International Journal of Informatics and Communication Technology (IJ-ICT)
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494 research outputs found
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Enhanced n-party Diffie Hellman key exchange algorithm using the divide and conquer algorithm
Get PDFCryptographic algorithms guarantee data and information security via a communication system against unauthorized users or intruders. Numerous encryption techniques have been employed to safeguard this data and information from hackers. By supplying a distinct shared secret key, the n-party Diffie Hellman key exchange approach has been used to protect data from hackers. Using a quadratic time complexity, the n-party Diffie-Hellman method is slow when multiple users use the cryptographic key interchange system. To solve this issue, the researchers created an effective shared hidden key for the n-party Diffie Hellman key exchange of a cryptographic system using the divide-and-conquer strategy. The current research recommends the use of the divide and conquer algorithm, which breaks down the main problem into smaller subproblems until it reaches the base solution, which is then merged to generate the solution of the main problem. The comparative analysis indicates that the developed system generates a shared secret key faster than the current n-party Diffie Hellman system
Categorizing hyperspectral imagery using convolutional neural networks for land cover analysis
Get PDFCategorizing hyperspectral imagery (HSI) is crucial in various remote sensing applications, including environmental monitoring, agriculture, and urban planning. Recently, numerous approaches have emerged, with convolutional neural network (CNN)-based algorithms demonstrating remarkable performance in HSI classification due to their ability to learn complex spatial-spectral features. However, these algorithms often require significant computational resources and storage capacity, which can be limiting in practical applications. In this study, we propose a novel CNN architecture tailored for HSI classification within the spectral domain, focusing on optimizing computational efficiency without compromising accuracy. The architecture leverages advanced spectral feature extraction techniques to enhance classification performance. Experimental evaluations on multiple benchmark hyperspectral datasets reveal that the proposed approach not only improves classification accuracy but also achieves a superior balance between performance and computational demand compared to traditional methods like K-nearest neighbors (KNN) and other deep learning-based techniques. Our results demonstrate the potential of the proposed CNN model in advancing the field of HSI classification, offering a viable solution for real-world applications with constrained computational resources
Shellcode classification analysis with binary classification-based machine learning
Get PDFThe internet enables people to connect through their devices. While it offers numerous benefits, it also has adverse effects. A prime example is malware, which can damage or even destroy a device or harm its users, highlighting the importance of cyber security. Various methods can be employed to prevent or detect malware, including machine learning techniques. The experiments are based on training and testing data from the UNSW_NB15 dataset. K-nearest neighbor (KNN), decision tree, and Naïve Bayes classifiers determine whether a record in the test data represents a Shellcode attack or a non-Shellcode attack. The KNN, decision tree, and Naïve Bayes classifiers reached accuracy rates of 96.26%, 97.19%, and 57.57%, respectively. This study's findings aim to offer valuable insights into the application of machine learning to detect or classify malware and other forms of cyberattacks
Quality of service optimization for 4G LTE upload and download throughput
Get PDFDemand for mobile data services and people’s dependence on 4G LTE networks continue to increase. However, the quality of service (QoS) of this network still requires improvement, especially regarding the effect of QoS on throughput at specific frequencies. The research gap lies in the lack of indepth analysis of the impact of QoS parameters on network performance at frequencies of 2,100 MHz and 2,300 MHz. This study evaluates the effect of QoS parameters, such as delay, jitter, and packet loss, on throughput in 4G LTE networks at both frequencies. The research methodology uses an experimental approach with throughput, delay, jitter, and packet loss measurements in various network conditions. The results showed that delay (17.2174 ms to 37.0322 ms), jitter, and packet loss significantly influence throughput, ranging from 624.5 Kbps to 1,322.4 Kbps. The 2,100 MHz frequency tends to show better performance than 2,300 MHz. This study concludes that optimizing QoS parameters, especially delay and jitter, can significantly improve 4G LTE network performance. These findings provide practical contributions for mobile operators in improving network quality and customer satisfaction and open opportunities for further research on other frequencies or newer network technologies
Scaling of Facebook architecture and technology stack with heavy workload: past, present and future
Get PDFLeading social media Facebook has improved its architecture to meet user needs. Facebook has improved its systems to handle millions of users with heavy workloads and large datasets using innovative architectural solutions and adaptive strategies. The study examines Facebook’s architectural and technological advances in heavy workload and big data. To understand how Facebook scaled with a growing user base and data volume, history and system architecture will be examined. It will also examine how cloud storage and high-performance computing optimize resource utilization and maintain performance during peak user activity. Facebook is managing big data and heavy workloads with new technologies like the hybrid communication model that uses PULL and PUSH strategies for real-time messaging. Facebook switched from HBase to MyRocks for message storage to improve performance as data grew. Architectural scaling and technology stack research must prioritize data storage innovations and optimized communication protocols to handle heavy workloads and big data. The messenger Sync protocol reduces network congestion and improves synchronous communication, reducing resource consumption and maintaining performance under high load. High-performance computing (HPC) and cloud storage should be studied together to support complex compute workflows. This convergence may improve large-scale application infrastructures and encourage interdisciplinary collaboration for scalable and resilient systems
