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Intrusion Detection in Cloud Architecture Using Machine Learning

Intrusion Detection in Cloud Architecture Using Machine Lear...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Govinda Rajulu. G R. Santhoshkumar D. Venkatesan Kalvikkarasi. S P. Santosh Kumar Patra Department of Computer Science and Design St. Martin’s Engineering College Department of Artificial Intelligence and Data Science St. Martin’s Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE St. Martin’s Engineering College Professor in ECE Department Podhagai College of Engineering & Technology St. Martin’s Engineering College

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ.

关键词： databases Cloud computing security Neural networks Intrusion detection Computer architecture Passwords Decision trees

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Crime Analysis and Prediction using Machine Learning Algorithms

Crime Analysis and Prediction using Machine Learning Algorit...

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international conference on Computational science and Technology (ICCST)

作者： P. Kirubanantham A. Saranya Arun Prasath G Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665476560

Machine Learning Algorithms are used widely nowadays in the majority of the data analysis field. Various fields of data analytics are now depending on the most advanced Machine Learning concepts and Algorithms like; K-Means, Random Forests so on and so forth. When comparing the data analysis based on society, in this research, we offer a new method for predicting and analytically classifying crimes by making use of data mining algorithms, which are K-Nearest Neighbor, Logistic Regression and Support Vector Machine algorithms. Utilizing these three commonly nsed techniques we classified the data sets along with pre-processing and the results of prediction obtained are highly accurate and excel the current models.

关键词： Prediction algorithms Support vector machines Classification algorithms Predictive models Machine learning algorithms Analytical models data models

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Feature Selection Approach Based on a Novel Variant of Hybrid Differential Evolution and PSO for Sentiment Classification 12th

Feature Selection Approach Based on a Novel Variant of Hybri...

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12th international conference on Soft computing and Pattern Recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Dixit, Abhishek Mani, Ashish Bansal, Rohit Department of Computer Science Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India Department of EEE Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India Department of Management Studies Rajiv Gandhi Institute of Petroleum Technology Raebareli Uttar Pradesh India

ISBN: (纸本)9783030736880

Over the last few years, digital text data is increasing with the advancement in use of technologies. Therefore, sentiment analysis is an important step for improving the performance of any classifier. But with the increase in data, this problem has become more complex. To improve the overall performance of classifier and reducing the data-dimensionality, Feature selection is a key sub-stage for the sentiment analysis. In this paper, we propose a feature selection approach, which is based on a novel variant of hybrid differential evolution algorithm and PSO for sentiment classification. Experimental results show that our approach achieves 97.56% and 98.54% of average accuracy by using Naïve Bayes (NB) classifier for the two Twitter datasets, viz., Testdata_2015 and twitter sanders, respectively. The results also show that our proposed approach is showing better results with state-of-art algorithms on Twitter datasets. Statistical results also showcase the effectiveness of our proposed model. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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Comparative Analysis of Compound TCP with Various End-to-End Congestion Control Mechanisms

Comparative Analysis of Compound TCP with Various End-to-End...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Subhra Priyadarshini Biswal Sheshank Gupta Arun Kumar Sanjeev Patel Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha India

Congestion control is a fundamental mechanism offered by the Transmission Control Protocol (TCP) to make sure stable data flow and efficient bandwidth allocations in the network. It mainly focuses on the adjustment of the incoming sending rate with respect to the receiving rate. The reliable TCP helps to manage the transmission rate by avoiding the state of network congestion. In recent years, different variants of TCP congestion control protocols have been proposed to enhance TCP reliability and performance under different network scenarios. Moreover, it is associated with several types of network states such as latency, throughput, and packet loss. In this paper, we have performed a comparative study on the performance analysis of various TCP congestion control protocols on NS2 simulator. The results show that CUBIC gives the highest average throughput, Vegas gives the lowest packet loss ratio, and Veno gives the lowest average end-to-end delay among other TCP congestion control techniques that are considered in this paper.

