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Prevention of Attacks on Spanning Tree Protocol 1

2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Adhikari, Manoj Singh Gupta, Ritika Raj, Dharm Astya, Rani Ather, Danish Agrawal, Ambuj Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India Amity University in Tashkent Tashkent Uzbekistan

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

Spanning Tree Protocol (STP) is an extensively used protocol in Ethernet networks that ensures loop-free paths by selectively blocking redundant links. However, STP is vulnerable to various attacks that can disrupt network operations, compromise network security, and lead to network downtime. This research proposes a novel approach to enhance the security and resilience of Ethernet networks by leveraging machine learning algorithms to detect and prevent attacks on the Spanning Tree Protocol. The core objective is to design a robust intrusion detection and prevention system that can adaptively safeguard the STP infrastructure in real-time. The first stage of the study involves collecting a comprehensive dataset of normal and attack scenarios in STP networks, including various attack types and their associated characteristics. Leveraging supervised learning techniques, machine learning models are trained on this dataset to learn the patterns and behaviors of different attacks on the STP. And finally, unsupervised learning algorithms are employed to analyze the network traffic and detect anomalies in real-time. This approach aims to identify any deviations from normal behavior, thereby indicating potential STP attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Implementation of WSN in the Smart Hanger to Facilitate MRO Operations on Aircraft Fuselage Using Machine Learning 1

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Singh, Tripti Rahamatkar, Surendra Department of Computer Science and Engineering ASET AUC Raipur India Faculty of Engineering and Technology Amity School of Engineering and Technology Amity University Chhattisgarh Raipur India

ISBN: (数字)9789819959747

ISBN: (纸本)9789819959730

New technology techniques have lately been adopted to make visual examinations of the outer aircraft fuselage easier. Automated image identifiers are now capturing high-resolution images of the aircraft for offline analysis inside the tarmac. Initiating the concept of smart hanger for optimization of MRO operations, it is noted that the properly trained dataset may result in accurate outcomes. Some acquisitions were annotated to give the vast dataset needed to train machine learning systems, particularly for important fault identification and defect detection. The high imbalance (i.e., unequal distribution between classes) of this dataset is an inherent problem: the rarest and most valued examples constitute a small number of elements among thousands of annotated objects. Just when enough data is there for each of the desired class, deep learning approaches have proven to be very effective, and also systems with lesser complexity like Support Vector Machine needs lesser data and dedicated methods of few-shot learning (Prototypical Network, matching network etc.) that can easily learn through limited illustrations. On the basis of the applicative scenario, several techniques are contrasted for the proposed system. Preliminary findings reveal that there is existence of empirical frontiers regarding dataset volume training, indicating associated strategy may be preferred. On the basis of these findings, a way for combining diverse approaches to produce the greatest outcomes in categorical accuracy, with a focus on underrepresented categories is proposed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Support vector machines

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Sentimental Technique Implementation on Textual Data 6

Sentimental Technique Implementation on Textual Data

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

作者： Karthik, Baggam Sai Bharat, Dhavileswarapu N.S.S.V.V. Durga Alam, Md Afroz Sai, Bandari Tharun Rana, Mrinalini Sharma, Smita Yadav, Suman Avdhesh School of Computer Science and Engineering Lovely Professional University Punjab Kapurthala India Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

The increasing amount of real-time or near-real-time data in the big data age provides organizations with opportunities to make data-driven decisions. However, social media data, such as that generated by Twitter, is typically unstructured and difficult to manage. This paper proposes an efficient text data pre-processing approach and develops an algorithm for training decision tree, random forest, SVM, and logistic regression classifiers on a dataset. The algorithm weights the sentiment score based on the hashtag and cleaned text weights, and the most efficient data pre-processing strategy is identified using a novel technology called the hash vectorizer. The effectiveness of the algorithm is tested against manually sorted sentiments and sentiments created before text data pre-processing, and the resultsshow its accuracy is comparable to human-annotated feelings. This research contributes to the existing body of knowledge by providing a precise method for cleaning social media data and a new approach to sentiment analysis. © 2023 IEEE.

关键词： Decision tree Hashing Vectorizer Hybrid model Logistic Regression Random Forest Sentimental Analysis SVM

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Optimizing Satellite Constellations for Global Internet Coverage: A Comparative Analysis of Communication Protocols and Orbital Configuration

Optimizing Satellite Constellations for Global Internet Cove...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Banerjee, Pallab Raj, Vivek Kumar, Biresh Thakur, Kanika Mitra, Dipra Kumar, Purushottam Amity University Jharkhand Dept. of Computer Science Engineering Jharkhand Ranchi India Amity University Jharkhand Amity School of Information Technology Jharkhand Ranchi India Galgotia University Dept. of Computer Science Engineering Greater Noida campus India Amity University Dept. of Computer Science Engineering Jharkhand Ranchi India