The bootstrap procedure for selecting the number of principal components in PCA
Get PDFThe initial step in determining the number of principal components for both classification and regression involves evaluating how much each component contributes to the total variance in the data. Based on this analysis, a subset of components that explains the highest percentage of variance is typically selected. However, multiple valid combinations may exist, and the final choice is often made manually by the researcher. This study introduces a novel yet straightforward algorithm for the automatic selection of the number of principal components. By integrating ANOVA and bootstrapping with principal component analysis (PCA), the proposed method enables automatic component selection in classification tasks. The algorithm is evaluated using three publicly available datasets and applied with both decision tree and support vector machine (SVM) classifiers. Results indicate that this automated procedure not only eliminates researcher bias in selecting components but also improves classification accuracy. Unlike traditional methods, it selects a single optimal combination of principal components without manual intervention, offering a new and efficient approach to PCAbased model development
Enhanced pulse shaping filters for minimizing interference in GFDM signals for 5G cellular networks
Get PDFThe imminent rise of 5th generation (5G) wireless standards heralds a pivotal era in cellular communication. Among the challenges faced, selecting an optimal multiple access technique stands out as crucial for achieving the desired blend of low latency, high data rates, and throughput. Generalized frequency division multiplexing (GFDM) emerges as a promising can-didate meeting 5G requirements. This study introduces two innovative pulse shaping filters (PSFs) the better than raised cosine filter (BRCF) and modified bartlett hanning filter (MBHF) paired with various modulation schemes such as binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and quadrature amplitude modulation (QAM) to assess GFDM signal performance. Considering its spectrum efficiency, QAM modulation emerges as the preferred choice. Performance evaluation of the PSFs entails analyzing symbol error rate (SER) against signal to noise ratio (SNR) across different modulation schemes
Real time hand gesture detection by using convolutional neural network for in-vehicle infortainment systems
Get PDFNowadays, a variety of technologies on autonomous vehicles have been extensively developed, including in-vehicle infotainment (IVI). It have been noted as one of the key services in the automobile industry. In the near future, people will be able to watch some virtual reality (VR) movies through the streaming service provided in the vehicle. However, a person sometime not tend to be joy while watching espcially when the remote controller or audio sensory controller lack of battery or too far from IVI panel. Thus, the purpose of this research is to design a scheme of real time hand gesture detection for in-vehicle infotainment system, in order to create human computer experience. In this research, the image of human palm hand will be taken by using camera for recognize the hand gesture action. This proposed scheme will recognize human gesture and convert to be computer intruction, that can be understood by IVI device. As a result, it show our proposed scheme can be the most consistent in term of accuracy and loss compared to others method. Overall, this research represents a significant step toward improving better user experience. Furthermore, the proposed scheme is anticipated to contribute significantly to the IVI field, benefiting both academia and societal outcomes
Symmetrical cryptographic algorithms in the lightweight internet of things
Get PDFThe internet of things (IoT) has emerged as a prominent area of scrutiny. It is being deployed in multiple applications like smart homes, smart agriculture, intelligent surveillance systems, and even innovative industries. Security is a significant issue that needs to be addressed in these types of networks. This paper aims to describe symmetrical lightweight cryptographic algorithms (SLCAs) for lightweight IoT networks. The article focuses on discussing the principal difficulties of using cryptography in lightweight IoT devices, exploring SLCAs and their types based on structure formation throughout the literature survey, and comparing and evaluating different LCAs proposed in recent research. The main goal is to demonstrate how to solve the issues associated with conventional cryptography techniques and how lightweight cryptography algorithms aid limited IoT devices in achieving cybersecurity objectives
A survey on novel approach to semantic computing for domain specific multi-lingual man-machine interaction
Get PDFNatural language processing (NLP) helps computational linguists to understand, process, and extract information from natural languages. Linguist Panini signifies ’information coding’ in a language and explains that Karakas are semanticosyntactic relations between nouns and verbs that resemble participant roles of modern case grammar. Computational grammar maps vibhakti (inflections) of nominals and verbs to their participant roles. Karaka’s theory extracts semantic roles in the sentences which act as intermediate steps for various NLP tasks. The survey shows that NLP seeks to bridge the gap for man-machine interaction. The work presents the impact of machine learning on natural language processing with changing trends from traditional to modern scenarios with Panini’s classification scheme for semantic computing facilitating machine understanding. The study presents the significance of Karaka for semantic computing, methodologies for extracting semantic roles, and analysis of various deep learning-based language processing systems for applications like question answering. The survey covered around 50 research articles and 21 Karaka-based NLP systems performing multiple tasks like machine translation, question-answering systems, and text summaries using machine learning tools and frameworks. The work includes surveys from renowned journals, books, and relevant conferences, as well as descriptions of the latest trends and technologies in the machine learning domain