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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Detection of payload injection in Firewall Using machine learning 12

Detection of payload injection in Firewall Using Machine Lea...

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12th IEEE international conference on Communication Systems and Network Technologies, CSNT 2023

作者： Surendhar, K. Pandey, Bishwajeet Kumar Geetha, G. Gohel, Hardik Jain Deemed to Be University Department of Computer Science Engineering Bangalore India University of Houston Department of Computer Science Engineering Victoria United States

ISBN: (纸本)9781665462617

the modern web application processes a huge amount of data to deliver customized services to the users, lack of security makes this application to becomes a victim of cyberattacks. the injection attacks line SQLI and XSS are the attacks that commonly take place. the firewall is implemented in the various layers of the OSI model to provide security for the assets. Each layer firewall uses different techniques and provides the functionality to prevent the attack. One of the important firewall designs is the application layers firewall, which is also known as the web application firewall WAF, which is used to examine the external request, and protocols to prevent the application layer attacks such as SQLI and XSS. the XSS is a critical vulnerability of web applications. With the XSS the attacker can Steal confidential information in an unauthorized way. With the XSS attack, the attacker set up phishing sites on the websites. In this paper, we are discussing how the application layer firewall is detecting the malicious request patterns of the payloads using the application of machine learning algorithms. Keywords- Supervision, Firewall, XSS, machine learning, SQLI © 2023 IEEE.

关键词： Decision trees

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Research on application of English learning App based on Android Platform 4

Research on Application of English Learning App based on And...

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4th international conference for Emerging Technology, INCET 2023

作者： Miao, Yun Anhui Institute of International Business Hefei231131 China

ISBN: (纸本)9798350335750

English learning application based on Android platform is one of the best ways to learn English. this is a good tool for those who are interested in learning English. With this application, you can have fun learning English. this application is designed for everyone. No matter what their age or gender, it is easy to learn English through this application. Android means it is an operating system that runs on mobile devices such as smartphones and tablets. this study aims to develop an Android platform application to help students learn English. the main purpose of this study is to analyze and evaluate the effectiveness of the application in improving students' English language skills. this application will be developed using Android SDK version 4.0 (API level 15), with a minimum requirement of 1 GB ram and 512 MB storage space. the touch screen device runs on Android 2.2 or later, and Java 5 or later installed on the computer. © 2023 IEEE.

关键词： Students

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Enhancing Cyber Security through machine learning: A Comprehensive Analysis 4

Enhancing Cyber Security Through Machine Learning: A Compreh...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： MacHhindra, Pagare Anuradha Vijay, Bhatkudav Nikita Mahendra, Borawake Samrudhi Rahul, Cholke Anushka Anil, Pangavhane Aditya Sunil, Pawar Rohit Sanjivani College of Engineering Savitribai Phule Pune University Department of Computer Engineering Pune India

ISBN: (纸本)9798350393248

In our increasingly digital world, cyber security has become a paramount concern, with threats evolving from malicious software to sophisticated hacking techniques. To effectively combat these challenges, the integration of machine learning techniques has garnered significant attention and relevance. this comprehensive analysis delves into the potential of leveraging machine learning to fortify cyber security, discussing the utilization of predictive analytics, anomaly detection, and threat intelligence. machine learning applications offer a multifaceted approach to identifying and mitigating cyber threats. Predictive analytics harness historical data to anticipate potential security breaches, while anomaly detection techniques scrutinize deviations from established norms, aiding in real-time threat detection. Furthermore, the integration of threat intelligence allows organizations to stay ahead of evolving threats. this analysis also addresses the challenges and ethical considerations associated with deploying machine learning in cyber security, emphasizing the importance of responsible AI practices. By offering insights into the current state of the field and future prospects, this comprehensive analysis serves as a valuable resource for cyber security professionals, researchers, and policymakers, enabling them to strengthen defenses against the ever-evolving landscape of cyber threats. It underscores the significance of continued research and implementation of machine learning in safeguarding our digital ecosystem. © 2023 IEEE.

关键词： Anomaly detection

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machine learning Algorithms for Crop Yield Prediction in Real-Time Scenarios 4

Machine Learning Algorithms for Crop Yield Prediction in Rea...

