检索结果-内蒙古大学图书馆

5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Konda, Rajasekhar Ayyannan, Manimuthu Chandramouli, Deepak Mohanakumar Software Engineering Manager & Ieee Senior Member Bay area San FranciscoCA United States AI/ML and Data Expert | Ieee Member Sr. Software Engineering Manager Bay Area San FranciscoCA United States Data Platform and Distributed Systems Sr. Software Engineer | Ml Platform Bay Area San JoseCA United States

ISBN: (纸本)9798350321425

A network's traffic can be monitored by an Intrusion Detection System (IDS), which then sends out warnings if any malicious behavior is detected. It's a program that checks if a website link leads to malicious content or if there was a policy violation on the network. As a case study for IDS, identifying phishing websites has been performed. One of the most effective tools for hackers to carry out a variety of security attacks is the widespread distribution of fake website Uniform Resource Locators (URLs) via messages, email, etc., with the goal of fooling unaware consumers into providing over sensitive information or worse. Traditional methods for detecting phishing websites, such as employing blacklists, are used to make predictions about phishing URLs. Traditional solutions based on blacklists are not effective at predicting many phishing websites, because so many new ones are created and released on the Internet every day. Three distinct Artificial Intelligence (AI) models-Random Forest (RF), Naive Bayes (NB), and XGBoost-are used to accurately detect dangerous URLs. Both the authentic and the fraudulent URLs were obtained from various sources. The AI model is trained with the retrieved relevant features. Following this, performance indicators are used to assess the quality of the AI models. According to the outcome of the testing data, the XGBoost model achieved a maximum accuracy of 96.98%, which is higher than the accuracy of the remaining models tested. Then the XGBoost model has been finalized and deployed as a website, anyone can immediately recognize the phishing URL. © 2023 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Forecasting Future Sales Using Linear Regression Approach

Forecasting Future Sales Using Linear Regression Approach

引用

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Jain, Ashish Gupta, Pankaj Saran, Hemlatha Kaur Parmar, Devendra Singh Bhati, Jai Prakash Rawat, Daksh CSE AI IIMT College of Engineering India Manager Data Engineer Discover Financial Services United States Independent Researcher India Discover Financial Services Principal Product Owner United States Greater Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

In this age of digital information, theoretical data generation has grown to a level that no person can handle. Thus, several machine learning models are available. Therefore, our aim is to test forecast sales in various locations with different machine learning methods and select the best algorithm of all for our problem statement. We utilize both traditional regression strategies as well as boosting techniques to evaluate whether boosting algorithms outperform traditional regression ones. Thus, it is necessary to apply intelligent data mining methods based on accurate forecasting models that can produce highly reliable results in order to carry out effective sales prediction analysis. It is important in this process to understand and to be skilled about many market segments as possible the projection of demand for business-to- business sales data analysis big data along with the accuracy of sales forecasting presents significant challenges for old-fashioned forecasting systems. This article will discuss the possibility of predicting sales volumes using different machine learning methodologies. In conclusion, this study provides realistic, accurate and valuable forecast data for future sale planning purposes. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

AI Enhanced Ontology Driven NLP for Intelligent Cloud Resource Query Processing Using Knowledge Graphs

arXiv

引用

arXiv 2025年

作者： Sunkara, Krishna Chaitanya Narukulla, Krishnaiah Independent Researcher Engineering Manager AI Data Center and Cloud Engineering IEEE Senior Member RaleighNC United States Independent Researcher Principal Software Engineer AI/ML Data Engineering IEEE Senior Member San JoseCA United States

