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Investigating the Benefits of Adopting Secure Shell (SSH) in Wireless Network Security

Investigating the Benefits of Adopting Secure Shell (SSH) in...

2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Gavathri, C. Patil, Vivek D. Singh, Dhiraj Kumar Kumar, Ritesh Narmadha, T. Kalra, Hitesh Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

cozy Shell is an encrypted network protocol that has become increasingly popular in network security. Using SSH, organizations can defend their wireless networks from community penetration and malicious interest. SSH affords encryption for statistics transfers, thus stopping records robbery and interception of better-level protocols, which includes statistics confidentiality, integrity, and authentication. Similarly, complex SSH configurations can offer defense in opposition to assaults such as phishing, malware dissemination, and man-in-the-middle attacks. This paper will examine the feasibility and effectiveness of SSH for use in Wi-FiWi-Fi network safety. It will discuss the benefits of SSH for defensive a Wi-FiWi-Fi network and the demanding situations related to its implementation. Additionally, the paper will become aware of the metrics used to degree the overall performance of an SSH-blanketed community and suggest a secure framework for SSH implementation. In the end, this paper will explore the potential for further research within the region of wireless network protection using SSH. © 2023 IEEE.

关键词： Authentication

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An Investigation into the Effectiveness of DNS-Based Authentication for Wireless Networks 3

An Investigation into the Effectiveness of DNS-Based Authent...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Katyal, Ashu Pandian, Rajesh Sharma, Ritu Rajan, T. Surulivel Sharma, Nakul S. Singh, Vaishali Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

the security of Wi-Fi networks is turning into an increasing number, significant with the growth of Wi-Fi technology. To prevent malicious entities from connecting to the network, getting admission to manipulate mechanisms was advanced, which might be based totally on authentication. Conventional authentication mechanisms like passwords are prone to brute-pressure attacks. Numerous options, such as dynamic DNS-primarily based authentication, have been proposed to provide stronger authentication. This research paper investigates the effectiveness of Domain Name system (DNS)-based authentication for wireless networks. The DNS is a critical component of the internet infrastructure and is used to translate domain names into IP addresses. With the rapid growth of wireless networks and the increasing popularity of devices that connect to the internet, securing these networks has become a major concern. Traditional methods of authentication, such as username and password, have proven to be vulnerable to various attacks. This study aims to explore the potential of DNS-based authentication in addressing these security concerns. Through a literature review and analysis of existing authentication protocols, this paper evaluates the strengths and weaknesses of DNS-based authentication. The research also includes a survey of wireless network users to gather their insights and experiences with current authentication methods. The results of this study will provide valuable insights into the effectiveness of DNS-based authentication and its potential as a secure method for wireless network authentication. The effects imply that DNS-primarily based authentication has several wonderful blessings over traditional approaches and may be a powerful mechanism for securing wireless networks. © 2023 IEEE.

关键词： authentication effectiveness improvements mechanism technologies

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A Novel approach for data mining Classification using J48DT Classifier for Intrusion Detection system 11

A Novel approach for Data mining Classification using J48DT ...

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11th IEEE International Conference on Communication systems and Network Technologies, CSNT 2022

作者： Manimegalai, T. Ravishankar, T. Nadana Kannagi, L. Kannan, K. Anitha, G. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Data Science and Business System Srm Institute of Science and Technology Chennai Kattankulathur India Department of Computer and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Ece R.M.K. College of Engineering and Technology Puduvoyal Chennai India Department of Ece Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9781665480383

Because of the growing quantity of network traffic that is being transmitted across the network in today's modern era, network attack detection has become a necessity. The technique of data mining is extremely important in the hunt for network assaults and anomalies. These strategies aid in the selection and refinement of meaningful and relevant knowledge from enormous data sets, which is beneficial in many situations. The process of data mining aids in the categorisation of important information for Intrusion Detection systems (IDS). The IDS provides warnings for the network traffic informing it of any foreign intrusions into the network. We show how different data mining methods may be used for intrusion detection to offer a safe network environment in the research article. The J48DT Classifier is utilised for classification in this case. Comparisons based on characteristics such as data accuracy, the discovery of valuable patterns, and the extraction of useful information between approaches are made. © 2022 IEEE.

