International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Comparative Analysis of Traditional and Modern Proxy Solutions in Cyber Security
No full textThis paper presents a broad comparative analysis of traditional and modern proxy solutions in cyber security landscape. This paper is highlighting the effectiveness, applications, and impact of these proxies in different cyber security environments. Through an extensive literature review, this paper explores the evolution of proxy technologies. A thorough evolution is presented here from basic content filtering and caching tools to sophisticated security techniques. And how these security solutions are integrated with advanced features. These features are deep packet inspection (DPI), real-time threat intelligence, and encryption management of traffic. The quantitative and qualitative data from the University of Maryland CISSM Cyber Attacks Database is used to analyze trends, frequency, and financial impacts for different organizations. This analysis is identifying a major increase in proxy-related attacks and highlighting importance of strong proxy solutions required to handle these growing sophisticated attacks. We investigate different case studies, like high profile breaches at OKX, i2VPN, and the 911[.]re. This investigation is showing real-world applications and consequences of proxy attacks. These case studies provide a qualitative input to our understanding. Also, these case studies are highlighting specific methodologies and mitigation strategies being used by the hackers to breach cyber security solutions. Our findings are showing that modern proxy solutions are considerably outperforming the traditional proxies in different parameters of cyber security. Like security effectiveness, scalability, and adaptability are the main parameters where modern proxy solutions are more proactive and more secure as compare to the tradition proxy solutions. This paper determines different lessons learned and recommendations for enhancing security of organizations. This paper concludes by emphasizing the need for continuous monitoring, comprehensive incident response plans, and employee training
Improving IoT Security with Balanced Resampling and Deep Learning
No full textThe rapid growth of the Internet of Things (IoT) has created an urgent need for robust security measures against advanced cyber threats. This research introduces a novel approach to detecting attacks on IoT devices by combining balanced resampling with deep learning models like CNN, GRU, DNN, and CNN-LSTM. Unlike traditional methods, our strategy dynamically adjusts class distribution to ensure balanced representation of both majority and minority classes, thus improving detection accuracy. Using the N-BaIoT dataset, which includes data from various IoT devices under Mirai and BASHLITE botnet attacks, we rigorously evaluate our method on seven different devices. We show herein that our method, even when used with limited data, provides major improvements in precision, recall, F1score and loss metrics compared to other existing methods. Therefore, our study aims at providing an enhanced security model for IoT, which provides a better detection accuracy and reliability by effectively integrating deep learning models with data preprocessing
A System for Crowd Estimation and Violence Detection
No full textCrowd density estimation is a challenging research problem in computer vision and has many applications in commercial and defense sectors. Various crowd density estimation methods have been proposed by researchers in the past, but there is an utmost need for accurate, robust and efficient crowd density estimation techniques for its practical implementation. In this paper, we propose an method to estimate the crowd and detect the anomaly. Here we used CSRNet and VGG16 methods. We evaluate proposed method on ShanghaiTech dataset for crowd estimation and for violence detection we have used movie and hockey dataset. The results demonstrate that proposed method outperforms the state-of-the-art crowd counting methods in estimating the crowd counts and detection of violence
Strengthening IOT Security by Using Ensemble Learning and Feature Selection for Intelligent Intrusion Detection Based on Complete UNSW-NB15 And Iotid20 Datasets
No full textInternet of Things (IoT) refers to a collection of devices with sensors or actuators, capable of sending and receiving data that are linked together over wireless networks. As the number of internet-connected gadgets is growing at a brisk pace, a huge volume of data is being passed through these devices. This calls for the development and optimization of algorithms related to network security such as Intrusion Detection Systems (IDSs) to keep data secure during transmission. As a result, IDSs are frequently used along with additional safety solutions like firewalls and access control for data security. Various Machine Learning (ML) based strategies have been used for customizing IDSs to meet the ever-increasing demands of secured networks based on a subset of IoT Intrusion detection dataset. In the present study, we implemented an ensemble ML technique applied to the full version of IoT Intrusion Dataset 2020 (IoTID20) as well as UNSW-NB15 datasets to carry out multiple experiments. ML methods including, Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) have been used to build the primary classifier and the meta-classifier respectively in the proposed model. ML-based IDSs often encounter challenges such as a rise in the false positive rate, reduced detection accuracy, and prolonged model-building time when training is conducted on several imbalanced datasets. We implemented a random forest classifier's feature importance score to evaluate all of the pre-processed data and generate a shortened feature set, which simplifies the process of creating models and increases detection accuracy. The evaluation assessment shows that the recommended approach exhibits superior performance when compared to the models published in the literature, achieving 99.14% detection accuracy on UNSW-NB15 datasets with only 12 out of 47 network features and 99.94% detection accuracy on IoTID20 datasets with only 16 out of 83 network features
