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Ethical Hacking: Importance, Controversies and Scope in the Future 12

Ethical Hacking: Importance, Controversies and Scope in the ...

引用

12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Vishnuram, G. Tripathi, Khushboo Tyagi, Amit Kumar School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai600127 India Amity University Department of Computer Science and Engineering Haryana India

ISBN: (纸本)9781665480352

With the ongoing digitalisation of the modern world and our quest to digitalise and automate everything, issues related to cybersecurity such as data breaches, security breaches etc., will be in the spotlight. Therefore, ethical hacking and its importance in the future can't be undermined. Ethical hacking technology has spread to almost all fields of the life and especially to all paths of computer industry;the need to protect the important data of the same should be addressed with right technology. In ethical hacking (i.e., white hat hacking) the objective in hand is to find weaknesses in the security systems and find potential data breaches and is in stark contrast to the almost universal definition of hacking i.e., to breach the security systems of individuals or companies with malicious intent and to steal data and plant viruses (black hat hacking). ethical hacking is a way of combatting and neutralising black hat hackers. With this research paper, my objective is to analyse and establish the importance of ethical hacking and also to analyse the controversies surrounding it, its pros and cons, its ethical and moral boundaries and its scope in the future. © 2022 IEEE.

关键词： Security systems

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Use of Neural Machine Translation in Multimodal Translation 6

Use of Neural Machine Translation in Multimodal Translation

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Nair, Manavi Tanwar, Sarvesh Badotra, Sumit Kukreja, Vinay Amity Institute of Information Technology Amity University Uttar Pradesh Noida India School of Computer Science and Engineering Bennett University U.P Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304480

Multimodal Neural Machine Translation (MNMT) is a type of Machine Translation that allows the translation of source language that contains various forms of information, such as images, video, audio, and signs, into the target language while maintaining the intended meaning of the source language. In MNMT, the use of deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are required to process different modalities and to generate an appropriate translation. The use of attention mechanisms, early and late fusion techniques, and hierarchical fusion have significantly improved the accuracy and fluency of translation. The performance of different MNMT systems can be evaluated in terms of various metrics such as BLEU score, accuracy, and fluency. MNMT has the potential to revolutionise the way we communicate by bridging the gap between different languages and cultures. The integration of MNMT in various industries, such as e-commerce, tourism, and healthcare, can bring significant benefits in terms of customer experience and accessibility. It states how Neural Machine Translation plays a significant role in processing different modalities in Multimodal Neural Machine Translation. More research work in this field can produce effective and innovative ideas to improve Multimodal Neural Machine Translation. © 2023 IEEE.

关键词： Machine Translation Multimodal Neural Machine Translation Natural Language Processing Introduction Neural Machine Translation

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Majority Voting-based Consensus Mechanism for UAVs Decision Making in Battlefield

Majority Voting-based Consensus Mechanism for UAVs Decision ...

引用

2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Kakkar, Riya Gupta, Rajesh Obaidiat, Mohammad S. Jadav, Nilesh Kumar Tanwar, Sudeep Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India King Abdullah Ii School of Information Technology The University of Jordan Jordan School of Computer and Communication Engineering University of Science and Technology Beijing100083 China The Amity University Uttar Pradesh Noida201301 India

ISBN: (纸本)9798350336092

The revolutionary and rapid development of Information and Communication technology (ICT) has significantly contributed to modernizing military operations by improving the long-range communication and information sharing of unmanned aerial vehicles (UAVs) on the battlefield. However, efficient decision-making between clusters of UAVs controlled by the controller station (CS) is still one of the critical aspects that can cause potential threats to the armed forces on the battlefield. Thus, we have proposed a majority voting-based mechanism to attain consensus decision-making for UAVs, i.e., followers, monitored by the CS (leader) on the battlefield. Furthermore, we have focused on various voting scenarios to choose the efficient decision (attack or retreat) for the armed forces without any conflict between leader and follower utilizing the majority voting mechanism. Finally, the performance evaluation of the proposed mechanism is analyzed and simulated using the performance metrics of consensus delay and throughput. © 2023 IEEE.