关键词： Wireless communication Protocols Packet loss Channel allocation Throughput Delays Performance analysis

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A Comprehensive Survey on Parametric and Non-Parametric Machine Learning Approaches for Bus Arrival Time Prediction

A Comprehensive Survey on Parametric and Non-Parametric Mach...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Chinmayee P Anusha H Patil Bhavika V K Ashwini B P R Sumathi Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

Public transportation plays a major role in modern society. Bus transportation is vitally important in public transportation, and it reduces more than 60% of private vehicle usage. It reduces fuel exhaustion and traffic burdens. Hence, promoting the usage of public transit as against private has become very important recently. The prediction of bus travel and arrival time for the passenger and service provider improves the reliability of the service by reducing the waiting time and uncertainty for the passengers. Several research works have focused on predicting the travel and arrival time of public transit buses in the past. A review of the existing works using parametric and non-parametric machine learning models for bus travel arrival time is conducted in this article. Parameters such as data sources, the spatial-temporal scales, and the study location are considered for the study. It is observed from the review that parametric models are widely used; especially network models, most models use data sourced from the GPS, spatially; segmented routes are well studied, and temporally, all-day data are the most studied. The existing works are well spread across the globe irrespective of developed and developing countries. Overall, it is observed from the survey that bus arrival time prediction is a well-studied area, but still, there is scope for innovative solutions in the future.

关键词： Uncertainty Soft sensors Machine learning Predictive models data models Spatial databases Parametric statistics

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Digital Learning Trends Post Covid-19

Digital Learning Trends Post Covid-19

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Nishil Madhani Eshika Shah Swarnalatha P Arjun Sivaraman Mahera Furniturewala School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781665493970

In the current society, mobile devices have become common in modern culture along with which the Internet transcends time and location constraints to become a widespread learning tool. This gave rise to digital learning which reached a new peak due to the recent COVID-19 pandemic. Not every student has the same learning opportunities. In order to make education more egalitarian, effective policies and programs must be implemented-and perhaps your unique data analysis could assist disclose the solution. Current research indicates that educational outcomes are far from egalitarian. The COVID-19 epidemic aggravated the imbalance. There is an immediate necessity for a higher standard of acknowledgement and quantify the range and impact of COVID-19 on the mentioned partisanship. This paper aims to understand the digital learning trends in the current times and visualize the inclination towards the use of different online learning tools.

关键词： COVID-19 Technological innovation Electronic learning Pandemics Education Market research Mobile handsets

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Effectiveness of SVM kernel for YOUTUBE Comment Filtering

Effectiveness of SVM kernel for YOUTUBE Comment Filtering

引用

advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Shailender Kumar Shweta Gupta Department of Computer Science & Engineering Delhi Technological University Delhi India

The impact of Social networking platform is evident in each generation and almost all fields. It made many tasks more convenient that was not possible earlier. YouTube is one such platform. It can be utilized as a source of entertainment, discussion portal, information sharing, knowledge gathering. Various newborn and popularly known organizations display their advertisements over the platform as short video or links in comment section that help content creators of the video through some financial gains. These advertisements can be greatly helpful to visitors and firm. Also the comment section may contain some cogitative discussions. With all such activities found on the platform some spiteful ones are also lurking over it. Spammers used to suffuse malicious links and data over the platform. In this study, we directed towards the comment section of the platform and executed a comparative analysis of different SVM kernels for the classification of Spam or ham comments over the YouTube application. We also studied the impact of feature extraction incorporating two feature extraction techniques Tf-Idf and BOW.

关键词： Support vector machines Video on demand Filtering Social networking (online) Computational modeling Unsolicited e-mail Standards organizations

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Achieving Linear and Systematic Perspectives to detect stroke rehabilitation exercise posture using Neural Network

Achieving Linear and Systematic Perspectives to detect strok...

引用

Smart technologies in computing, Electrical and Electronics (ICSTCEE), international conference on

作者： A. Umamageswari S. Deepa J.Faritha Banu Dept.of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

A lot of individuals suffer from motor impairment these days, led by various factors. The cause can be a stroke, accident, or even old age. Rehabilitation process for these people can be costly and ineffective, if not done properly. Integration of physiotherapy with technology can have positive outcomes. However, existing approaches are not much effective and are complex in their own ways. We present a unique system that consists of two parts: a model and a model for evaluating overall performance. With easy-to-understand instructions, the model provides feedback on exercise correctness. Individuals with chronic stroke did a variety of rehabilitation exercises while wearing inertial measurement units to capture upper body motion, and the model extracts clinically accepted features from the joint dataset. Human labelers detected primitives and labelled and segmented the associated dataset. We created a convolutional neural network model that outperformed existing models after performing automatic classification of these primitives. A data pre-processing module is included in the model to compute discrete entities of varying physical movements in sensor data. In addition, instance normalization replaces batch normalization. When the approach is applied to new patients, this leads to an increase in efficacy and probable distributional alterations. The simulations show that the virtual rehabilitation strategy suggested in this paper is more effective in existing systems than standard methods and introduces new thresholds.

