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

作者： Jha, Amit Kushwaha, Pradeep Kumar Srivastava, Arun Pratap Thakur, Anil Kumar, Devesh Gupta, Shubhi Amity School of Engineering and Technology Department of Computer Science Uttar Pradesh India Lloyed Institute of Engineering and Technology Uttar Pradesh Greater Noida India Lloyd Law College Uttar Pradesh Greater Noida India Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

An overview of Automatic Speech Recognition (ASR) technology, including its history and challenges in implementing it for regional languages. The challenges include lack of training data, dialectal and pronunciation variations, limited vocabulary, code mixing, and lack of standardization. Addressing these challenges requires significant research and development efforts to create accurate and reliable ASR systems for regional languages. In this research paper we have discussed various Automatic Speech Recognition model along with the application used in various field finally we have evaluated the accuracy of ASR for major Indian languages. © 2023 IEEE.

关键词： ASR Deep Neural Network Hidden Markov Model

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Framework to analyze and exploit the smart home IoT firmware

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Measurement: Sensors 2025年 37卷

作者： Kaushik, Keshav Bhardwaj, Akashdeep Dahiya, Susheela School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Amity School of Engineering and Technology Amity University Punjab Mohali India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India

Thousands of smart gadgets are linked to the internet each month, and due to various privacy and security issues, such devices may be vulnerable to evil attackers. Currently, there are around 8 billion devices online, and by the beginning of 2025, there will likely be 25 to 35 billion IoT devices globally. Despite this, the security of the devices is not receiving any more attention. Since operating system (OS) and hardware security have improved recently, researchers and hackers now seek vulnerabilities in other areas, such as firmware. When the firmware on many IoT devices isn't updated, it leaves them open to cyberattacks. The period when the Mirai Botnet was widely used is one of the situations in which we may have heard about firmware security. By gaining access to the equipment using the default credentials, the Mirai Botnet infects devices. Therefore, to analyze the firmware's contents for alteration during runtime, the authors of this research performed reverse engineering on it. Authors have exploited the smart home IoT firmware using our framework that identified ten critical network-based vulnerabilities within the firmware, with five vulnerabilities scoring a maximum CVSS score of 10.0 and the remaining five scoring 9.8, highlighting significant threats to smart home IoT devices. In addition, examining the firmware binaries demonstrates the widespread usage of dangerous functions like sprintf and strcpy in addition to the absence of critical security features like NX, PIE, RELRO, and stack protection. By offering a thorough analysis of the vulnerabilities and suggesting best practices for boosting the security of smart home IoT firmware, the results add to the body of information already in existence. © 2024 The Authors

关键词： Smart homes

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A Proposed Framework for Robot-Mediated Healthcare and Response Systems

A Proposed Framework for Robot-Mediated Healthcare and Respo...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Hafsah Khan Deepak Arora Sheenu Rizvi Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Lucknow India

The study proposes an experimental model for robot-mediated systems in healthcare. The framework outline and required modules are discussed successively alongside the methodology needed to achieve the system's base functioning. The concept of response systems and their importance in building an efficient agent for healthcare support are also discussed. The study looks at the concept of human-machine interfaces, medical robotic systems, methods of implementation and design to achieve the conception, the underlying effects, and the public's opinion on technology in healthcare. The proposed system derives from the issues faced in conveying the full capabilities of a healthcare network. The idea behind the proposal is to provide a means to leverage different components of healthcare systems to attain a holistic model capable of serving various patient and staff needs.

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Online social network automation attack detection methods for energy analysis and consumption modelling

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International Journal of Information technology (Singapore) 2024年 1-13页

作者： Rawat, Romil Sikarwar, Ranjana Maravi, Prakash Kumar Ingle, Mandakini Bhardwaj, Vivek Rawat, Anjali Rawat, Hitesh Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore Indore India Department of CSE Amity University Madhya Pradesh Gwalior India Department of Computer Science Engineering Parul University Gujarat Vadodara India Department of CSE Medicaps University Indore India School of Computer Science and Engineering Manipal University-Jaipur Rajasthan Jaipur India Department of Computer and Communication Technology University of Extremadura Badajoz Spain Department of Business Management and Economics University of Extremadura Badajoz Spain

