International Journal of Wireless and Microwave Technologies @ijwmt
Journal articles - International Journal of Wireless and Microwave Technologies
All articles: 648
Evaluating the Effectiveness of WPA3 Protocol against Advanced Hacking Attacks
Scientific article
Personal Mode Home Wi-Fi networks are an integral part of our daily lives, providing convenience and ease of access to the Internet. However, many people believe that modern encryption protocols such as Wi-Fi Protected Access3 (WPA3) provide sufficiently strong protection. This research aims to evaluate the effectiveness of encryption protocols used in home Wi-Fi networks, focusing on the currently most widely used Wi-Fi Protected Access2 (WPA2) protocol and the newer and more secure WPA3 protocol, and the effectiveness of the Protected Management Frames (PMF) against deauthentication attacks. A penetration test was conducted in a controlled, secure environment using a set of specialized tools such as Aircrack-ng, Fluxion, Bettercap, and Wireshark to assess the vulnerability of these networks to various attacks. The research results showed that home Wi-Fi networks using WPA2 protocol and WPA3 protocol (who support transitional mode) are vulnerable to hacking. deauthentication attacks and dictionary attacks were successful in hacking networks, especially when the passwords were weak or could be guessed. In addition, evil twin attacks using the captive portal approach have been proven effective in penetrating networks that use WPA2 and WPA3 (even when they do not support transitional mode) by exploiting weaknesses in user behavior. The results also show that deauthentication attacks are still effective before establishing a 4-way handshake. This paper proposes some countermeasures to reduce the risk of home network penetration.
Free
Evaluation of Machine Learning Algorithms for Malware Detection: A Comprehensive Review
Scientific article
Malware outperforms conventional signature-based techniques by posing a dynamic and varied threat to digital environments. In cybersecurity, machine learning has become a potent device, providing flexible and data-driven models for malware identification. The significance of choosing the optimal method for this purpose is emphasized in this review paper. Assembling various datasets comprising benign and malicious samples is the first step in the research process. Important data pretreatment procedures like feature extraction and dimensionality reduction are also included. Machine learning techniques, ranging from decision trees to deep learning models, are evaluated based on metrics like as accuracy, precision, recall, F1-score, and ROC-AUC, which determine how well they distinguish dangerous software from benign applications. A thorough examination of numerous studies shows that the Random Forest algorithm is the most effective in identifying malware. Because Random Forest can handle complex and dynamic malware so well, it performs very well in batch and real-time scenarios. It also performs exceptionally well in static and dynamic analysis circumstances. This study emphasizes how important machine learning is, and how Random Forest is the basis for creating robust malware detection. Its effectiveness, scalability, and adaptability make it a crucial tool for businesses and individuals looking to protect sensitive data and digital assets. In conclusion, by highlighting the value of machine learning and establishing Random Forest as the best-in-class method for malware detection, this review paper advances the subject of cybersecurity. Ethical and privacy concerns reinforce the necessity for responsible implementation and continuous research to tackle the changing malware landscape.
Free
Evaluation of Performance for Wireless Sensor Networks Based on Gray Theory
Scientific article
A performance evaluation method of wireless sensor networks based on gray theory is proposed. Firstly the influence factors of performance are analyzed, and the index set in evaluation of wireless sensor networks' performance is built which include index of key performance and reliable characteristics. Based on AHP and gray theory, a model of evaluation of wireless sensor networks performance is given. Finally the results of example show that the evaluation model is rationality and feasibility.