关键词： Intrusion detection

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Designing Robust Deep Learning Models for RealTime data Analysis in Machine Learning 3

Designing Robust Deep Learning Models for RealTime Data Anal...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Borade, Monali R. Gupta, Mukur Faiz, Ayaan Acharjya, Kalyan Rajesh, A. Nunciya, P.M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India

ISBN: (纸本)9798350319125

The developing call for real-time statistics analysis and selection-making has been a primary driver of improving deep studying models for the device getting to know. These models permit the evaluation of massive statistics sets, the generation of predictions from the statistics, and the speedy studying of new behaviors and patterns. Deep mastering fashions have been efficaciously used for various tasks, including natural language processing, computer imagination and prescient, and reinforcement studying. However, because of their complexity and a massive variety of parameters, they can be unreliable and volatile when uncovered to real-global records. This paper outlines strategies and techniques to lay out deep mastering fashions that can be robust to actual-world records. We also provide high-quality practices for deploying and retaining deep learning models in manufacturing packages. Those strategies include regularization techniques, model architectures, facts pre-processing techniques, and model validation and monitoring techniques. We additionally speak of the numerous strategies for assessing and improving the reliability of deep getting-to-know fashions. Subsequently, we provide a case study of a product that uses deep studying fashions to stumble on and classify objects in near-actual time in a manufacturing place. © 2023 IEEE.

关键词： improving maintaining production regularization techniques

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Discovering Innovative Applications for Real-Time data Analysis with Machine Learning 3

Discovering Innovative Applications for Real-Time Data Analy...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jadhav, Prranjali Raja Praveen, K.N. Aggarwal, Prateek Gautam, Sandeep Naval, Preeti Nirmala, D. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

This paper investigates the capability for discovering modern programs for actual-time information analysis the use of system learning. We will take a look at the technology and strategies used in system getting to know and talk their adaptive potential to interpret and analyze dynamically changing facts inputs. Especially, we can examine existing technological methods to understand the capacity of AI to discover creative solutions to complicated challenges. We will also explore capability techniques to contain gadget gaining knowledge of into present real-time records analysis platforms and architectures. Finally, we are able to speak the programs of machine studying in real-time records evaluation and its potential to allow fast and correct decision making. This paper gives a theoretical framework to apprehend and harness the power of device gaining knowledge of to investigate and interpret real-time facts in modern approaches. © 2023 IEEE.

关键词： Information analysis

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Investigating and Implementing the Efficiency of Image Restoration Techniques in Digital Image Processing 3

Investigating and Implementing the Efficiency of Image Resto...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Acharjya, Kalyan Yuvaraj, S. Jadhav, Varsha D. Sidhu, Amritpal Sinha, Deepak Kumar Kaneria, Ojasvee Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

This look at explores the efficiency of diverse photo recovery strategies, inclusive of noise reduction, histogram change, facet enhancement, polishing, and smoothing, in digital picture processing. An extensive evaluation of the techniques becomes conducted to assess their effectiveness in restoring degraded virtual pictures. Numerous objective metrics, together with Structural Similarity (SSIM) Index, peak sign-to-Noise Ratio (PSNR), and mean Squared blunders (MSE), have been used to quantitatively examine the first-rate of the restored photographs. Effects indicated that noise discount and area enhancement furnished the finest improvements within the snap shots. Histogram changes and sprucing also stepped forward the high-quality of the pictures, at the same time as the smoothing technique ended in a moderate lower in pleasant. This examines offers a detailed insight into the efficiency of various photo healing techniques and can serve as a reference for in addition investigations. © 2023 IEEE.

关键词： Investigations Smoothing Strategies Structural Techniques

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Enhancing data Visualization in Real-Time with Machine Learning 3

Enhancing Data Visualization in Real-Time with Machine Learn...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sripriya, T. Kachhoria, Renu Dev, Sudhanshu Mahesh, T.R. Nivesh Acharjya, Kalyan Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

Statistics visualization is an effective tool for information and communicating insights from massive facts sets. However, giving complicated records in a correct and complete style becomes challenging, mainly when the statistics are dynamic and change rapidly in real time. Device learning may be leveraged to enhance records visualization, making it more particular and comprehensive. This paper discusses the integration of machine learning techniques to enhance data visualization in real-time. The exponential growth of data in recent years has made it challenging for traditional techniques to effectively visualize and interpret data in real-time. Machine learning algorithms offer the ability to analyze and classify data in real-time, providing a more accurate and efficient way to visualize complex datasets. By leveraging machine learning, data visualization can be enhanced by automatically generating visual representations that are tailored to the data and user preferences. This paper presents an overview of the different machine learning techniques that can be applied to data visualization, along with their advantages and limitations. Additionally, a case study is presented to demonstrate the effectiveness of using machine learning for real-time data visualization. Through this study, it is shown that the integration of machine learning can significantly improve the speed, accuracy, and usability of data visualization in real-time scenarios. By using including the strength of machine learning to statistics visualization, such methods can permit quicker and deeper insights into records, taking into consideration higher selection making within the modern-day international wherein facts modifications swiftly. © 2023 IEEE.