Preventing the spread of Domain Name System (DNS) tunnelling and the use of automation infrastructure is one of the key problems that the idea of a contemporary smart OSN (online social network) must address. The proposed technique focuses on online social network automation attacks (OSNAA) based on smart social network devices (SSND) energy consumption (Ecomp) analysis based on user’s selection modes (USM) for the detection of OSNAA and Man-in-the-Middle (MiTM) attacks. Automated opcode sequence analysis (OSA) is used to improve the accuracy of OSNAA detection and localise MiTM attacks. It analyses the performance of the SSND for MiTM attacks by localising suspicious automated tools on these devices with applicable accuracy and is based on the monitoring of energy consumption, which helps to analyse and categorise the behaviour of the SSND in the state of normal or in compromised conditions by the threat agents. The suggested technique enables high-efficiency detection of SSND threats, such as MiTM attacks, at a level of about 99.91% and localization of suspicious automation tools on these machines with an accuracy of around 99.73%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cyberattack Data mining Energy efficiency and consumption MiTM Opcode analysis OSN-Automation

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Deep Learning for Cardiovascular Health: CNN-Based Automated Analysis of PCG Signals 11

Deep Learning for Cardiovascular Health: CNN-Based Automated...

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11th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2024

作者： Dwivedi, Juhi Hazela, Bramah Singh, Kamlesh Kumar Amity School of Engineering & Technology Amity University Department of Computer Science & Engineering Uttar Pradesh Lucknow India Amity School of Engineering & Technology Amity University Department of Electronics & Communication Enginerring Uttar Pradesh Lucknow India

ISBN: (纸本)9798350378726

Since cardiovascular diseases (CVDs) continue to be the largest cause of death worldwide, precise and effective diagnostic techniques are essential. Phonocardiography (PCG), which records minute heart sounds, has the potential to identify cardiac defects early. In order to automate the detection of heart abnormalities, this study explores the use of convolutional neural networks (CNNs) in PCG signal analysis. Five different kinds of heart sounds were pre-processed from a large dataset. To extract complex characteristics from PCG signals, a sophisticated CNN architecture was developed that consists of numerous convolutional and pooling layers. The model was able to identify between normal and pathological cardiac sounds with an accuracy of more than 90 %. Techniques for data normalisation, augmentation, and visualisation were used to improve training effectiveness and validate the model. This study provides an ethical and effective method of identifying circulatory irregularities at an early stage, demonstrating the promise of AI in transforming cardiac diagnostics. © 2024 IEEE.

关键词： Convolutional neural networks

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Role of Smali Opcodes in Identifying Android Malware Obfuscation Variants

Role of Smali Opcodes in Identifying Android Malware Obfusca...

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International Conference on computer, Communication, Control and Information technology (C3IT)

作者： Abhishek Anand Jyoti Prakash Singh Amity School of Engineering and Technology Amity University Patna Patna India Computer Science and Engineering National Institute of Technology Patna Patna India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

With the widespread adoption of Android and its applications, obfuscated malware poses significant challenges to the platform's security. Addressing these challenges requires automated, lightweight, and scalable models capable of understanding obfuscation variants and distinguishing obfuscated malware from non-obfuscated malware. This paper proposes a lightweight model based on static analysis, utilizing reverse engineering techniques to extract Smali opcodes. For efficient, fast, and accurate analysis, we employ the SelectKBest feature selection method to identify the most important opcodes critical for recognizing obfuscation variants. The machine learning classifier Random Forest is used for the classification tasks, distinguishing between obfuscated and non-obfuscated malware and identifying specific obfuscation variants with high precision and accuracy. Our approach achieves an impressive accuracy of up to 99.32% in detecting obfuscation and an accuracy of 83.56% in classifying different obfuscation variants. This demonstrates the potential of our lightweight model to significantly enhance the security of the Android platform by effectively identifying and classifying obfuscated malware.

关键词： Analytical models Accuracy Operating systems Reverse engineering Static analysis Feature extraction Malware Security Information technology Random forests

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications 7

Next-Generation Computational Models for Enhanced Interpreta...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gowtham Raj, R. Sreekanth, S. Nijhawan, Ginni Gupta, Shubhi Sharma, Saurabh Mounika, T. New Horizon College of Engineering Deparment of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lloyd Law College Plot No. 11 Knowledge Park II Greater Uttar Pradesh Noida201314 India Lovely Professional University Phagwara India Amity University Amity School of Engineering & Technology Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts. © 2024 IEEE.