A Deep Learning Multifractal Texture Analysis Using Principal Line Extraction Approach for Palmprint Recognition System
No full textTo make the methodical Palmprint Recognition System (PRS), this research proposed an unique Deep Learning classifier using the palm hand’s principal lines extraction approach and multifractal texture analysis approach. To reveal the efficient biometric security, a Deep Learning Multifractal Texture Analysis for Palmprint Recognition System (DLMTA-PRS) has been suggested. In DLMTA-PRS system, exact principal lines of Two–Dimensional Palmprint Region of Interest (2DPROI) image are extracted using morphological operations and an edge detection algorithm in a peculiar manner. Then, Feature values of 2DPROI image are fetched using multifractal texture analysis approach. To perform this approach, Box-counting and Gliding-Box algorithms are performed and the feature vector is created. The feature vector is classified using the proposed Convolution Neural Network (CNNNet) classifier approach to get the higher authentication security. The multi-spectral 2DPROI image database has been utilized for this research which is acquired from the PolyU, the Hong Kong Polytechnic University in Hong Kong. The suggested DLMTA-PRS system underwent scrutiny as well as proved the best of its by evaluating the DLMTA-PRS system using numerous criterias with the achievement of getting 99.25% accurate identification rate
Comparative Sensory Data Monitoring Model Based On Multiple Algorithms between Server and Client PI within A Smart Manufacturing Setup
No full textThe optimal use of data in decision-making for instituting effective and efficient processes within the manufacturing sector is increasing rapidly. As a result, this digital transition poses high risk of cyber-attacks for various reasons such as financial gain etc. This research paper therefore aims at investigation the feasibility of a modelled system with the ability to correlate data between simulation model and physical model and the ability of such a model to cipher and decipher data without any losses. The presentation of such a model seeks to answer the research question looking at the impact of the encryption speed and its contributing to the data security quality and its influence in the implementation of security measures within a Smart Manufacturing Plant. The model setup was developed by creating two identical models based on the two PI4s and the application of the investigated algorithms on both PI4s with the same secret key that is used for both encryption (server-side) and decryption (client-side). Furthermore, the model setup was developed by implementing the shift rows and the mix column and inverse mix column on the 16X16 array based on the 128-bit-length. The results demonstrate that the developed model is secure and accurate without any loss of data. Furthermore, DES, Salsa29, RSA and DSA were tested and compared against each other utilising the same data file comprising of sensory data and the results demonstrate that all the five algorithms can cipher and decipher data without experiencing any data losses. However, the RSA and DSA execution times were 17ms and 21ms respectively, while the other AES executed at 4ns, DES at 3ns and Salsa29 2ns respectively. Therefore, this paper concludes that the investigated algorithms does provide high-level data-security, however, it is empirical to further investigate the optimization of RSA and DSA algorithms to ensure efficiency
Optimized Booth Multiplier-Based FPGA Design for Least Square Channel Estimation
No full textThis paper introduces an optimized FPGA design utilizing the Booth multiplier for efficient Least Square (LS) channel estimation in wireless communication systems. A novel technique is proposed to address the worst-case scenarios associated with Booth multiplication, enhancing both robustness and efficiency in the implementation. The design is realized on the Spartan 7 FPGA platform and validated through detailed simulation waveforms, data flow designs, and schematic diagrams. Results highlight significant resource optimization, with utilization figures of just 5.59% for Slice LUTs, 0.06% for Slice Registers, and 5.00% for DSPs. The implementation achieves high processing speed and ensures reliable performance under varied operational conditions. This research provides a scalable and efficient solution for FPGA-based LS channel estimation and paves the way for future advancements in FPGA-based signal processing, potentially extending benefits to other complex applications in digital communications. The proposed design framework offers valuable insights into optimizing hardware resources while effectively handling intricate computation scenarios