Free
Evaluation the performance of DMZ
Scientific article
Local area networks are built mainly for two essential goals, the first one is to support the framework’s business functionality such as email, file transferring, procurement systems, internet browsing, and so forth. Second, these common networks should be built using secure strategies to protect their components. Recent developments in network communication have heightened the need for both secure and high performance network. However, the performance of network sometime is effected by applying security rules. Actually, network security is an essential priority for protecting applications, data, and network resources. Applying resources isolation rules are very important to prevent any possible attack. This isolation can be achieved by applying DMZ (Demilitarized Zone) design. A DMZ extremely enhance the security of a network. A DMZ is used to add an extra layer of protection to the network. It is also used to protect a private information. A DMZ should be properly configured to increase the network’s security. This work reviewed DMZ with regard to its importance, its design, and its effect on the network performance. The main focus of this work was to explore a means of assessing DMZ effectiveness related to network performance with simulation under OpNet simulator.
Free
Scientific article
The demand for cloud computing systems has increased tremendously in the IT sector and various business applications due to their high computation and cost-effective solutions to various computing problems. This increased demand has raised several challenges such as load balancing and security in cloud systems. Numerous approaches have been presented for load balancing but providing security and maintaining integrity and privacy remains a less explored research area. Intrusion detection systems have emerged as a promising solution to predict attacks. In this work, we develop a deep learning-based scheme that contains data pre-processing, convolution operations, BiLSTM model, attention layer, and CRF modeling. The current study employs a machine learning-based approach to detect intrusions based on the attackers' historical behavior. Deep learning algorithms were used to extract features from the image and determine the significance of dense packets to generate the salient fine-grained feature that can be used to detect malicious traffic and presents the final classification using fused features.
Free
Scientific article
Data aggregation is one of the core processing in wireless sensor network which ensures that environmental data being captured reaches the user via base station. In order to ensure proper data aggregation, there are many underlying principles that need more attention as compared to more frequently visited routing and energy problems. We reviewed existing data aggregation schemes with special focus on data correlation scheme and found that there is still a large scope of investigation in this area. We find that there are only less number of research publications towards existing techniques of data aggregation using correlational-based approach. It was also explored that such techniques still does not focus much on data quality, computational complexity, inappropriate benchmarking, etc. This paper elaborates about all the unsolved issues which require dedicate focus of investigation towards enhancing the data reliability and data quality in aggregation process in wireless sensor network.
Free
Extension of refinement algorithm for manually built Bayesian networks created by domain experts
Scientific article
Generally, Bayesian networks are constructed either from the available information or starting from a naïve Bayes. In the medical domain, some systems refine Bayesian network manually created by domain experts. However, existing techniques verify the relation of a node with every other node in the network. In our previous work, we define a Refinement algorithm that verifies the relation of a node only with the set of its independent nodes using Markov Assumption. In this work, we did propose Extension of Refinement Algorithm that uses both Markov Blanket and Markov Assumption to find the list of independent nodes and adhere to the property of considering minimal updates to the original network and proves that less number of comparisons is needed to find the best network structure.
Free
FLAT: A Federated Graph Attention Network for Secure Routing and Attack Detection in MANETs
Scientific article
Abstract: Mobile Ad Hoc Networks (MANETs) are autonomous wireless networks that do not rely on fixed infrastructure for communication among the mobile nodes. The mobile nodes communicate with each other with no centralized control, and routes are not established in advance. This makes MANETs vulnerable to routing attacks such as Black Hole and Sybil attacks, which divert or drop packets and thereby degrade network performance. This paper presents a novel hybrid approach combining Federated Learning (FL) and Graph Attention Network (GAT), which is termed as FLAT. GAT dynamically assigns importance to neighbouring nodes, allowing the model to capture the mobility, link instability, and heterogeneous node behaviour inherent in MANETs. FL, in turn, enables routing and attack patterns to be learned in a decentralized manner without sharing raw data. The approach was evaluated in NS-3.36 on networks ranging from 10 to 150 nodes, under Black Hole and Sybil attacks with 20% of the nodes acting maliciously, and was compared against AODV, SAODV, AOMDV, and the optimization-based Dolphin Cat Optimizer, using PDR, PLR, throughput, and end-to-end delay as evaluation metrics. It is found that the PDR of FLAT is approximately 15.1% higher than AODV and 8.2% higher than the Dolphin Cat Optimizer, while its PLR is reduced by about 77% relative to AODV, 75% relative to SAODV, and 50% relative to the Dolphin Cat Optimizer. With respect to throughput performance, FLAT shows a performance level of about 4.4, 2.8, 2.3, and 2.1 times higher than AODV, SAODV, AOMDV, and Dolphin Cat Optimizer respectively. The above findings clearly show that FLAT outperforms current techniques with regard to all measured parameters.