关键词： Decision making

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A Comprehensive Analysis of Various Federated Learning Techniques

A Comprehensive Analysis of Various Federated Learning Techn...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Neha Saini Dolly Sharma Deepti Mehrotra Aikaterini Bourazeri Amity School of Engineering and Technology Amity University Noida India School of Computer Science & Electronic Engineering University of Essex UK

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

A method for data privacy maintenance while training machine learning models across decentralized devices is Federated Learning (FL). It comes under the category of decentralized field of machine learning. The decentralized approach faces various challenges, one of them is aggregating model updates which come from diverse and potentially unreliable sources. This study provides a comparison review of various algorithms for privacy preservation, and maintaining security and also talks about the various federated averaging techniques. These techniques are essential for an efficient model aggregation. This research paper also provides an analysis of strengths, weaknesses, and various application areas of federated learning. This paper has included the maximum techniques available for federated learning by comparing their datasets and various other parameters. This paper also provides insight into the related future work that is proposed in various research articles.

关键词： Training Privacy Data privacy Machine learning algorithms Federated learning Reviews Intrusion detection Maintenance Information and communication technology Security

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Analysis of Big Data in Cloud Computing Technologies

Analysis of Big Data in Cloud Computing Technologies

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Sanjeev Thakur Lalit Verma Computer Science & Engineeering Amity School of Engineering & Technology Noida India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The abstract provides a brief summary of the research paper, highlighting the key topics covered. It explains that the paper will give a brief description of the various technologies and tools used for Big Data in cloud computing environments, as well as the advantages and difficulties posed by this technology. This paper reviews the research challenges and issues associated with big data analysis in situations using cloud computing. It discusses both the traits and benefits of big data analytics in cloud computing, and highlights the major research challenges such as scalability, security, privacy and resource management. The paper also presents some failure research directions and potential solutions to address these challenges.

关键词： Cloud computing Data privacy Reviews Scalability Big Data Market research Reliability

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Ensemble Methods on NSL-KDD 5

Ensemble Methods on NSL-KDD

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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Kothari, Arun Vashishtha, Purvit Singh, Prabhishek Diwakar, Manoj Pandey, Neeraj Kumar Amity University Amity School of Engineering and Technology Department of CSE Uttar Pradesh Noida India Department of Computer Science Engineering Uttarakhand Dehradun India DIT University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9781665403412

The application of machine learning and also deep learning strategies in the field of cyber safety and security is a lot more popular than in the past. From IP (Internet Protocol) web traffic classification, a filtering system for harmful web traffic for breach discovery, Machine learning is among the appealing solution that can be reliable against zero-day dangers. This paper is a concentrated study of machine learning and also its application to the cyber division for breach discovery, web traffic classification as well and also applications such as e-mail filtering systems. Ensemble learning is being implemented in this paper which has two parts namely bagging and boosting. It is basically focused on combining multiple models in order to solve a particular problem whether it be regression or classification, which in this problem statement is classification. Applicability of these methods in different cybersecurity tasks such as breach identification, recognition of viruses, phishing, forecasting cyberattacks, e.g., denial of service, fraud recognition or cyber anomalies, and many more are going to be reviewed in this paper additionally. On the whole, the objective of this paper is to have an introduction of exactly how ensemble learning can be shown reliable and also a brilliant aspect for the academic community as well as experts in the cyber industries for identifying attacks. © 2021 IEEE.

关键词： Learning systems

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Analysis of Image Classification Using Machine Learning Approach

Analysis of Image Classification Using Machine Learning Appr...

引用

Data, Computation and Communication (ICDCC), International Conference on

作者： Rinki Pakshwar Jhankar Moolchandani Kiran Tomar Department of Computer Science & Engineering School of Engineering and Technology ITM University Gwalior Madhya Pradesh India Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Gwalior Madhya Pradesh India Department of Computer Science & Engineering Amity University Gwalior Madhya Pradesh India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Among the technologies that are utilized in the research and development of contemporary science and technology, image classification is an essential technology. Scholars from a variety of nations have not only become proficient in the application of a significant amount of theoretical knowledge, but they have also established a solid foundation for the categorization of images and the description of their features. One of the most significant methods in the field of machine learning is known as support vector machine, and it has been utilized extensively in the process of image identification and classification. In the process of researching the application technology of image classification based on support vector machine, researchers from both the United States and other countries primarily focus on analysing the characteristics of image forms, textures, colours, and spatial relationships. In this Paper, the primary focus is on the application of support vector machine in image classification, with the purpose of evaluating its efficiency via the use of actual examples and calculate Performance mesearurment. Using the existing knowledge of image classification and support vector machine research as a foundation, it intends to make a contribution to the development of network technology research in the new era.

