International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Recognition of the Integrity of Chic China Aesthetic Elements Based on Computer Vision Technology
Get PDFRecent trends in the fashion industry indicate that China Chic (CC) has emerged as an integral part of the Chinese lifestyle. The fashion industry in recent years was dominated by foreign brands or luxury brands because of the economic conditions and the failure to produce iconic designs by the domestic fashion industry. Recent years have witnessed the genesis of China-chic brands that began to produce original designs with a traditional touch to modern outfits. As a result of which, many international fashion elements were integrated into their clothes and accessories. However, many types of security attacks are pronounced on these elements, which will degrade their market value. This work proposes a computer vision-based defrauding model that relies on a Siamese-based Convolutional Neural Network (S-CNN) to detect counterfeited and fake products. This is done by injecting adversarial attacks on Simplified Graph Convolutional Networks (SGCN) that effectively misclassify the Adversarial Images (AdI), which are created by the Improved Fast Gradient Sign Method (I-FGSM). The training phase of the proposed model is performed using the ImageNet dataset augmented with AdI. The testing is done using the custom dataset of the CC elements, which showed a 6% improvement over the S- CNN, which is a breakthrough in preserving the integrity of the CC elements
HaLow Wi-Fi performance in multiusers and channels environment with MATLAB Simulink
Get PDFHaLow Wi-Fi (IEEE 802.11ah) wireless networking standard. As opposed to 2.4 GHz and 5 GHz-based conventional Wi-Fi networks, it leverages 900 MHz frequencies license-exempt for enabling networks Wi-Fi with a longer range. Lower energy usage makes it possible to build extensive networks of sensors or stations that work together to communicate signals, which is another advantage. In this paper IEEE 802.11ah Wi-Fi system design and implemented using MATLAB Simulink and tested under multiusers and channels environment in terms of Spectrum analyzer and constellation Diagram where 4 users, 2 MHz and 4 MHz channels bandwidth used to perfume the test also power of coarse synchronization, fine synchronization and initial channel estimation, to make Wi-Fi networks with a greater range possible were illustrated in space time stream
The role of Information System to measure the Cost and Performance Aware Scheduling Technique for Cloud Computing Environment
No full textThe lot of interest has been put forth by researchers to improve workload scheduling in the cloud platform. However, the execution of scientific workflow on a cloud platform is time-consuming and expensive. Much research has been emphasised, as users are charged based on the usage hour, minimising processing time to reduce cost. However, the processing cost can be reduced by minimising energy consumption, especially when resources are heterogeneous; Minimal work has been done considering optimising cost with energy and processing time parameters to meet task Quality of Service (QoS) requirements. This paper presents cost and performance-aware workload scheduling (CPA-WS) techniques under a heterogeneous cloud platform. This paper presents a cost optimisation model through the minimisation of processing time and energy dissipation for the execution of the task. Experiments are conducted using two widely used workflows such as Inspiral and CyberShake. The outcome shows the CPA-WS significantly reduces energy, time, and cost compared to the standard workload scheduling model
Radar Based Activity Recognition using CNN-LSTM Network Architecture
Get PDFHuman Activity Recognition based research has got intensified based on the evolving demand of smart systems. There has been already a lot of wearables, digital smart sensors deployed to classify various activities. Radar sensor-based Activity recognition has been an active research area during recent times. In order to classify the radar micro doppler signature images we have proposed a approach using Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Convolutional Layer is used to update the filter values to learn the features of the radar images. LSTM Layer enhances the temporal information besides the features obtained through Convolutional Neural Network. We have used a dataset published by University of Glasgow that captures six activities for 56 subjects under different ages, which is a first of its kind dataset unlike the signals captured under controlled lab environment. Our Model has achieved 96.8% for the training data and 93.5% for the testing data. The proposed work has outperformed the existing traditional deep learning Architectures
Heart Rate Variability and EEG Responses to Insomnia in the Menopausal Transition
No full textThis study examines the complex interplay between hormonal fluctuations, psychological distress, and heart rate variability (HRV) in menopausal women, comparing those with insomnia to controls without insomnia. We found that women with insomnia reported significantly higher psychological and vasomotor symptoms, evidenced by elevated Greene Climacteric Scale scores. Sleep quality was markedly worse in the insomnia group, with a mean Pittsburgh Sleep Quality Index score of 8.5 compared to 3.6 in controls. Hormonal analyses revealed distinct patterns, including lower progesterone levels and altered estradiol fluctuations in the insomnia group, indicating potential hormonal dysregulation contributing to sleep disturbances. Correspondingly, HRV measures indicated increased sympathetic activity and reduced vagal tone, particularly during REM sleep, suggesting a state of chronic hyperarousal.The correlation between HRV and EEG metrics highlights the role of autonomic dysfunction in sleep regulation during menopause. These findings highlight the multifaceted nature of insomnia during the menopausal transition, emphasizing the critical need to address psychological health, hormonal balance, and autonomic function. Interventions focusing on enhancing psychological well-being and managing menopausal symptoms may improve sleep quality and overall health outcomes. This study underscores the necessity for further research into targeted treatment strategies for insomnia in menopausal women, facilitating better health outcomes in this vulnerable population
Advancements and Challenges in Image Steganographer: A Comprehensive Review
No full textImage steganography, a combination of computer vision and encryption, is a classic challenge for hiding information in cover images for covert communication. This review paper examines conventional and modern image steganography approaches, including key issues and advancements. Explore the classical tension between concealing maximum information and avoiding discovery, stressing payload capacity in steganographic algorithms. Dissecting traditional methods like embedding RAR archives in JPEG files reveals weaknesses to third-party changes that risk hidden data. Image domain, transform domain, and file-format-based steganography approaches are described, along with their pros and cons. Image domain methods, such as the Least Significant Bit (LSB) method, are widely used for covert information transfer via pixel-level statistical changes. Modern advances include deep learning in image steganography. End-to-end auto encoder-based models show promising embedding capacity and durability against passive attacks. The study emphasizes the complex relationship between deep steganography and security issues by highlighting adversarial situations and their possible susceptibility to assaults. A visual representation of a common encoder-decoder network for deep steganography models shows attack channels for deleting or changing secret images and the usual path for correct image recovery. The article indicates that steganography algorithms must balance payload capacity, detection robustness, and adaptability to cover image patterns. This paper covers the progression from classical to deep learning-based image steganography and the associated issues that pave the way for future research.
