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Endpoint detection and response using machine learning

Journal of Physics: Conference Series 2021年第1期2062卷

作者： Harmionee Kaur Richa Tiwari Computer Science Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India.201313

The need for cybersecurity has increased manifold over the past decade due to an unprecedented shift towards digital. With the increase in the number and sophistication of threats, cybersecurity experts have been forced to seek out new and efficient ways to secure endpoints on a network. Machine learning provides one such solution. This paper discusses how IoT devices are threatened and the need for endpoint security. It overviews different Machine learning-based intrusion detection systems that are currently in use e.g., STAT, Haystack, etc., and other Endpoint Detection and Response Techniques.

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QPSO-CD: Quantum-behaved particle swarm optimization algorithm with Cauchy distribution

arXiv

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arXiv 2020年

作者： Bhatia, Amandeep Singh Saggi, Mandeep Kaur Zheng, Shenggen Nayak, Soumya Ranjan Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Department of Computer Science Thapar Institute of Engineering & Technology India Center for Quantum Computing Peng Cheng Laboratory Shenzhen China Amity School Of Engineering & Technology Amity University Uttar Pradesh Noida India

Motivated by the particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analysed and compared with the classical PSO. It has been proved that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results shown that QPSO associated with Cauchy distribution and natural selection strategy outperforms other variants in context of stability and convergence. Copyright © 2020, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Efficient Predictive Model for Utilization of Computing Resources using Machine Learning Techniques

Efficient Predictive Model for Utilization of Computing Reso...

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： K.S. Sendhil Kumar M. Anbarasi G. Siva Shanmugam Achyut Shankar School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Assistant Professor Department of CSE Amity University Uttar Pradesh

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

Data mining is a viable innovation to break down and extract patterns from crude information, which can change the original data into up-to-date information. Predictive analytics includes an assortment of factual systems that analyze present and historical facts to make forecasts about future or generally obscure occasions. Machine learning incorporates statistical methods for regression and classification. The objective of machine learning is to create a predictive model that is unclear from the correct model. The assessed relative execution qualities were evaluated by Ein-Dor and feldermesser utilizing a linear regression method considering the properties machine cycle time, minimum main memory, maximum main memory, cache memory, minimum channels, and maximum channels. This relationship is communicated as a mathematical statement that predicts the reaction variable published relative performance as a linear function of the parameters. The proposed technique utilizes machine learning work to re-phrase prediction as an optimization problem. Confidence prediction and polynomial regression include imaginative application utilization and promising research. The experimental evaluation platform contains detailed performance analysis of the preferred methods. It is expected that this machine learning approach gives a quick and straightforward approach to fabricate applications.

关键词： Predictive models Mathematical model Data models Regression tree analysis Machine learning Computational modeling Linear regression

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First International Conference on Sustainable Technologies for Computational Intelligence 1

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丛书名： Advances in Intelligent Systems and Computing

2020年

作者： Ashish Kumar Luhach Janos Arpad Kosa Ramesh Chandra Poonia Xiao-Zhi Gao Dharm Singh

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Hybridizing deep neural network for genes expression classification using histone modification

Hybridizing deep neural network for genes expression classif...

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3rd Smart Cities Symposium (SCS 2020)

作者： R. Nair A. Bhagat P. K. Shukla P. K. Dutta SoET Jagran Lakecity University Bhopal India Department of Computer Application MANIT Bhopal India University Institute of Technology Department of Computer Science & Engineering RGPV Bhopal M.P. India Amity University Kolkata Department of Electronics and Communication Engineering Amity School of Engineering and Technology Kolkata India

Genes expression datasets are considered to be high dimensional dataset. It is very much needed that before predicting the genes expression dimensionality reduction must be performed. Machine learning algorithms fail to analyze genes expression due to its high dimensionality behaviour. These algorithms have to perform feature selection or feature extraction before classification. To overcome this dimensionality reduction approach deep learning algorithms are implemented which has shown improved performance. This paper is also based on the approach of deep learning algorithm which is based on a classification approach using hybridization of a deep neural network named Hybrid Deep Neural Network (HDNN) in which Convolutional Neural Networks are combined with Recurrent Neural Networks to perform prediction. Parameters like accuracy, AUC, precision, and F1score have shown improved performance than other state of art algorithms with 85.5%, 0.84, 0.81, and 0.79 respectively.

关键词： image classification neural nets learning (artificial intelligence) recurrent neural nets feature extraction pattern classification genetics

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Empirical Analysis of Bitcoin network (2016-2020)

Empirical Analysis of Bitcoin network (2016-2020)

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Ajay Kumar Abhishek Kumar Pranav Nerurkar Muhammad Rukunuddin Ghalib Achyut Shankar Zheng Wen Xin Qi NIT Patna Bihar India MPSTME NMIMS University Dept. of CE&IT VJTI Mumbai Mumbai India School of Computer Science and Engineering VIT Vellore India Amity School of Engineering and Technology Amity University Waseda University Japan

ISBN: (数字)9781728187556

ISBN: (纸本)9781728193106

Bitcoin system (or Bitcoin) is a peer-to-peer and decentralized payment system that uses cryptocurrency named bitcoins (BTCs) and was released as open-source software in 2009. Bitcoin platform has attracted both social and anti-social elements. On the one hand, it is social as it ensures the exchange of value, maintaining trust in a cooperative, community-driven manner without the need for a trusted third party. At the same time, it is anti-social as it creates hurdles for law enforcement to trace suspicious transactions due to anonymity and privacy. To understand how the social and anti-social tendencies in the user base of Bitcoin affect its evolution, there is a need to analyze the Bitcoin system as a network. The current paper aims to explore the local topology and geometry of the Bitcoin network during its first decade of existence. Bitcoin transaction data from 01 Jan 2016 00:00:00 GMT to 08 May 2020 13:21:33 GMT was processed for this purpose to build a Bitcoin user graph.