关键词： Training Systematics Sensitivity Computational modeling Stroke (medical condition) Feature extraction Real-time systems

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Quantum Image Teleportation Protocol (QITP) and Quantum Audio Teleportation Protocol (QATP) by using Quantum Teleportation and Huffman Coding

Quantum Image Teleportation Protocol (QITP) and Quantum Audi...

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Trends in Quantum computing and Emerging Business technologies (TQCEBT), international conference on

作者： Mekala Karthik Jitesh Lalwani Babita Jajodia Artificial Brain Technology (OPC) Private Limited Pune India Artificial Brain Tech Inc 2055 Limestone RD STE 200-C Wilmington Delaware USA Department of Electronics and Communication Engineering Indian Institute of Information Technology Guwahati India

ISBN: (纸本)9781665453622

In order to transmit images and audio securely, the authors present the Quantum Image Teleportation Protocol (QITP) and Quantum Audio Teleportation Protocol (QATP), which utilizes the Quantum Teleportation (QT) technique combined with Huffman Coding. The QITP secures the teleportation of quantum states of an image while simultaneously encrypting and decrypting them using Huffman Coding since it is only possible to recover or decode data if the prefix codes are known. To test their approach, the authors transformed pixels or RGB values from digital images into text, which was then fed into the Huffman Coding Technique. It has the advantage of compressing the entire text, which makes it faster to transmit vast amounts of information. This work also demonstrates the Quantum Audio Teleportation Protocol (QATP) with and without Huffman coding. For proof of concept, experimental evaluations were performed for both suggested QITPs and QATPs (Standard QITP, QITP with Huffman Coding, Standard QATP, QATP with Huffman Coding), using IBM Quantum Assembly Language (IBM QASM) Simulator and real quantum hardware using the Quantum Information science Kit (Qiskit), a quantum computing platform.

关键词： Quantum computing Protocols Codes Image coding Teleportation Quantum state Quantum information science

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OFDMA Based UAVs Communication for Ensuring QoS

OFDMA Based UAVs Communication for Ensuring QoS

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international conference on advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Karabulut, Muhammet Ali Shahen Shah, A.F.M. Islam, Md Baharul Rana, Muhammad Ehsan Department of Electrical and Electronics Engineering Kafkas University Kars Turkey Department of Electronics and Communication Engineering Yildiz Technical University Istanbul Turkey School of Data Science and Engineering American University of Malta Bormla Malta School of Computing Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia

ISBN: (纸本)9789811948305

Unmanned aerial vehicles (UAV), more often familiar as drone, has been widely used both in civilian activities and military missions. UAV ad-hoc networks, also known as flying ad-hoc networks, are ad-hoc multi-UAV systems (FANETs). The FANET’s medium access control (MAC) and physical (PHY) layer specifications are defined by the IEEE 802.11 standard, which uses the enhanced distributed channel access function (EDCAF) to assure the quality of service (QoS) in the MAC layer. Because it suffers from collisions, carrier sensing multiple access with collision avoidance (CSMA/CA) is useless when a high data rate is required. As a result, the use of orthogonal frequency division multiple access (OFDMA) is advised. By lowering the collision probability, OFDMA increases performance and reduces latency. This study develops an analytical model for assessing IEEE 802.11 EDCAF performance for OFDMA-based UAV communication. A new channel contention method that combines EDCAF and OFDMA is suggested. Firstly, EDCA based prioritized channel contention is done. Then, channel access and transmission are done using OFDMA. We undertake a theoretical study based on the Markov chain model, which gives throughput, packet drop rate (PDR), and delay expressions. The simulation results verify the model performance and show that the proposed technique ensures strict delay requirements and supports QoS. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Frequency division multiple access

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