deep learning model in classifying network intrusions. The suggested model attained 96.97% and 96.81% in overall accuracy and F1-score, respectively, when the PCA method was used for dimensionality reduction. The hybrid model further achieved a detection rate of 97.91% whereas the false alarm rate was reduced to 0.012 with the discriminative features reduced to 48. Thus the model based on the principal component analysis extracted salient features that improved detection rate and reduced the false alarm rate.
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Evaluating Machine Learning Efficacy for DoS Intrusion Detection in Wireless Sensor Networks
Scientific article
Wireless Sensor Networks (WSNs) are integral to mission-critical applications, including environmental monitoring, smart infrastructure, and healthcare. However, they are particularly vulnerable to denial-of-service (DoS) attacks, which can deplete the node's energy and disrupt communication. This study examines the effectiveness of various machine learning algorithms in enhancing intrusion detection within WSNs, focusing on balancing detection accuracy and computational efficiency. Utilising the Network Simulator-2 (NS-2) generated WSN-DS dataset, seven algorithms—K-Nearest Neighbours (KNN), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Stacking Classifier, AdaBoost, and Artificial Neural Network (ANN)—were implemented and evaluated. The experimental results indicate that AdaBoost achieved the highest overall performance, with an accuracy of 99.7%, ROC-AUC of 0.996, and detection speed of 1.6 min, underscoring its suitability for real-time intrusion detection. Stacking and Random Forest also demonstrated high accuracy (99.7% and 99.6%, respectively) but required slightly longer detection times of 7.07 and 7.33 min, respectively. In contrast, KNN exhibited the longest detection time (86.2 min) due to its high computational overhead, whereas Naïve Bayes was the fastest (0.02 min) but had lower precision (0.757) and F1-score (0.771). AdaBoost demonstrated superior detection accuracy, efficiency, and adaptability under constrained WSN conditions, outperforming all other algorithms across multiple performance metrics. These findings offer a practical benchmark for developing lightweight, high-performance intrusion detection systems in resource-limited wireless sensor environments, thereby enhancing the resilience and reliability of next-generation WSN infrastructures.
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Evaluating the Capacities and Limitations of 5G and 4G Networks: An Analysis Approach
Scientific article
The utilization of millimeter waves in 5G technology has led to key differences in the capacities and performance of radio communications. Examining the advantages and challenges of this technology and comparing it with an established technology like 4G can provide a deeper understanding of these changes. Overall, this study conducts examinations to provide the characteristics of 5G and 4G technologies. In this study, the performance of 5G was evaluated and compared to 4G, under fair conditions, by analyzing the effect of increasing the distance of antennas, the number of users, and bandwidth on signal power, delay, throughput, channel quality, and modulation metrics. The analysis demonstrates the superiority of 5G in terms of speed and its ability to support more users compared to 4G. The higher data rates and enhanced capacity of 5G are evident in the results. However, it's worth noting that 4G offers a wider coverage area compared to 5G, making it more suitable for certain scenarios where extended coverage is essential. Additionally, it was observed that 5G signals are more susceptible to noise and obstacles compared to 4G, which can impact signal quality and coverage in certain environments. The presented results suggest that using 5G antennas in geographically limited and densely populated areas, such as rural regions, would be more cost-effective compared to using 4G antennas. This is because fewer antennas are required to serve more users without the need for extensive coverage. Additionally, numerous obstacles in urban areas pose challenges to 5G technology, thus requiring a greater number of antennas to achieve satisfactory accessibility.
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