关键词： Support vector machines Knowledge engineering Accuracy Shape Image color analysis Machine learning Solids Kernel Research and development Image classification

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The Impact of Industrial Automation on the Manufacturing Industry in the Era of Industry 4.0 15

The Impact of Industrial Automation on the Manufacturing Ind...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gurav, Vidya Sunil Gugnani, Anchit Meena, Y.R. Marathe, Vijay Vijay, S. Arul Antran Nanda, Simranjeet D.K.T.E.S's Textile and Engineering Institute Department of Management Studies Ichalkaranji416115 India Amity Institute Of Physiotherapy Amity University Rajasthan Jaipur302020 India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam College of Engineering Department of Computer Science Engineering Coimbatore641032 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

With the dawn of Industry 4.0, industrial automation has become one of the most disruptive technologies in the manufacturing industry. It encompasses the use of innovative technology and methods to automate different processes related to production, which decreases dependence on human laborers and ensures increased production results with greater efficiency. It was a game changer for the industry - industrial Automation that transformed product design, production, and delivery. A significant positive aspect of using industrial Automation is that it improves the speed and accuracy at which production occurs. Robotization, artifidal intelligence, and modern equipment combined to allow manufacturing to continue mumuchquicker while being more accurate. It helps reduce production time, bringing down wastage and errors due to cost-saving, leading to better quality control. The integration resulted in Industrial automation leading towards Smart Factories where machines & systems are interconnected and communicate banks on each other. This has enabled the manufacturers to monitor and optimize their production processes, ensuring their organization planned maintenance over preventive repairs, avoiding machinery failures, or putting suppliers in check with plummeting demand. Industrial Automation Increasing Potential Job Displacement and Reskilling/Upskilling of Workers. © 2024 IEEE.

关键词： Industry Machinery Manufacturing Robotization technology

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Implementing Robust Security Protocols for Virtual Images in Cloud Computing Platform

Implementing Robust Security Protocols for Virtual Images in...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Shiv Kumar Tiwari Manish Gupta Subhrendu G. Neogi Department of Computer Science Engineering Amity School of Engineering and Technology Amity University Gwalior India Department of Computer Science Engineering The Neotia University West Bengal India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

The new technology, with the aid of newly emerging knowledge known as cloud computing, can provide resources remotely and on demand. With the use of cloud computing, users can operate in settings where they are not dependent on physical infrastructure. Numerous protocols facilitate the utilization of cloud computing resources. Cloud computing is Internet-based technology that is made available to consumers of its resources and services and is billed depending on demand and usage. All cloud servers cannot see the precise place where the user is to be configured. User-generated secure keys are employed in the proposed work for both virtual machine (VM) switching and authentication. Every time a valid user is authenticated, a secure pass key is utilized to guarantee security and authentication between the user and the system.

关键词： Resistance Cloud computing Protocols Authentication Switches Solids Virtual machines Security Servers Standards

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An Innovative Ensemble Approach to Detecting Distributed Denial of Service Attacks Using Machine Learning

An Innovative Ensemble Approach to Detecting Distributed Den...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Deepak Singh Rajput Arvind Kumar Upadhyay Dept. of Computer Science and Engineering Amity School of Engineering &Technology (ASET) Amity University Gwalior Madhya Pradesh

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

In the present era marked by rapid Internet development, there is a growing emphasis on information security. Distributed Denial of Service (DDoS) attacks poses significant threats to servers globally. Various methods and services have emerged to prevent and mitigate damage caused by these attacks. The challenge in implementing defence lies in timely attack detection, requiring a highly accurate mechanism. DDoS attack methods are diverse and evolving, rendering traditional prevention methods less effective. This research explores the application of machine learning, utilizing the CICDDoS2019 dataset to train hybrid models. The proposed ensemble model combines unsupervised K-Means for dataset labelling and supervised classifiers (Random Forest, XGBoost, AdaBoost, SVM) with Artificial Neural Network (ANN). Achieving an overall accuracy of 99.46%, this study underscores the effectiveness of deep learning in DDoS attack detection.

关键词： Training Support vector machines Accuracy Scalability Artificial neural networks Denial-of-service attack Rendering (computer graphics) Real-time systems Servers computer crime

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