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4th international conference on Signal Processing and Communication, ICSPC 2023

作者： Krishna, N. V Suresh Neeraj, Pokala Surya, Bathina Prudhvi, B.V. Deepthi, V. Hima Kalasalingam Academy of Research and Education Department of Ece India

ISBN: (纸本)9798350300772

the main pillar of the Indian Economy is Agriculture as it ensures food security and financial stability for all people in ancient days. However, due to the unnatural weather conditions, the production of yield crops has severely affected the income of farmers. this work helps early-stage farmers by guiding them through the process of sowing the right crops based on soil using the help of machine learning algorithms. the seeds are collected based on the soil parameters such as Nitrogen, phosphorous, potassium, temperature, and humidity. the system is trained using the Random Forest algorithm to suggest and help the farmers to achieve a successful harvest. A web-based application is also being developed which will allow users to get data about nutrients required for the crop. Experiments are conducted on the collected data samples from the web application and predict the crop yield with good accuracy of 91.2 % and in less response time. © 2023 IEEE.

关键词： Crops

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Optimising the computational and cost efficiency of Hierarchical Federated Edge learning 4

Optimising the computational and cost efficiency of Hierarch...

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4th international conference on Telecommunications, Optics, and computer Science, TOCS 2023

作者： Wang, Chen School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

Hierarchical Federated Edge learning (HFEL) is a promising and efficient framework, providing privacy preservation, which aims to address the issue of limited resources and network congestion by utilizing the available resources in the edge network. this paper proposes a new scheme, HFEL-Q, based on HFEL to address the high energy consumption problem of FL training, as well as the inherent communication and user heterogeneity problems. To improve training performance, a utility function is designed based on users' learning quality and training time to efficiently select the user group with the highest utility. Additionally, a frequency determination method is employed to optimize idle time and reduce energy consumption during training. Finally, the performance of HFEL-Q is evaluated on two real datasets to demonstrate its superiority over state-of-the-art baselines in terms of training rate, accuracy, and energy savings. © 2024 SPIE.

关键词： Energy conservation

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2023 4th international conference on computers and Artificial Intelligence Technology, CAIT 2023

2023 4th International Conference on Computers and Artificia...

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4th international conference on computers and Artificial Intelligence Technology, CAIT 2023

ISBN: (纸本)9798350326710

the proceedings contain 51 papers. the topics discussed include: adaptive weighted ensemble deep learning for robust rumor detection on social media;a novel QoS interval prediction method for web services based on random walk;3MVGAT: multi-modal and multi-view knowledge graph attention network for recommendation algorithm;face expression recognition combining convolutional neural network and ASM algorithm;text-enhanced reasoning for question answering over incomplete knowledge base;research on the pathway of the impact of organizational ambidextrous learning on business sustainable development performance under machine learning algorithms;path planning algorithm of unmanned ship based on improved ant colony;and a virtual body try-on model based on edge detection algorithm.

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machine learning based data and signal analysis methods for the application in failure analysis 49

Machine learning based data and signal analysis methods for ...

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49th international Symposium for Testing and Failure Analysis conference, ISTFA 2023

作者： Kögel, Michael Brand, Sebastian Altmann, Frank Fraunhofer Institute for Microstructure of Materials and Systems Halle Germany

ISBN: (纸本)9781627084741

What you learned in the tutorial Difference between Artificial Intelligence, machine learning and Deep learning Concept and basic approach of machine learning workflow Distinguish between supervised, unsupervised and reinforcement learning Models for supervised and unsupervised learning especially Neural Networks and Convolutional neural networks 4+ Case Studies that cover the field of machine learning in failure analysis What next? there are several packages and tutorials that cover the field of machine learning. © 2023 ASM international. All rights reserved.

