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A Hybrid Approach for Network Intrusion Detection

computers, Materials & Continua 2022年第1期70卷 91-107页

作者： Mavra Mehmood Talha Javed Jamel Nebhen Sidra Abbas Rabia Abid Giridhar Reddy Bojja Muhammad Rizwan Department of Computer Science Kinnaird College for WomenLahore54000Pakistan ASET Labs IslamabadPakistan Prince Sattam bin Abdulaziz University College of Computer Science and EngineeringAlkharj11942Saudi Arabia College of Business and Information Systems Dakota State UniversityMadisonUnited States of America

Due to the widespread use of the internet and smart devices,various attacks like intrusion,zero-day,Malware,and security breaches are a constant threat to any organization’s network ***,a Network Intrusion Detection System(NIDS)is required to detect attacks in network *** paper proposes a new hybrid method for intrusion detection and attack *** proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack *** the first step,the dataset is preprocessed through the data transformation technique and min-max ***,the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model’s ***,we use various Support Vector Machine(SVM)types to detect intrusion and the Adaptive Neuro-Fuzzy System(ANFIS)to categorize probe,U2R,R2U,and DDOS *** validation of the proposed method is calculated through Fine Gaussian SVM(FGSVM),which is 99.3%for the binary *** Square Error(MSE)is reported as 0.084964 for training data,0.0855203 for testing,and 0.084964 to validate multiclass categorization.

关键词： Network security intrusion detection system machine learning attacks data mining classification feature selection

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Real-Time Facial Mask Detection Using Deep Learning 1

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3rd International Conference on Information Technology, InCITe 2023

作者： Chauhan, Ayush Rajput, Rohan Chaudhary, Divyansh Gupta, Aakanshi Gurugram Cognizant India Noida Paytm India Tuffplast Division Suraj Logistix Pvt. Ltd Jamshedpur India Department of Computer Science and Engineering ASET AUUP Noida India

ISBN: (数字)9789819959976

ISBN: (纸本)9789819959969

The COVID-19 outbreak shook the world with its continuous waves. Since, the virus was mainly spreading through respiratory droplets, wearing a face mask was made compulsory for every person in the world with other preventive measures like taking vaccine doses. As a famous saying "Prevention of disease is still better than the cure," perfectly sums up the increase in use of face masks and even shows the importance of the masks. This research is centred around the implementation of deep learning and computer vision methodologies. Image classification has also been used on the different images of people wearing masks. Some pretrained models like MobileNetV2, VGG16, ResNet50 were used along with training a new model from scratch. Subsequently, the models were applied to a live video stream to determine whether an individual is wearing a mask correctly, incorrectly, or not at all. The use of several types of masks in various aspects and various places of society is also particularly important, wrong type of mask at wrong place can also cause severe problems. Surgical masks are used in hospitals, N95 masks in chemical labs, etc. So, it was decided to classify the masks into various categories and implement it into the model such that the model can classify the type of mask as well. To sum up the research and make the model presentable, an application was developed to make the model easy to use in different scenarios and platforms such as mobile phones, desktops, etc. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Evaluating Optimization Algorithms for Dynamic Workforce Management: A Comparative Approach

Evaluating Optimization Algorithms for Dynamic Workforce Man...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Aabhas Kumar Jha Rishita Chaubey Aadhar Goel Misha Kakkar Kushagr Kakkar Computer Science and Engineering ASET Amity University Uttar Pradesh Noida UP India Amazon USA

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Effective workforce management is critical for optimizing operational efficiency in dynamic environments, particularly in healthcare settings. This paper presents a comparative analysis of five prominent optimization algorithms—Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Tabu Search (TS)—specifically applied to the problem of dynamic staffing needs in hospitals. Each algorithm is evaluated based on key performance metrics, including execution time, solution quality, scalability, and robustness. The study reveals that while GA and SA deliver high-quality solutions, ACO and TS are better suited for dynamic environments where conditions frequently change. PSO is recognized for its speed but may compromise on solution quality in more complex scenarios. The findings suggest that healthcare practitioners should consider hybrid approaches that combine the strengths of these algorithms to enhance workforce optimization. Recommendations for future research include the exploration of adaptive algorithms and the integration of machine learning techniques to further improve staffing efficiency in healthcare and other industries.