ISBN: (纸本)9798350385199

As a way to provide ubiquitous connectivity, satellite constellations are becoming more and more popular due to the growing need for worldwide internet access. In order to provide dependable and seamless worldwide internet coverage, this study investigates how satellite constellations might be optimized by comparing and contrasting communication protocols and orbital topologies. This ambitious aim involves a number of problems and complications, which are explored in the research along with the trade-offs and design considerations that must be made in order for the implementation to be effective. Two main elements form the framework of the comparative analysis. First, by considering factors like latency, data throughput, and coverage efficiency, it evaluates a range of communication protocols, including traditional geostationary satellites, Low Earth Orbit (LEO) constellations, and Medium Earth Orbit (MEO) satellites. Second, to ascertain the effect of various orbital configurations on worldwide internet coverage, the study considers satellite dispersion, orbital altitudes, and coverage patterns. By means of this study, the research endeavors to provide a valuable contribution to the current discussion on satellite constellations for worldwide internet coverage, therefore facilitating well-informed decisions about the design and implementation of these systems. © 2024 IEEE.

关键词： Geostationary satellites

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Advancing Vehicular Ad-Hoc Network Solutions in Emerging Economies: A Comparative Analysis of V2V Protocols Through Simulation Studies

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SN computer science 2024年第8期5卷 1-13页

作者： Raj, Dharm Ather, Danish Sagar, Anil Kumar Department of Computer Science & amp Engineering Sharda School of Engineering & amp Technology Sharda University Greater Noida India Amity University in Tashkent Tashkent Uzbekistan

This paper aims at moving ahead with Vehicular Ad-Hoc Network (VANET) in the emerging economies via protocol analysis of Vehicle-to-Vehicle (V2V) communication through simulation study. Given the distinct challenges in developing regions and the lack of the infrastructure and resources, the research aims to find out the feasible V2V communication protocols, applicable in VANET settings and profitable. Multiple network simulators that are widely accepted were employed in the network to perform an extensive evaluation of the primary V2V protocols, considering their performance, reliability, and scalability under various conditions that are common in emerging economies. This paper ends up with a different outcome of each protocol and some of them show a better resistance compared to the others in high-density traffic scenarios while others are more suitable to the areas with sparse, low-connectivity. The research allows for a complete evaluation of existing V2V protocols for VANET implementations in these regions with guidelines on critical points to focus on in the future deployment of VANET systems in emerging economies. This research is in line with the general efforts of improving Intelligent Transportation Systems for the developing world, which aims to realize coordinated and intelligent traffic management, safer roads and better overall traffic efficiency. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Communication protocols Intelligent Transportation Systems (ITS) Network simulation Vehicle-to-Vehicle (V2V) communication Vehicular Ad-Hoc Networks (VANETs)

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Deep Learning-Powered Camouflaged Object Recognition Model 2

Deep Learning-Powered Camouflaged Object Recognition Model

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Tomar, Riya Chauhan, Naman Chauhan, Chhavi Tomar, Yash Singh, Soumyendra Chauhan, Nishant Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of Petroleum and Energy Studies School of Computer Science Dehradun India Cmr University Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350317558

This research paper delves into the intersection of advanced technology and military strategies, specifically focusing on the imperative task of camouflaged object detection within the Indian Paramilitary forces. Leveraging cutting-edge DL Frameworks, like CNNs, Faster RCNN, and YOLOv8, our methodology aims to enhance the precision and efficiency of identifying concealed weapons and camouflaged objects in real-world scenarios. The collaboration with the Indian Paramilitary has enabled the creation of a dataset tailored to the challenges faced on the ground, reflecting a commitment to addressing contemporary threats. The study explores the global positioning of India in military technology, emphasizing the nation's role as a key player in adopting sophisticated solutions for defense. Beyond technological innovation, this research underscores a noble cause the protection of lives and the fortification of national security. The fusion of advanced technology and a profound sense of duty forms the core of this work, exemplifying a commitment to the greater good. © 2024 IEEE.

关键词： Camouflaged object detection Convolutional Neural Networks (CNNs) Deep Learning Defense Strategies Faster RCNN Indian Paramilitary Military technology Object Recognition Situational Awareness YOLOv8

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Methods and Applications for Sentiment Analysis in Online Social Networks 1

Methods and Applications for Sentiment Analysis in Online So...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Srivastava, Atul Chandra, Kunal Sharma, Vijay Shankar Pillai, Anuradha Amity School of Engineering and Technology AUUP Lucknow India Communication Engineering Manipal University Department of Computer Jaipur India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350353648

With hyped popularity of social media, sentiment analysis, a branch of natural language processing (NLP), has gained significant attention. Social media is a treasure trove for gaining insights into the thoughts, feelings, and opinions of the general public due to the abundance of user-generated content. This work offers a thorough review of sentiment analysis applied in social media, covering its methods, applications, and challenges. It discusses various approaches to sentiment analysis, from deep learning and machine learning models to rule-based techniques. Additionally, it examines the challenges associated with sentiment analysis of social media content, such as context dependence, slang, and sarcasm. The study also explores the various applications of sentiment analysis in social media, including tracking public opinion, political sentiment analysis, customer feedback analysis, and brand monitoring. By exploring these facets, this work seeks to throw light on the current state of the art in social media sentiment analysis and its implications. © 2024 IEEE.

