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IEEE International Conference on Communications Workshops, ICC

作者： Pronaya Bhattacharya Aparna Kumari Sudeep Tanwar Ishan Budhiraja Sahil Patel Joel J.P.C Rodrigues Department of Computer Science and Engineering ASET Amity University Kolkata India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India COPELABS Luso fona University Lisbon Portugal

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

The paper presents a scheme, Quant-Jack, which employs Quantum Machine Learning (QML) to combat the cryptojacking threat in Industrial Internet-of-Things (IIoT) networks. We propose a dual-layered QML architecture, based on Quantum Neural Networks (QNN) architecture. The first layer is the QNN detection layer that operates via a weighted sum approach on time, frequency, and network traffic. The problem is modeled as multi-objective optimization, which is solved by an iterative Quantum Approximate Optimization Algorithm (QAOA). At the QML filtration layer, a Quantum Metric (QM) is computed that filters anomalies based on specified thresholds. For performance evaluation, the CSE-CIC-IDS 2018 benchmark dataset is augmented with live IIoT data. The performance is simulated for model parameters like convergence rate, attack detection time, network throughput utilization, and other metrics like precision, recall, and F1-score. For 60 nodes, the measured throughput is 11.84 KBps, which is an average improvement of 23.44% compared to baseline quantum models. the QNN model has an accuracy of 0.97 in classifying malign and benign requests, which indicates the efficacy of the proposed scheme against classical security designs.

关键词： Filtration Conferences Machine learning Telecommunication traffic computer architecture Throughput Iterative algorithms

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Association rule learning for threat analysis using traffic analysis and packet filtering approach

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International Journal of Information technology (Singapore) 2023年第6期15卷 3245-3255页

作者： Rawat, Romil Chakrawarti, Rajesh Kumar Raj, A. Samson Arun Mani, Gayathri Chidambarathanu, K. Bhardwaj, Ramakant Computer and Communication Technology Department University of Extremadura Badajoz Spain Department of Computer Science and Engineering Sushila Devi Bansal College (SDBC) Bansal Group of Institutions (BGI) Indore India Karunya Institute of Technology and Sciences Karunya Nagar Tamil Nadu Coimbatore India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Business Systems R.M.K. Engineering College Chennai India Department of Mathematics Amity University West Bengal Kolkata India

The Mirai botnet source code, which was distributed on the dark web, is still changing as malware designers modify it to produce more sophisticated varieties of the botnet—the Satori—based on the malware source code. An botnet that is tied to the Internet of Things, infested with malware, and administered by offenders creates a backdoor for cybercriminals to carry out destructive operations throughout the system. The work provides association rule learning, which reveals design characteristics and warning indications of hazards using data (a data stream) collected for darknet vulnerability research. The negative effects of threat-originating computers associated with malware scripts are investigated by looking at trends in IoT-linked applications (like destination port, TCP window lengths, and kind of service).This paper provides an study of an attack (both before and after) with the first source scripts development of a conventional IoT malware, the Satori, as well as an uncommon insight into the viral processes of an attack campaign. Trials demonstrate that the suggested system is efficient in anticipating and tracking new virus tendencies in cyberspace, demonstrating that it is a viable way for streamlining and accelerating the discovery of new illicit patterns in online activities. Satori-based mathematical modelling of destination ports, ToS, and TCP window size is provided with NICT/16 DN sensor dataset that is employed in the research. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Association rule learning Cyber-attack Dark web IoT Malware

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Deep Learning Based Image Classification for Automated Face Spoofing Detection Using Machine Learning: Convolutional Neural Network

Deep Learning Based Image Classification for Automated Face ...

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Communication & Computing (WCONF), World Conference on

作者： Biresh Kumar Kumari Manisha Anurag Sinha Abhishek Kumar Jeevan Kumar Amity Institute of Information Technology Amity University Ranchi Jharkhand India School of Computing and information Science IGNOU New Delhi India Computer Science and Engineering Jharkhand University of Technology Ranchi India RVS College of Engineering & Technology RVSCET Jamshedpur India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Facial recognition technology has gained widespread use in various applications, raising concerns about the weakness of frameworks to confront mocking assaults. This study presents an implementation of face spoofing detection using machine learning techniques. The exploration utilizes a far-reaching system that envelops data combination, preprocessing, incorporate extraction, and model readiness. A diverse dataset comprising genuine and spoofed facial images, representing various spoofing techniques, is utilized. Feature extraction leverages Convolutional Brain Organizations (CNNs) to catch discriminative facial elements. The selected machine learning model is trained and fine-tuned, with a focus on achieving robustness against evolving spoofing methods. The evaluation of the implemented system involves rigorous testing on a separate dataset, utilizing estimations like precision, exactness, survey, and F1-score. The study investigates post-processing techniques and considerations for real-time deployment, ensuring practical applicability is done by the method convolutional neural network (CNN). Cross-approval is performed to evaluate the model's speculation capacities, and the deployment phase explores integration into real-world scenarios. Ethical considerations, user feedback, and compliance with data privacy regulations are integral components of the study.