关键词： Bitcoin Tools Social network services Conferences

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Snippet generation using textbook corpus - an NLP approach based on BERT

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Journal of Physics: Conference Series 2021年第1期1716卷

作者： Andrew Moses G. Bharadwaja Kumar Student School of Computer Science and Engineering Vellore Institute of Technology Chennai India Professor School of Computer Science and Engineering Vellore Institute of Technology Chennai India

In today's technology-driven world, most millennials are tech-savvy. They have neither the time nor the interest in reading textbooks, newspapers or journals. They would like to immediately get instant answers and clarifications for all their doubts and questions. On many occasions, we are unable to find the exact word or meaning which we are searching for. So, if we have a clear, concise summary of a piece of literature, and we could understand what it contains with just a glimpse, we would be able to save a lot of time. This paper dwells about utilizing Natural Language Processing (NLP) to summarize a given text/textbook/paper. The state-of-the-art technology in this field has been demonstrated by Google's Bidirectional Encoder Representations from Transformers (BERT), one of the latest developments in NLP. BERT is believed to understand English better than other models because of its underlying bidirectional architecture. The present proposal is to use BERT as a sentence similarity extractor. By applying the TextRank algorithm, the sentences holding the most important information are extracted. This comes under the domain of extractive summarization. Abstractive summarization is much talked about, but since Google BERT is not built for generating text, we are utilizing it in a different way to achieve the requirement. This paper intends to discuss the use of BERT for the gen-next kids which will save time and initiate further interest for researchers in developing new programs continuously in the future.

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End-to-End Analysis for Text Detection and Recognition in Natural Scene Images

End-to-End Analysis for Text Detection and Recognition in Na...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ahlam Alnefaie Deepak Gupta Monowar H. Bhuyan Imran Razzak Prashant Gupta Mukesh Prasad Center for Artificial Intelligence School of Computer Science FEIT University of Technology Sydney Australia Department of Computer Science National Institute of Technology Arunachal Pradesh India Department of Computing Science Umea University Sweden School of Information Technology Deakin University Geeloing Australia Amity School of Engineering and Technology Noida India

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Right from the very beginning, the text has vital importance in human life. As compared to the vision-based applications, preference is always given to the precise and productive information embodied in the text. Considering the importance of text, recognition, and detection of text is also equally important in human life. This paper presents a deep analysis of recent development on scene text and compare their performance and bring into light the real modern applications. Future potential directions of scene text detection and recognition are also discussed.

关键词： Text recognition Image recognition Videos Character recognition Performance evaluation Task analysis Feature extraction

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Corrigendum to “UAVs path planning architecture for effective medical emergency response in future networks” [Physical Communication, Volume 47 (August 2021), 101337]

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Physical Communication 2024年 63卷

作者： Sara Imran Khan Zakria Qadir Hafiz Suliman Munawar Soumya Ranjan Nayak Anil Kumar Budati K.D. Verma Deo Prakash Faculty of Chemical Energy University of New South Wales Kensington Sydney 2052 NSW Australia School of Computing Engineering and Mathematics Western Sydney University Australia School of Built Environment University of New South Wales Kensington Sydney 2052 NSW Australia Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Department of ECE GRIET (Autonomous) Hyderabad 500090 India Department of Physics Shri Varshney (P.G.) College Aligarh 202001 U.P. India School of Computer Science & Engineering Faculty of Engineering Shri Mata Vaishno Devi University Kakryal Katra 182320 J&K India

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Deep Learning Techniques for using Intelligent Internet of Things Appliances to Protect Health Department Information

Deep Learning Techniques for using Intelligent Internet of T...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Neha Tyagi Dharam Buddhi Sabbineni Poojitha Kumud Pant A Aruna Tarun Parashar Department of Computer Science and Engineering Amity University Greater Noida Uttar Pradesh India Uttaranchal University Dehradun Uttarakhand India KL Business School Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Technology SNS College of Technology Anna University Sathy Road SNS Kalvi Nagar Vazhiyampalayam Coimbatore India Uttaranchal Institute of Pharmaceutical Sciences Uttaranchal University Dehradun Uttarakhand India

Profound Learning (DL) is an innovation that repeats the manner in which the human mind’s capabilities with regard to deciphering information and showing up at choices. DL was created by Google and IBM. DL and brain networks have a wide assortment of uses, some of which incorporate discourse acknowledgement, normal language handling (NLP), and PC vision. In the field of medical care, it has been put to use in the determination and therapy of an expansive assortment of constant sicknesses, as well as in the counteraction and the board of such circumstances. These calculations might help with ending the spread of ailment, improving the proficiency with which clinical analyses are made, and diminishing the monetary weight put on patients as well as authoritative staff. This paper examines the technique of profound realizing, which is utilized in an assortment of medical care disciplines, in addition to analyzing the Elliptic Curve Cryptography solution to the healthcare data security problem. Following the completion of the summaries of the data obtained from each category, comparison tables are prepared employing the essential criteria. The frameworks for DL make use of a broad array of applications, tools, techniques, and data sets. DL stands for deep learning. In order to bring this whole thing to a close, we will talk about the opportunities and challenges that lie ahead for further research into profound learning models.

关键词： Deep learning Technological innovation Medical treatment Medical services Elliptic curve cryptography Reliability Internet of Things

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