关键词： Adversarial machine learning

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DQNANFCT: design of a deep Q-learning network for augmented network forensics via integrated contextual trust operations

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International Journal of Information technology (Singapore) 2023年第5期15卷 2729-2739页

作者： Choudhary, Ashutosh Kumar Rahamatkar, Surendra Purbey, Suniti Research Scholar Computer Science & Engineering Department Amity School of Engineering & Technology Amity University Chhattisgarh India Department of IT GMR Institute of Technology AP Rajam India Amity School of Engineering & Technology Amity University Chhattisgarh India Department of CSE GMR Institute of Technology AP Rajam India

Network forensics helps analyze pre-emptive attacks and identify attackers using temporal behavior patterns. Most deep learning methods to design high-efficiency forensic models are too complex for real-time deployment. These context-insensitive models cannot scale to multitier networks. This text proposes a deep Q-Learning network for augmented network forensics via integrated contextual trust operations to address these issues. The model collects large-scale IP & network entity-specific traffic data from different network components and processes it using pattern analysis Neural Networks. (NN). This helps predict future traffic patterns, which deep Q-Learning Network (DQLN) processes compare to attack patterns. DQLN maps attack types to participating IPs and network entities. Contextual Trust Analysis (CTA) uses temporal behavioural patterns to estimate risk severity levels for these IPs and network entities. The model uses contextual trust analysis to predict future attacks that could degrade network performance and block IPs or restrict access. Thus, the model detects attacks 4.5% more accurately and 2.9% more precisely. The model's light-weighted DQLN & CTA operations improved detection speed by 8.3% compared to standard forensic methods, making it useful for many real-time network deployments. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Context Entity Forensics IP Network Patterns Q-Learning Temporal Trust

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Fault Tolerance and Error Handling Techniques in Apache Kafka 5

Fault Tolerance and Error Handling Techniques in Apache Kafk...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Vyas, Shubham Tyagi, Rajesh Kumar Sahu, Shashank Amity Institute of Information Technology Haryana Gurgaon India Department of Computer Science and Engineering Amity School of Engineering and Technology Haryana Gurgaon India

ISBN: (纸本)9798400709418

Real Time streaming framework enables the availability of data for users in near real time. For achieving the high throughput, low latency and to avoid loss of any events at runtime, real time streaming frameworks has their own methods to accomplish this task of message handling, but there are scenarios when due to huge volume of data at the source or due to other environment or Infrastructure related issues, some of the critical data might get lost in the process. This article evaluates methods to handle data rejections in conjunction with a fault-tolerant error handling recommendations for real time data streaming. There are many frameworks available for streaming the events in real time, in this article Apache Kafka is used to examine most frequent situations when an error can occur. This article will evaluate the key performance factors in terms of capability of the framework to recover when downtime occurs. Apache Kafka [15] is a distributed real time data streaming platform;it was developed by Linked inn and currently used by many applications across the industry. It works in producer consumer fashion and with correct configuration and set up, high Throughput with low latency can be achieved. This article will explain the configurations provided with the framework that can be utilized to design and develop optimized event streaming applications. This article is explaining the different parameters that can be used to design and develop the stream processing applications and what are the available options to handle the errors during the streaming to avoid any negative impact to the application. © 2023 ACM.

关键词： Errors

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Deepfakes: A New Era of Misinformation 4th

Deepfakes: A New Era of Misinformation

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Khan, Rushan Hazela, Bramah Singh, Shikha Asthana, Pallavi Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh Lucknow India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh Lucknow India

ISBN: (纸本)9789819914784

The world that we live in today is flooded with information from sources that sometimes are true and sometimes are not. Just a simple change of narrative on the same thing might drastically change people’s opinions. With fabricated media already rampant, a new player has emerged in the synthetic media industry which goes by the name of "deepfake" which is a type of fabricated video made using deep learning, accurate to such an extent that it is sometimes impossible to differentiate the real video from the fake. The paper evaluates information from various sources and then expresses in a non-complex manner, the technical details of deepfakes, it’s formal definitions, an overview of the impact that it has had on the world till now, the technical process of making it also explaining the technical concepts in the process, and lastly the chapter talks about how some technical indicators that we can notice to spot deepfake videos, their place in today’s world and their legality is also talked about. This paper serves as the review of the technical details that we have about deepfake vides and then expresses the essential technical details about it in an organized, articulate, and an easy-to-understand manner. For people interested in making their own deepfakes, the chapter serves as an efficient starting point, by explaining the procedure in a simple yet technical way, and by explaining the tools that are required to make deepfake videos and how to use them. The paper might also serve as a way of spreading awareness about deepfakes and what the average person can do from his or her side to identify deepfake videos. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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