Free
Fast Matching Algorithm Based on Fingerprint Classification Information
Scientific article
This paper focuses on fingerprint minutia matching algorithm. A special minutia neighbor structure is proposed during the matching process in this algorithm. It can locate fingerprints using the singular from classification information. In addition, minutia structure can be used to save the time of matching minutia in a simple but effective way. Then, the matching of minutia is based on the changeable sized boundary box. At the same time, possible reference position is computed to make sure the algorithm more robust to nonlinear deformation from fingerprint images. Experimental results on Fingerprint verification competition FVC2004 databases show that this algorithm can speed up the matching of fingerprint database with a preferable performance.
Free
Scientific article
Wireless Sensor Networks (WSNs) play a critical role in various applications, including environmental monitoring, healthcare, and industrial automation. However, these networks face significant challenges related to energy efficiency, fault tolerance, and reliable data transmission, particularly in dynamic environments. Existing clustering and routing techniques often fail to ensure seamless fault tolerance and energy optimization simultaneously. Many traditional approaches lack robust mechanisms to handle Cluster Head (CH) failures, resulting in reduced network stability and shorter operational lifetimes. To address these limitations, this study proposes a Fault-Tolerant Backup Cluster Head with Ant Colony Optimization (FT-BKCH-ACO) approach that enhances energy efficiency and network resilience. The methodology involves optimized CH and Backup CH (BKCH) selection, considering parameters such as residual energy, distance to the base station, and network density. Additionally, Ant Colony Optimization (ACO) is employed to dynamically adjust pheromone levels for energy-efficient routing, ensuring reliable intra-cluster and inter-cluster communication. Simulation results demonstrate that the FT-BKCH-ACO approach significantly improves energy consumption by 23.2%, packet delivery ratio by 10.5% and end-to-end delay by 17.8% compared to existing models. The inclusion of backup CHs ensures seamless communication even in the event of node failures, making this method highly suitable for IoT-enabled WSN applications. The proposed approach bridges the gap between fault-tolerant clustering and adaptive routing, offering a scalable and energy-efficient solution for large-scale sensor networks.
Free
Feature Dimension Reduction Algorithm Based Prediction Method for Protein Quaternary Structure
Scientific article
Knowing the quaternary structure of an uncharacterized protein often provides useful clues for finding its biological function and interaction process with other molecules in a biological system. Here, dimensionality reduction algorithm is introduced to predict the quaternary structure of proteins. Our jackknife test results indicate that it is very promising to use the dimensionality reduction approaches to cope with complicated problems in biological systems, such as predicting the quaternary structure of proteins.
Free
Feature Engineering for Cyber-attack detection in Internet of Things
Scientific article
Internet of Things (IoT) consists of group of devices which communicates information over private networks. One of the key challenges faced by IoT networks is the security breaches. With the objective of automating the detection of possible security breaches in five categories, IoT traffic created with Message Queue Telemetry Transport (MQTT) protocol is analyzed. The five categories of cyber-attacks considered are brute force, denial of service (DoS), flooding, malformed data, and SlowITe attacks along with legitimate traffic. The popular five machine learning (ML) models, LightGBM, Random Forest, MLP, AdaBoost, and Decision Tree Classifiers are trained to predict cyber-attacks. In traditional traffic analysis all the available features of MQTT traffic were utilized for the ML modeling and in this work, we challenge the practice by showing that automated feature selection improves the performance of the overall ML models. The average accuracy, precision, recall and the F1 score are used as performance evaluation metrics. It is observed that all models in average are able to achieve 90% of accuracy in classification, while MLP model is trained 10 times faster than the other models. Further the optimal number of features for correct classification is identified as 10 features through Monte Carlo analysis. With the reduced features, it is possible to detect DoS, flooding, and SlowITe attacks with more than 90% accuracy and precision. Yet, it is difficult to tell apart brute force and malformed data attacks.