An analysis using a structural equation model to assessthe various factors influencing the Iraqi construction industry, with a specific focus on the moderating of organizational culture
No full textThe construction industry is of great importance as it is able to achieve cost savings and promote economic development worldwide. Regardless of a country's level of development, be it an underdeveloped country. Nevertheless, there are a number of constraints and hazards that hinder the start or progress of a construction project, and which usually have a significant negative impact on the overall project. In a previous study, the influence of a company on construction performance was investigated, leaving out certain factors. This study aims to fill this research gap by using the methodology of organizational culture and the various factors including stakeholders, communication, cost, technology, top management support and local authority support to investigate the impact on the Iraqi construction industry. The data pertaining to the research was gathered through a survey questionnaire administered to multiple construction project practitioners in Iraq. The research objective was achieved through structural equation modeling (SEM). The study operator a quantitative approach to gather data, which includes a survey questionnaire administered to construction project practitioners and interviews conducted with academicians who specialize in the construction industry. The results obtained from the SEM analysis indicate the model is appropriate for the characteristics of variables and data under investigation. The further analysis of research outcomes demonstrated that the hypotheses (H1, H2, H3, H4, H5, H6, H7, and H8) all the results were found to be statistically significantand had positive findings. A survey instrument was utilized to obtain information for the research from many construction companies in Iraq. The data have been analyzed, and an SPSS AMOS 26 software-based structural model has been constructed to test the results of the hypotheses.A moderate relationship can be inferred between organizational culture and the construction industry in Iraq, as indicated by a positive correlation coefficient of 0.036. A positive association is denoted by the positive sign that is an increase in one variable is typically accompanied by an increase in the other. A correlation coefficient of 0.08 indicates a positive relationship between organizational culture and stakeholder factors. Although the correlation demonstrates statistical significance, its magnitude suggests the strength of the relationship. A relationship exists between stakeholder factors and their influence on the Iraqi construction industry, as indicated by a positive correlation of 0.080. Alterations in construction industry developments might be correlated with stakeholder factor changes, as indicated by the positive correlation; however, the relationship is not definitive, noting that correlation does not imply causation is essential. Although the statistical relationships presented offer valuable insights, further investigation and analysis are required to comprehend the fundamental mechanisms and factors that underlie these correlations within the organizational culture and construction industry of Iraq
An Improved Plant Leaf Disease Identification Using ResNet50 and Enhanced Back Propagation Neural Network
No full textFor the Indian economy, agriculture plays a significant role. Farmers farm various plantsaccording to the seasons, but many diseases, including fungal, bacteria, Viruses, etc.,affect plants easily, preventing them from growing in the usual way. It is important tocreate a robust solution to identify the disease efficiently at the right moment and toovercome conventional techniques where the disease identification process is long and notaccurate most of the time. There are lots of researchers working on finding a good solutionfor plant diseases, and identification. Through this paper, we are trying to create a singleinputhybrid model using ResNet50 and an Enhanced Back propagation neural networkfor better plant disease identification and classification. Through this experiment, weachieved 95.7
Cybercrime in Social Media: Addressing Challenges and Implementing Solutions for Information Security
No full textSocial media has become a part of our life. It is a great way to connect people without geographic boundaries and share their ideas, feelings, and emotions. People are using social media due to the extraordinary development of digital technical equipment. Without social media, they cannot start their single day, because the social media world attracts them in many ways. There are many advantages to social media. Because of social media, the world is shrinking. We can get information about anything, from anywhere through social media. Especially, children use social media extensively. They are influenced by social media mentally and physically. Also, they are often exposed to crimes such as cyberbullying, sexual harassment, and cyberstalking by other users on social media. After COVID-19 cybercrime incidences increased against children in India. This article analyses the cybercrimes against children and legal safeguards against children in India to deal with cybercrimes. It also provides suggestions to prevent cybercrimes against children more effectively