关键词： Measurement Ant colony optimization Machine learning algorithms Hospitals Heuristic algorithms Scalability Simulated annealing Robustness Particle swarm optimization Genetic algorithms

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Nirjas: An open source framework for extracting metadata from the source code

arXiv

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arXiv 2024年

作者： Bhardwaj, Ayush Sahil Pratap, Kaushlendra Mishra, Gaurav Department of Computer Science & Engineering ASET Amity University Noida India Sathyabama University India

Metadata/Comments are the critical element of any software development process. In this paper, we went on explaining how the metadata/comments in the source code can play an essential role in comprehending the software. We introduced a python based open-source framework "Nirjas" that helps us in extracting these metadata in a structured manner. There are various syntax, types and widely accepted conventions for adding a comment in the source file of different programming languages. Various edge cases can create noise in our extraction, for which we used Regex to accurately retrieve these metadata. The non-Regex method can give us the result but misses out on accuracy and noise separation. Nirjas can also separate different types of comments, source code and provide us with the details about those comments in terms of the line number, file name, language used, total SLOC, etc. Nirjas is a standalone python framework/library and can be easily and quickly installed using the source installation or using pip (package installer for Python). Nirjas was first created and started out as one of the projects during Google Summer of Code program and is currently developed and maintained under the FOSSology organization. © 2024, CC0.

关键词： Software design

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Optimizing the Capacity of Extreme Learning Machines for Biomedical Informatics Applications 1

Optimizing the Capacity of Extreme Learning Machines for Bio...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Logeshwaran, J. Bhardwaj, Rajat Salagrama, Shailaja Das, Abhijit Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India University of the Cumberland's Department of Information Technology Kentucky Williamsburg United States Amity University Aset Department of Computer Science and Engineering Karnataka Bengaluru India Rv University School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359756

the paper discusses a way of growing the capability of severe deep learning machines (ELM) for biomedical informatics programs. This technique involves varying the dimensions of the training set, the variety of neurons in the hidden layer, the kind of activation function used, the range of different sorts of neurons used and the regularization techniques *** that it will maximize the ability of ELM fashions for biomedical informatics applications, the authors advocate using a stepwise seek technique to determine the fine combination of parameters. This technique entails first defining a baseline or initial configuration of ELM parameters and then iteratively enhancing that configuration via deep learning from the empirical effects of each optimization step. distinctive regularization strategies are carried out at every optimization step which could involve pruning hidden neurons as a way to reduce the complexity of the model. The authors verified their technique on publicly to be had datasets consisting of the Michigan Imputation of missing Values (MIMV) dataset and on a dataset of clinical records from the branch of Anesthesiology, branch of health and Human services. © 2023 IEEE.

关键词： Neurons

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Generative Adversarial Networks (GANs) in computer-Generated Imagery

Generative Adversarial Networks (GANs) in Computer-Generated...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Sannan Ahmad Shah Shahhab Hasin Drabu Murtaza Mahdi Khan Dawar Bin Qayoom Kirmani Shailendra Narayan Singh Department ASET Computer Science and Engineering Amity University Noida India

In order to estimate the model using the method it is necessary to train two different models simultaneously. The first model, called the model (D) is responsible, for determining whether a particular sample is from the training data or generated by another model called G. On the hand G aims to understand the underlying distribution of the data. By training G to maximize the likelihood of D making mistakes a competitive environment is established. There are applications for G and D but one particularly notable solution emerges. D consistently evaluates any input as if it came from training data while G effectively captures the details of that training data distribution. Notably this system can be trained using known techniques like backpropagation when implementing G and D as multilayer perceptrons. This eliminates the need for inference networks or intricate Markov chains, during both training and sample generation processes. Thorough evaluations of the generated samples confirm the potential of this framework as demonstrated through testing. This innovative approach offers an efficient means to capture data distributions, which could potentially revolutionize generative modeling.