Free
Federated Graph Attention Network for IoT Edge Anomaly Detection
Scientific article
The rapid evolution of Internet of Things (IoT) networks has presented serious security threats because of the enormous volume of distributed data produced by connected devices. Traditional IDSs (IDS) usually follow centralized data collection, resulting in communication overhead, scalability issues, and privacy problems. Although federated learning (FL) offers a way to train distributed models while preserving privacy, many current FL-based AD techniques cannot be adapted to account for the interaction relationships between IoT devices. To address these challenges, this study introduces the federated graph attention network (FL-GAT) for anomaly detection in IoT-edge environments. The proposed framework treats IoT devices as graph nodes and introduces a multi-head graph attention mechanism to capture the spatial interaction among devices while guaranteeing data privacy by adopting federated learning. Local models are trained in a distributed manner on edge devices without sharing raw data. Distributed IoT attack scenarios were used to evaluate the proposed framework using the TON_IoT and Bot-IoT benchmark datasets. Experimental results show that FL-GAT achieved 95.2 % accuracy and 94.5 % F1 score on TON_IoT and 94.8 % accuracy and 94.1 % F1 score on Bot-IoT, with better results than centralized deep learning and federated deep learning baseline models, and graph-based baseline models. Furthermore, the attention mechanism enhances the interpretability of the model by identifying the key interactions between devices that lead to unusual activities. Although the proposed framework shows encouraging performance and scalability, the evaluation was conducted using benchmark IoT datasets under a simulated experimental setting. Future work will focus on real-world deployment scenarios, dynamic network conditions, and lightweight edge optimization for resource-constrained IoT devices.
Free
Scientific article
Dynamic spectrum access (DSA) in 5G IoT setups with cognitive radio is characterized by rapid and decentralized decision-making processes in highly non-stationary wireless environments, limited communication needs, and restrictive bounds. In this work, we present F-DMRL, a federated, communication-efficient decentralized meta-reinforcement learning framework for allowing a massive number of IoT devices to meta-learn collectively about spectrum-access strategies in a decentralized way without centralized control and without an extensive amount of inter-agent communication. Our method incorporates lightweight federated meta-parameter aggregation with gradient sparsification and periodic communication, allowing devices to only compress the meta-updates during this process and then adapt locally for task-specificity. We have presented analytical speedup guarantees and upper bounds on communication cost under bounded environmental drift and shown that using the approach proposed here, F-DMRL preserves convergence properties while posing a large reduction in coordination overhead at the same time. Simulations across various 5G IoT spectrum environments showed that F-DMRL performed faster adaptation (up to 45% fewer episodes), higher spectral efficiency, and lower interference probability compared to centralized meta-RL, federated DRL, and traditional decentralized RL baselines. Simulation results averaged across 10 independent runs demonstrate improvements of 45% faster adaptation and 60–80% lower communication overhead relative to baseline methods, while maintaining stable convergence.
Free
Fixed Cluster Formations with Nearest Cluster Heads in Wsns
Scientific article
The limited battery usage of a sensor node is one of the significant issues in WSNs. Therefore, extending the lifetime of WSNs through energy efficient mechanisms has become a challenging research area. Previous studies have shown that clustering can decrease the transmission distance of the sensor nodes thus, prolongs the lifetime of the network. In literature, most of the LEACH variants aim to set-up clusters in each round by changing CHs randomly. These formations cause to spend high amount of energy and induce additional network costs. In this paper, an energy-efficient nearest constant clustering approach is proposed to solve the problems of LEACH based protocols. The proposed approach uses constant clusters which are formed only once when algorithm starts. The cluster formation remains fixed until the energies of the all sensors are finished. Proposed approach aims to select nearest CHs in each cluster randomly without changing the cluster formations.