关键词： Surveys Technological innovation Face recognition Feature extraction Brain modeling Robustness Regulation Convolutional neural networks Security Testing

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Segmentation and Classification of Skin Cancer Using K-means Clustering and EfficientNetB0 Model 5th

Segmentation and Classification of Skin Cancer Using K-means...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： Anand, Vatsala Gupta, Sheifali Koundal, Deepika Nayak, Soumya Ranjan Shafi, Jana Bhoi, Akash Kumar Institute of Engineering and Technology Chitkara University Chitkara University Punjab Rajpura India Department of Systemics School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Department of Computer Science College of Arts and Science Prince Sattam Bin Abdul Aziz University Wadi Ad-Dwasir11991 Saudi Arabia KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Gangtok Sikkim737102 India

ISBN: (纸本)9789811920035

Nowadays, skin cancer is the widely recognized cancer all over the world. As the spreading rate of skin cancer is increasing day by day, so, there is a need to develop a technique that can detect skin cancer at an early stage. These days, deep learning has attained outstanding success for the detection and diagnosis of cancers. In this paper, a transfer learning-based EfficientNetB0 model is improved by adding one average pooling layer, one dropout layer, one batch normalization and one dense layer with softmax activation function. The proposed model has been simulated using the Kaggle database. The training and calculation are done with different hyper parameters such as batch size, optimizer and epochs. The data augmentation technique is applied to solve the problem of less amount of images. The proposed model has attained 87% accuracy on Adam optimizer with 32 batch size and 30 epochs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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Optimized Web Searching Using Inverted Indexing Technique 11

Optimized Web Searching Using Inverted Indexing Technique

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11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Saini, Dilip Kumar Jang Bahadur Patil, Pratap Gupta, Keshav Dev Kumar, Sarvesh Singh, Prabhishek Diwakar, Manoj Swami Rama Himalayan University Himalayan School of Science and Technology Department of Computer and Information Science Swami Ram Nagar Jolly Grant Uttarakhand Dehradun248016 India Amity University in Tashkent Labzak 70 100128 Uzbekistan Apex University Department of Computer Science and It Jaipur India Babu Banarasi das University Department of Computer Science and Engineering Lucknow India Amity University Amity School of Engineering and Technology Uttar Pradesh Noida India Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9781665480383

As the information on world wide web (WWW) is growing exponentially, it is very difficult to retrieve the relevant search results from datacenter repositories quickly. Prior research shows that the performance of an index deeply affects the retrieval results. However, the current practice of indexing in search engine is to recreate whenever any update of the dataset occurs in the repository. This practice can lead to performance efficiencies. To address this problem, we propose an approach based on an inverted index in this paper. We investigate various indexing approaches on the performance of the search engine. Experimentally analyze search engine performance improvement and efficiency by the creating Index. Compared with the traditional search engine architecture, the proposed scheme is containing various indexing modules for fitting the frequent update problem of index. The proposed approach will be compared with the conventional indexing approaches used in search engines and show that the proposed approach performs better than the traditional indexing schemes. © 2022 IEEE.

关键词： Search engines

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Critical Review: Fog Computing Dimensions for Data Security Features Cloud-Based Applications

Critical Review: Fog Computing Dimensions for Data Security ...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Kanta Prasad Sharma Girish Paliwal Deepshikha Bhargava Vijay Mohan Shrimal Ashish Kumar Amity School of Engineering & Technology Amity University Uttar Pradesh Apex Institute of Technology Chandigarh University Mohali Punjab Department of Computer Science and Engineering Institute of Technical Education and Research Siksha 'O' Anusandhan Bhuvneshwar Odisha

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Fog computing combines traditional cloud services with the proximity of users and devices to enhance service efficiency and speed up processes, allowing for mobility and several users to perform heterogeneous computing tasks. This can also be referred to as the fog network. This paper presents a study on cloud computing with a specific focus on its current advances. Mobile edge computing and cloud computing can be regarded as preferred examples of the technologies put under this category. Most of the approaches are design approaches aimed improvement of the service quality among the devices and the cloud environment. This theory has been constructed based on the active development of threat model for fog computing focusing on its services, data operations, and management. Classification criteria are developed from the main and the most importane aspects of fog computing. Benefits and disadvantages are studied. Such as the safety, secrecy, applicability and communication with the end users as in thanks to the research and review.