关键词： Failure analysis

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3D Shape Estimation from RGB Data Using 2.5D Features and Deep learning 4

3D Shape Estimation from RGB Data Using 2.5D Features and De...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Ashfaq, Hamid Jalal, Ahmad Air University Dept. of Creative Technologies Islamabad Pakistan

ISBN: (纸本)9781665463775

Creation of 3D models from a single RGB image is challenging problem in image processing these days, as the technology is in its early development stage. However, the demands for 3D technology and 3D reconstruction have been rapidly increasing nowadays. the traditional approach of computer graphics is to create a geometric model in 3D and try to reproduce it onto a 2D image with rendering. the major aim of the study is to create 3D models from 2D RGB image using machine learning techniques to be less computationally complex as compared to any deep learning algorithm. the proposed model has been based on three different modules such as: 2.5D features extraction, mesh generation, and 3D boundary detection. the ShapeNet dataset has been used for comparison. the testing results has shown an accuracy of 90.77 % in the plane class, 85.72% in the chair class, and 72.14% in the automobile class. the proposed model could be applicable to problems where reconstruction of 3D models is required such as: variations in geometric scale, mix of textured, uniformly colored, and reflective surfaces. © 2023 IEEE.

关键词： Data mining

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Enhancing Mobile application Security through Android threat Classification 4

Enhancing Mobile Application Security Through Android Threat...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Josephine, Helen V.L. Rajagopal, Manikandan Gayatri, S. Lakshmi, K. Business Analytics School of Business and Management Chrisut University Bangalore560074 India School of Business and Management Chrisut University Bangalore560029 India School of Business and Management Christ Univeristy Bangalore560074 India School of Computer Science and Application Reva University Bangalore India

the Android application market has grown significantly, offering customers an ever-growing range of features to suit a variety of purposes. Users are exchanging more and more sensitive data thanks to the widespread usage of mobile applications, therefore safeguarding personal information is crucial. But this boom has also opened the door for a corresponding rise in cybersecurity risks, especially for malware and adware that target mobile devices. It is imperative to categorise mobile applications into distinct groups such as malware, adware, and benign in order to fortify the mobile ecosystem. this project's primary objective is to create and apply cutting-edge machine learning algorithms that can precisely categorise mobile apps into groups including adware, malware, and benign apps. this will necessitate investigating various machine learning strategies and ensemble methods to improve classification accuracy and robustness. Multiple machine learning models were developed based on feature importance, utilizing various machine learning techniques. the evaluation metrics showcase the effectiveness of the final model, especially the Tuned XGBoost model. While achieving a high overall accuracy of 92.51%, the findings highlight the importance of considering diverse features beyond traditional flow-based ones, providing a more robust and complete perspective on mobile network security. © 2025 the Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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Anti-Ant Framework for Android Malware Detection and Prevention Using Supervised learning 4

Anti-Ant Framework for Android Malware Detection and Prevent...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Awais, Muhammad Tariq, Muhammad Arham Iqbal, Junaid Masood, Yasir University of Lahore Computer Science & IT Department Lahore Pakistan University of Central Punjab Computer Science & IT Department Lahore Pakistan

ISBN: (纸本)9781665463775

Android users have been increasing drastically by the day, therefore apps for android users are being introduced frequently in the market which are currently available on the Play Store, APK Pure, APK Mirror, and other APK stores. Consequently, it is difficult to find apps that don't harm users' privacy, integrity, and intellectual property rights. For that reason, we made a framework named ANTI-ANT that detects malware apps and prevents them from harming the phone. We mainly target that malware which comprises of Botnets, Rootkits, SMS malware, Spywares, app installers, and ransomware. In this paper, we proposed a framework that consists of three layers of detection. the first layer is the application layer, the second layer is the user background layer, and the last one is the package layer, in order to distinguish malicious behaviors of malware. Both static and dynamic detection analyses are used for feature extraction of android malware, and then to classify them as malware or benign applications. the framework consists of the participation of multi-classifiers Logistic Regressor, Decision Tree, Random Forest, and Support Vector machine. For the training and testing, we used 13,559 samples of malware that are analyzed on the datasets of CCCS-CIC-AndMal-2020 (Canadian Institute for Cybersecurity). We detect the malware in four phases. First, we analyze the features and then perform assessments, and in Phase four, we train the machine learning models to detect the malware and prevent it by using the malware block applist generated by our model. We run that framework on 500 android phones which checks their behavior in the background and the permissions that are used in their manifest file. After training, we got the results based on the labeled datasets for our ML Models, the SVM achieved the highest accuracy of 96.64%, along with the accuracy of 91.50% for Logistic Regressor. © 2023 IEEE.

关键词： Android (operating system)

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