Free
Format-Compliant Encryption of JPEG2000 Codestreams
Scientific article
In this paper, we propose two format-compliant encryption schemes for JPEG2000, which preserve the syntax of the original codestream and do not introduce superfluous markers into the encrypted bitstream. The proposed efficient scheme randomly encrypts either low or upper half bytes of those randomly selected bytes in Codeblock Contribution to Packets (CCPs). The secure scheme encrypts both low and upper half bytes and can protect the nearly whole codestream except for the header information. The proposed schemes can provide efficient, secure, scalable and completely format-compliant protection of JPEG2000, which is proved by lots of experiments.
Free
Fractal Peano Antenna Covered by Two Layers of Modified Ring Resonator
Scientific article
Negative index of refraction has attracted a great attention in literatures. These materials are artificial structures named metamaterials has characteristics not found in nature. Microstrip antennas covered by metamaterial are very interesting areas of study. In this paper fractal Peano shape antenna is proposed and covered by two layers of modified ring resonator. The results show an enhancement in Reflection Coefficients, gain, and directivity.
Free
Fractal multiband microstrip patch antenna for wireless applications
Scientific article
This paper proposes a design of fractal multi-band microstrip patch antenna dedicated for wireless applications. The proposed antenna uses a FR-4 epoxy substrate having permittivity 4.4 and tangent loss to be 0.02.The microstrip antenna proposed is designed by incorporating double I-shape slot on the surface of rectangular patch and simulated by using HFSS software. Further the antenna is modified by inserting fractal shape and obtained the three frequency bands located at 2.4Ghz , 3.5Ghz and 5.50Ghz respectively and antenna parameter like VSWR, return loss, antenna gain, directivity of an antenna and bandwidth has been analyzed. The designing of proposed antenna is to be followed by fabrication by using A FR-4 substrate of thickness 1.60mm. Further, this paper discusses, the simulated and measured results comparison and analysis of the same. For designing and simulating of antennae simulation software HFSS has been used. The simulated and measured results represented are in permissible range.
Free
Frameproof Codes Based on The Generalized Difference Function Families
Scientific article
The frameproof codes are used in copyright protecting. Motivated by the method of constructing frameproof codes coined by D.Tonien etal, in this paper, we introduced a new combinatorial designs which in fact generalized the difference function family introduced by D.Tonien. The new designs can be constructed from difference matrix efficiently. By using the new designs we construct more larger number of frameproof codes .
Free
Scientific article
We are running in the communication era demanding a very large bandwidth for high speed applications. Among the available bandwidth technologies, the optical fiber seems the most appropriate and best fit. The optical fiber technology laid down at the backbone networks has almost replaced the preexisting coax cables. To extend fiber connectivity to the end user, especially in congested and remote areas is quite a difficult task in the context to cost and installation time. Therefore, the first and last mile connectivity (FLMC) is still a bottleneck in extending the beauty of optical fiber to the edge of the network. In most of the applications, the connectivity from backbone to the end user is carried out with the radio or copper links having a much lesser capacity than optical fiber. Keeping in view the nature and size of emerging applications, the FLMC needs to be addressed with the appropriate technology. To address this issue, the emerging solution is optical wireless communication like free space optics (FSO). The FSO is coming as a more promising alternative due to its properties like huge bandwidth and less cost. In this paper, we discuss the possible solution to first and last mile connectivity by FSO links and hence the gap between the optical core and the edge of network can be bridged by FSO communication in reliable and cost effective manner. The significance of the proposed work gives an everlasting impression of using FSO communication in FLMC over the existing ones. The ever increasing demand of high bandwidth can be meticulously met with the FSO communication. The simulation results show that the desirable performance is achieved and is analyzed with performance indicators like Q-factor and BER.
Free