关键词： Threat modeling Cloud computing Multi-access edge computing Reviews Urban areas Taxonomy Medical services Quality of service Safety Standards

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Leveraging Cloud technology for Enhanced Online Examination System

Leveraging Cloud Technology for Enhanced Online Examination ...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Vijay Mohan Shrimal Girish Paliwal Kanta Prasad Sharma Deepshikha Bhargava Ashish Kumar Apex Institute of Technology Chandigarh University Mohali Punjab Amity School of Engineering & Technology Amity University Uttar Pradesh Department of Computer Science and Engineering Institute of Technical Education and Research Siksha 'O' Anusandhan Bhuvneshwar Udisha

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

An online cloud-based Examination Framework is a web application in which the user can try an online exam. Through a web browser, the user can search for an exam for a particular subject that they want to improve. Users can also review their earlier exams and learn and improve their mistakes. The user can login using his/her account details of interest, or modern users can set up an account exceptionally rapidly using the enrollment strategy. They ought to grant the subtle elements of their title, contact and their level of education.

关键词： Electronic learning Reviews Face recognition Government Education Information age Browsers Recommender systems Testing

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A Novel MobileInceptionNet Architecture for Apple Leaf Disease Classification

A Novel MobileInceptionNet Architecture for Apple Leaf Disea...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Ritesh Maurya Rahul Sharma T Gopalakrishnan Nageshwar Nath Pandey Soumyashree M Panchal Amity Centre for Artificial Intelligence Amity University Noida India Amity School of Engineering & Technology Amity University Noida India Department of Information Technology Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India Computer Science & Engineering ITER Shiksha ‘O’ Anusandhan Odisha India Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Diagnosis of agricultural plant leaf diseases is a significant, but a time-taking process, if performed manually. Therefore, automated systems are much required for the early diagnosis of the leaf diseases. Recent advancements in Deep Learning(DL) have accelerated the development of these autonomous systems. This study introduces a novel deep learning model designed, particularly to classify apple leaf diseases accurately, hence, addressing the critical aspect of agricultural management. The proposed model combines the efficiency and lightweight-nature of the MobileNet architecture with the strong feature extraction properties of Inception module and hence, is named MobileInceptionNet. To assess the efficiency of the model, metrics such as recall, precision, and f1score are used. An average precision, recall and F1-score of 0.947,0.946 and 0.946 respectively were recorded for experimental results across thirteen different classes. With a test accuracy of $\mathbf{9 4. 9 9 \%}$, the proposed model has demonstrated its potential in improving automatic diagnosis systems for apple leaf diseases.

关键词： Deep learning Measurement Accuracy Autonomous systems Plants (biology) Feature extraction Convolutional neural networks Medical diagnosis Diseases Image classification

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Binary Particle Swarm Optimization Algorithm Based on Z-shaped Probability Transfer Function to Solve 0-1 Knapsack Problem

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-9页

作者： Sun, Wei-Zhong Zhang, Min Wang, Jie-Sheng Guo, Sha-Sha Wang, Min Hao, Wen-Kuo A lecturer of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China A postgraduate student of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China A professor of School of International Finance and Banking University of Science and Technology Liaoning Anshan114051 China

Binary particle swarm optimization (BPSO) algorithm can map the original continuous searching space to the binary searching space by introducing a new velocity transfer function. For the 0-1 knapsack problem, a binary particle swarm optimization algorithm based on the Z-shaped probability transfer function was proposed. In order to solve the shortcomings of BPSO algorithm based on S-shaped and V-shaped probability functions that it is easy to fall into local optima and slow convergence speed, a new probability function (Z-shaped transfer function) was proposed. Then a penalty function strategy is adopted to deal with the violation of the constraint solutions. In order to verify the effectiveness of the proposed algorithm, the BPSO algorithm based on the Z-shaped transfer function with different parameters was used to solve the typical 0-1 knapsack problems, which is compared with the BPSO algorithm based on the S-shape and the V-shape transfer functions. Simulation experiment results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm. © 2021. All Rights Reserved.

关键词： Transfer functions

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Transfer Learning Approach for Detection of Autism Spectrum Disorder using Facial Images

Transfer Learning Approach for Detection of Autism Spectrum ...

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

作者： Krisha Patel Ashwini Ramanuj Sohan Sakhiya Yogesh Kumar Ajay Rana Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India Amity School of Engineering and Technology Amity University Greater Noida UP India

Autism Spectrum Disorder (ASD) is characterized by deficits in social interaction, communication issues, and repetitive behavioral patterns. Effective ASD intervention and assistance depend on early detection and diagnosis. Children with autism differ noticeably from normally developing (TD) children in their patterns of facial features. Autism may be indicated by a child's eyes, nose, and lip distances as well as their placement. Using common evaluation measures like accuracy, sensitivity, and specificity, the built ASD detection system is assessed. The outcomes of this study show the utility of facial image analysis in the identification of ASDs. The proposed strategy shows potential in aiding physicians and researchers in early diagnosis by utilizing face clues and machine learning techniques. This study aims to investigate how well machine learning and feature extraction algorithms based on deep learning can identify ASD in young children. Deep convolutional neural networks known for their capacity to extract significant characteristics from images, VGG16 and VGG19, are used. Several machine learning algorithms, such as logistic regression, support vector machines (SVM), naive Bayes,, and artificial neural networks (ANN), are then applied with the retrieved characteristics as input.

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