The conventional resource search in cloud infrastructure relies on keyword-based searches or GUIDs, which demand exact matches and significant user effort to locate resources. These conventional search approaches often fail to interpret the intent behind natural language queries, making resource discovery inefficient and inaccessible to users. Though there exists some form of NLP based search engines, they are limited and focused more on analyzing the NLP query itself and extracting identifiers to find the resources. But they fail to search resources based on their behavior or operations or their capabilities or relationships or features or business relevance or the dynamic changing state or the knowledge these resources have. The search criteria has been changing with the inundation of AI based services which involved discovering not just the requested resources and identifiers but seeking insights. The real intent of a search has never been to just to list the resources but with some actual context such as to understand causes of some behavior in the system, compliance checks, capacity estimations, network constraints, or troubleshooting or business insights. This paper proposes an advanced Natural Language Processing (NLP) enhanced by ontology-based semantics to enable intuitive, human-readable queries which allows users to actually discover the intent-of-search itself. By constructing an ontology of cloud resources, their interactions, and behaviors, the proposed framework enables dynamic intent extraction and relevance ranking using Latent Semantic Indexing (LSI) and AI models. It introduces an automated pipeline which integrates ontology extraction by AI powered data crawlers, building a semantic knowledge base for context aware resource discovery. © 2025, CC BY.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Effectiveness of Deep Learning Models in Network Intrusion Detection

Evaluating the Effectiveness of Deep Learning Models in Netw...

引用

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kumar, Gaurav Gupta, Pankaj Yadav, Ganesh Kumar Verma, Richa Bhati, Jai Prakash Bhakuni, Vihan Singh IIMT College of Engineering Department of CSE Uttar Pradesh Greater Noida India Manager Data Engineer Discover Financial Services United States JSS Academy of Technical Education Department of Computer Science and Engineering UP Noida201301 India Computer Science and Engineering BBDITM Lucknow India Greater Noida India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9798331542344

Network security is now a significant concern in the current digital era since the increasing volume of cyber-attacks in day-to-day life has become a potential threat for the integrity and privacy of data. Most traditional intrusion detection systems face problems when it comes to detecting new and sophisticated attacks. The paper goes ahead to elaborate on how the concepts of machine learning and deep learning can be utilized to enhance the process of detecting and classifying network attacks. For instance, we experimented on the UNSW-NB15 dataset with different models: Decision Tree, Random Forest, SVM, KNN, MLP, and DNN. We have proved through our results that such kinds of models can considerably enhance the accuracy and robustness of IDS. With an average accuracy of 94.91%, the Decision Tree model took second place as the Deep Neural Network was closely followed. We will outline performance metrics based on the respective strengths and weaknesses of the models. This research is used to construct more effective and reliable network intrusion detection systems, which lead further into advancements of cybersecurity. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

AI and IoT based Intrusion Detection System for Cybersecurity

AI and IoT based Intrusion Detection System for Cybersecurit...

引用

International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Rajasekhar Konda Manimuthu Ayyannan Deepak Mohanakumar Chandramouli Software Engineering Manager & IEEE Senior Member San Francisco Bay area California USA Sr. Software Engineering Manager AI/ML and Data Expert | IEEE member San Francisco Bay Area California USA Sr. Software Engineer | ML Platform Data Platform and Distributed Systems San Jose Bay Area California USA

关键词：

来源：评论

学校读者我要写书评

暂无评论

Forecasting Future Sales Using Linear Regression Approach

Forecasting Future Sales Using Linear Regression Approach

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： Ashish Jain Pankaj Gupta Hemlatha Kaur Saran Devendra Singh Parmar Jai Prakash Bhati Daksh Rawat CSE AI IIMT College of Engineering Manager Data Engineer Discover Financial Services USA Independent Researcher India Principal Product Owner Discover Financial services USA Department of CSE (IoT) GNIOT (Engineering Institute) Greater Noida Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

关键词： Training Accuracy Machine learning algorithms Linear regression Collaboration Machine learning Predictive models data models Forecasting Business

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Effectiveness of Deep Learning Models in Network Intrusion Detection

Evaluating the Effectiveness of Deep Learning Models in Netw...