关键词： interactive interested machine potential visualization

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Adaptive Modeling for Real-Time data Analysis with Machine Learning 3

Adaptive Modeling for Real-Time Data Analysis with Machine L...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mahesh, T.R. Singh, Shweta Srivastav, Vaibhav Kalra, Hitesh Surulivel Rajan, T. Yadav, Gitanjali B. Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Adaptive Modeling for actual-Time information evaluation with system studying is a critical studies area in the modern-day technology of information-in depth evaluation. Conventional gadget studying strategies are static, meaning that the models do no longer evolve as information scientist interactions occur or because the data changes over time. As a result, models can grow to be obsolete and further downstream machine overall performance will become constrained. Consequently, the want for adaptive modeling algorithms has arisen with the intention to permit dynamic, system-stage getting to know from statistics. Such strategies look to provide affordable predictions for real-time inputs, while also looking forward to enough destiny reaction behavior to make more accurate predictions for destiny, unseen facts. The purpose of this approach is to create a solution which optimally balances accuracy and flexibility for an ever converting environment. One of these system could be carried out for predictive upkeep, diagnostic analysis, cyber security detection or self-sustaining vehicle decision making. © 2023 IEEE.

关键词： Forecasting

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Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction

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Intelligent Automation & Soft Computing 2023年第2期35卷 1277-1293页

作者： M.Vinoth Kumar M.Prakash M.Naresh Kumar H.Abdul Shabeer Department of Information Science and Engineering Dayananda Sagar Academy of Technology&ManagementKanakapura Main Road560082BangaloreIndia Department of Data Science and Business Systems School of Computing SRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia Department of ECE Vardhaman College of EngineeringHyderabad501218India IBM India Pvt Ltd. DLF IT ParkChennai600125India

The most noteworthy neurodegenerative disorder nationwide is appar-ently the Alzheimer's disease(AD)which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy,a sen-sitive method for evaluating the AD has to be developed *** to the correla-tions between ocular and brain tissue,the eye(retinal blood vessels)has been investigated for predicting the ***,en enhanced method named Enhanced Long Short Term Memory(E-LSTM)has been proposed in this work which aims atﬁnding the severity of AD from ocular ***ﬁnd the level of disease severity,the new layer named precise layer was introduced in E-LSTM which will help the doctors to provide the apt treatments for the patients *** avoid the problem of overﬁtting,a dropout has been added to *** the existing work,boundary detection of retinal layers was found to be inaccurate during the seg-mentation process of Optical Coherence Tomography(OCT)image and to over-come this issue;Particle Swarm Optimization(PSO)has been *** the best of our understanding,this is theﬁrst paper to use Particle Swarm *** compared with the existing works,the proposed work is found to be per-forming better in terms of F1 Score,Precision,Recall,training loss,and segmen-tation accuracy and it is found that the prediction accuracy was increased to 10%higher than the existing systems.

关键词： Alzheimer's disease enhanced LSTM particle swarm optimization OCT image boundary detection

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Leveraging Machine Learning to Predict IFC Project Success and Failure 1

Leveraging Machine Learning to Predict IFC Project Success a...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jayanthi, S. Ganesan, Jeya Begum, Ismatha Ramesh, E. Balaraman, Ravikumar Gunasree, Sunkam The ICFAI Foundation for Higher Education Faculty of Science and Technology Department of AI&DS Telangana India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Guru Nanak Institute of Technology Department of Information Technology Telangana Ibrahimpatnam India ICFAI Business School The ICFAI Foundation for Higher Education Department of Finance and Accounting Telangana India Teegala Krishna Reddy Engineering College Department of Information Technology Telangana India

ISBN: (纸本)9798331518943

This research expounds on the efficacy and impact of a machine learning-based approach in managing the investment services projects of the International Finance Corporation (IFC). It is achieved by focusing on the impact of financial interventions and project completion. We used a detailed dataset to assess trends in project status over time. The details about the growth of completed and active projects and the fluctuations in pending stages, like signing, disbursement, and approval are available in the dataset. We experimented with the ML models, including Random Forest, Gradient Boosting, Logistic Regression, SVC, and Decision Tree, to predict project outcomes. These models are evaluated with the metrics such as, accuracy, precision, recall, F1-score, and ROC AUC. After rigorous experiments, we observed that Gradient Boosting achieved the highest accuracy (90.76%) and Random Forest closely followed (90.43%). We identified significant trends that include a high success rate in project completion, an incremental growth in active projects, and a low occurrence of hold and pending approval statuses. We also performed statistical analysis and observed that loans dominate IFC's financial support and risk management and guarantees see less use. Additionally, correlations between total investments and other financial products highlight a diversified investment strategy. The findings emphasize opportunities for improving project approval efficiency, managing bottlenecks, and implementing data-driven decision-making to optimize investment strategies and enhance sustainable development outcomes. This research offers actionable insights for policymakers, demonstrating how ML can improve project management processes and maximize the impact of IFC investments in developing economies. © 2024 IEEE.

关键词： Decentralized finance

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