IoT Protection Against Cyber Threats Based on Blockchain and Access Control: A Comprehensive Review
Get PDFThe Internet of Things (IoT) has undeniably transformed the way we interact with the world around us. As a revolutionary technology, it seamlessly integrates into our daily routines, offering unparalleled convenience and efficiency. By embedding connectivity into everyday objects, IoT has made it possible for devices to communicate, making our lives significantly easier. This constant communication and data exchange occur everywhere, from our homes to workplaces, and even in public spaces. Unfortunately, whenever connections increase, the threat of attacks increases too. Therefore, there is a critical need for systems that provide robustness at the service level. In this paper, a basic interface to IoT devices’ security architecture along with blockchain is introduced to provide scalability and authentication. This survey differs from the majority of existing reviews in that it presents a more comprehensive review of emerging research to help researchers and readers understand the state-of-the-art IoT protection against cyber threats. Additionally, different types of IoT protection against cyber threats based on blockchain and access control techniques are described in this paper. The findings demonstrate that blockchain technology offers IoT devices security along with scalability
Assessing the Feasibility of RF Fingerprinting for Security in Unmanned Aerial Vehicles
Get PDFThe wireless network of consumer drones is particularly vulnerable to remote attacks due to the weak encryption scheme involving the exchange of a Global Unique Identifier (GUID) between transceiver pairs using the binding process, thus exposing the technology to a host of attack vectors such as data spoofing and malicious authentication, among others, leading to security breaches that threaten the prospects of the consumer drone. This study assesses the feasibility of RF fingerprinting as a complementary layer of security devoid of cryptography in the wireless network of unmanned aerial vehicles for enhanced resilience. We evaluate the feature performance of the toy-grade and the universal-grade drone RC transmitters to discern the prospects for device identification in inexpensive, low-end device and the high-end device. Instantaneous amplitude and phase features extracted from the transient phase of time-domain signals acquired off-the-air in the near-field show a high recognition rate in a support vector machine and k-Nearest Neighbour, suggestive of device classification in unmanned aerial vehicle RF hardware, irrespective of built quality
Identification of Linear / Nonlinear Systems via the Coyote Optimization Algorithm (COA)
Get PDFClassical techniques used in system identification, like the basic least mean square method (LMS) and its other forms; suffer from instability problems and convergence to a locally optimal solution instead of a global solution. These problems can be reduced by applying optimization techniques inspired by nature. This paper applies the Coyote optimization algorithm (COA) to identify linear or nonlinear systems. In the case of linear systems identification, the infinite impulse response (IIR) filter is used to constitute the plants. In this work, COA algorithm is applied to identify different plants, and its performance is investigated and compared to that based on particle swarm optimization algorithm (PSOA), which is considered as one of the simplest and most popular optimization algorithms. The performance is investigated for different cases including same order and reduced-order filter models. The acquired results illustrate the ability of the COA algorithm to obtain the lowest error between the proposed IIR filter and the actual system in most cases. Also, a statistical analysis is performed for the two algorithms. Also, the COA is used to optimize the identification process of nonlinear systems based on Hammerstein models. For this purpose, COA is used to determine the parameters of the Hammerstein models of two different examples, which were identified in the literature using other algorithms. For more investigation, the fulfillment of the COA is compared to that of some other competitive heuristic algorithms. Most of the results prove the effectiveness of COA in system identification problems
Detection of Distributed Denial of Service Attacks in Software Defined Networks by Using Machine Learning
Get PDFWithin the sphere of Software-Defined Networking (SDN) — an innovative architectural paradigm that segregates the control plane from the data plane — a paramount concern is the defense against Distributed Denial of Service (DDoS) assaults. These attacks pose a significant threat to the integrity and operational sustainability of SDN infrastructures, potentially leading to extensive system disruptions and financial losses.To address this challenge, our study introduces an innovative approach utilizing machine learning strategies to enhance the detection of DDoS threats. We employed a trio of classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), applied to a publicly available SDN dataset specific to DDoS attacks. Our methodology integrates a blend of feature selection techniques, including Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and t-Distributed Stochastic Neighbor Embedding (t-SNE), with the aim of refining the accuracy of our classifications.In a comparative analysis with existing models, our innovative application of KNN in conjunction with RFE demonstrated exceptional performance, achieving an accuracy of 99.97%, a precision of 99.98%, a recall of 99.96%, and an F1-score of 99.97%. This breakthrough indicates a significant advancement in the field of SDN security