关键词： Frequency domain analysis

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Deep Learning for Cardiovascular Health: CNN-Based Automated Analysis of PCG Signals

Deep Learning for Cardiovascular Health: CNN-Based Automated...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Juhi Dwivedi Bramah Hazela Kamlesh Kumar Singh Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Lucknow Uttar Pradesh India Department of Electronics & Communication Enginerring Amity School of Engineering & Technology Amity University Lucknow Uttar Pradesh India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

关键词： Heart Training Deep learning Ethics Accuracy Data visualization Data models Convolutional neural networks Signal analysis Phonocardiography

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Long Short-Term Memory and KNN based Approach for Smart Irrigation System using IoT

Long Short-Term Memory and KNN based Approach for Smart Irri...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Soni, Deepak Kumar Venkatesh, Koudagani Alone, Vinod N. Hazela, Bramah Chauhan, Birendra Kumar Kumar, T Ch Anil Galgotias University Department of Civil Engineering Maharashtra Greater Noida India Holy Mary Institute of Technology and Science Department of Civil Engineering Telangana Hyderabad India Vppcoe & VA Sion Computer Engineering Department Maharashtra Mumbai India Amity University Uttar Pradesh Amity School of Engineering & Technology Department of Computer Science & Engineering Lucknow Campus Uttar Pradesh India Iimt University School of Basic Sciences and Technology Uttar Pradesh Meerut India Vfstr Department of Mechanical Engineering andhra Pradesh Vadlamudi India

ISBN: (纸本)9798350333602

The inefficient use of water in common irrigation practices leads to significant water loss. In order to save water and speed up the process, an intelligent irrigation system is required. With the development of machine learning and the Internet of Things, an intelligent system may now be built to perform this task automatically with minimal human intervention. This study showcases an LSTM-RNN-trained recommendation system for enabling effective water use with minimal farmer interaction, this is all made possible through the Internet of Things. Internet of Things (IoT) sensors are deployed all around the farm to gather information about the soil and surrounding environment. The data is then stored and processed using ML methods on a cloud server, and the farmer will be provided with irrigation recommendations based on the findings. Preprocessing, Feature Selection, and Model Training are the three main components of the proposed system. Preprocessing with an M3 filter is employed. RFE is used to perform feature selection. In the end, it's put to use in LSTM - RNN model training. When compared to LSTM and RNN models, the proposed method performs better. © 2023 IEEE.

关键词： Internet of things

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A Smart NIDS Design for SDN-based Cloud IoT Network using Optimal Path Selection and RNN 9

A Smart NIDS Design for SDN-based Cloud IoT Network using Op...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kumar, Narendra Aeron, Anurag Dawar, Ishaan Kumar, Santosh Department of Computer Science and Engineering Amity University Jharkhand Ranchi India School of Computing DIT University Dehradun Dehradun India Computer science and Information Technology Siksha 'O' Anusandhan University Bhubaneshwar India

ISBN: (纸本)9798350383201

The Internet of Things (IoT) has greatly increased the possibility for developing intelligent connections and applications in many facets of daily life. Traditional security solutions frequently fail to solve security issues in cloud-based IoT systems, proving ineffective and insufficient. Software Defined Networking (SDN), which effectively detects and monitors network security vulnerabilities thanks to its programmable characteristics, presents a viable solution to these problems. In order to strengthen computer systems and fend off threats to network security, machine learning (ML) techniques have recently been included into SDN-Network Intrusion Detection Systems (NIDS). Deep learning (DL), one of these cutting-edge ML techniques, has become more popular in the SDN environment. For SDN-based cloud IoT networks, this paper design an efficient path selection system in this study utilizing DL approaches. The strategy comprises employing a Recurrent Neural Network (RNN) to identify threats and a selection mechanism to pick the best route for data delivery. This study performed tests on two datasets, KDD and UNSWNB15, and the results show that the suggested RNN outperformed existing DL methods by achieving an amazing accuracy ranging from 91% to 95% on both datasets. © 2023 IEEE.

关键词： Internet of things

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