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Artificial Intelligence and Its Application in Marketing 2nd

Artificial Intelligence and Its Application in Marketing

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2nd Biennial International Symposium on Fluids and Thermal engineering, FLUTE 2023

作者： Gupta, Rahul Umang, Paridhi Gupta, Sumit Amity Business School Amity University Uttar Pradesh Greater Noida India Department of Computer Science Bennett University Greater Noida India Department of Mechanical Engineering ASET Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819731725

AI uses are fluctuating over the years owing to the development. Applications of new technologies and the kind of outcomes it has shown are tremendous in the field of marketing. AI with deep learning or machine learning enables computers to learn by themselves regarding consumer behavior, and their buying patterns. AI also supports newly launched products/services by simulating through virtual experience. The different applications areas in the field of marketing such as (transcript, speech, image acknowledgment, choice-making, self-directed vehicles, and robots) have found their utility. The first three are used extensively in marketing and the last two have found their uses in industrial applications. The examples gathered so far have proved that AI has influenced both consumer value and management operations. The research objective is to review and understand the scope of AI’s applications in the marketing mix and what learning it has offered for marketing people. To find the answers to these questions, the authors have conducted secondary research through an extensive literature review. After going through multiple examples in the marketing domain where AI is being used, it is found that the implementation of AI technology has given positive results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Agile Software Development Effort Estimation: A Comprehensive Survey

Agile Software Development Effort Estimation: A Comprehensiv...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Vikrant Malik Renuka Arora Deepti Mehrotra Hitesh Singh Engineering Amity University Uttar Pradesh India Computer Science and Engineering ASET Amity University Uttar Pradesh India Department of Information Technology ASET Amity University Uttar Pradesh India Computer Science and Engineering Noida Institute of Engineering and Technology Greater Noida India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

This survey paper examines the evolution of effort and cost estimation methodologies in Agile software development, with a focus on Serum-based projects. Traditional techniques such as Story Points and Function Points are evaluated against modern approaches that integrate machine learning (ML), fuzzy logic, and optimization algorithms. The paper synthesizes findings from various studies, including those by Arora et al. (2018), Owais and Ramakishore (2016), and Dave et al. (2021), to assess the effectiveness of these methods in improving estimation accuracy. It also considers the influence of human factors, team dynamics, and customer involvement on project success, as highlighted by Tam et al. (2020) and Meckenstock (2024). The survey identifies a trend towards data-driven estimation models, as confirmed by Fernandez-Diego et al. (2020), and suggests that future research should explore hybrid ML models and deep learning algorithms to further refine Software Cost Estimation (SCE) processes. The paper contributes to the literature by providing a comprehensive overview of the current state of Agile estimation practices and by outlining directions for future research to enhance the precision and applicability of these techniques. This abstract encapsulates the essence of the survey, summa- rizing the research question, the approach taken, the key findings, and the significance of the work within the field of Agile software development.

关键词： Surveys Technological innovation Costs Heuristic algorithms Software algorithms Estimation Agile software development Software Sustainable development Optimization

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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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Smart Healthcare System for Biomedical and Health Care Applications using Aadhaar and Blockchain 5

Smart Healthcare System for Biomedical and Health Care Appli...

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

作者： Haidar, Mumtaj Kumar, Sunil ASET Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665403412

Bitcoin cryptocurrency was proposed in 2008 and its open-source implementation was released in 2009. Blockchain was underlying technology in the implementation of bitcoin. Blockchain is decentralized which facilitate transactions across a peer-to-peer network. Researchers have been trying to explore its applications in non-financial domains. Healthcare is one such industry in which blockchain can play significant role. Blockchain technology has the potential to strengthen the healthcare industry in a variety of ways, health monitoring devices, clinical trial data, sharing and storing of patient medical records, insurance related information storage and claim settlement. The proposed system is an attempt to use Blockchain technology to address issues about healthcare data privacy, security, and ownership. © 2021 IEEE.

关键词： Blockchain

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