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： Gaurav Kumar Pankaj Gupta Ganesh Kumar Yadav Richa Verma Jai Prakash Bhati Vihan Singh Bhakuni Department of CSE IIMT College of Engineering Greater Noida Uttar Pradesh India Manager Data Engineer Discover Financial Services USA Department of Computer Science and Engineering JSS Academy of Technical Education Noida UP India Computer Science and Engineering BBDITM LUCKNOW Department of CSE (IoT) GNIOT (Engineering Institute) Greater Noida Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

关键词： Deep learning Measurement Support vector machines Accuracy Network intrusion detection Artificial neural networks Network security Robustness Decision trees Random forests

来源：评论

学校读者我要写书评

暂无评论

The Geostationary Operational Satellite R Series SpaceWire Based data System Session: SpaceWire Networks and Protocols, Long Paper

The Geostationary Operational Satellite R Series SpaceWire B...

引用

7th International SpaceWire Conference (SpaceWire)

作者： Anderson, William Krimchansky, Alexander Birmingham, Michael Lombardi, Matthew NASA Goddard Space Flight Ctr GOES R Flight Data Syst Lead Engn Greenbelt MD 20771 USA NASA Goddard Space Flight Ctr GOES R Miss Syst Manager Greenbelt MD 20771 USA NASA Goddard Space Flight Ctr GOES R Embedded Software Engn Denver CO USA Lockheed Martin GOES R Simulat & Test Engineer Denver CO USA

ISBN: (纸本)9780955719684

The Geostationary Operational Environmental Satellite R-Series Program (GOES-R, S, T, and U) mission is a joint program between National Oceanic & Atmospheric Administration (NOAA) and National Aeronautics & Space Administration (NASA) Goddard Space Flight Center (GSFC). SpaceWire was selected as the science data bus as well as command and telemetry for the GOES instruments. GOES-R, S, T, and U spacecraft have a mission data loss requirement for all data transfers between the instruments and spacecraft requiring error detection and correction at the packet level. The GOES-R Reliable data Delivery Protocol (GRDDP) [1] was developed in house to provide a means of reliably delivering data among various on board sources and sinks. The GRDDP was presented to and accepted by the European Cooperation for Space Standardization (ECSS) and is part of the ECSS Protocol Identification Standard [2]. GOES-R development and integration is complete and the observatory is scheduled for launch November 2016. Now that instrument to spacecraft integration is complete, GOES-R Project reviewed lessons learned to determine how the GRDDP could be revised to improve the integration process. Based on knowledge gained during the instrument to spacecraft integration process the following is presented to help potential GRDDP users improve their system designs and implementation.

关键词： data transfer

来源：评论

学校读者我要写书评

暂无评论

The geostationary operational satellite R series SpaceWire based data system: Session: SpaceWire networks and protocols, long paper

The geostationary operational satellite R series SpaceWire b...

引用

International SpaceWire Conference (SpaceWire)

作者： William Anderson Alexander Krimchansky Michael Birmingham Matthew Lombardi GOES-R Flight Data System Lead Engineer NASA Goddard Space Flight Center Greenbelt MD USA GOES-R Embedded Software Engineer NASA Goddard Space Flight Center Denver CO USA GOES-R Mission Systems Manager NASA Goddard Space Flight Center Greenbelt MD USA GOES-R Simulation and Test Engineer Lockheed Martin Denver CO USA

ISBN: (纸本)9781509008780

关键词： Instruments Space vehicles Protocols Program processors Delays Aerospace electronics Testing

来源：评论

学校读者我要写书评

暂无评论

Sustainable data centers

引用

XRDS: Crossroads, The ACM Magazine for Students 2011年第4期17卷 8–12页

作者： Bill Weihl Erik Teetzel Jimmy Clidaras Chris Malone Joe Kava Michael Ryan Green Energy Czar at Google Google's Greenkeeper a Staff Technical Program Manager Principal Datacenter Engineer at Google Datacenter Research & Development group Director of Data Center Operations at Google Datacenter Research and Development Mechanical Engineer at Google

How Google addresses energy and environment issues as they pertain to its global data centers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：