Empirical Evaluation of the Internet Analysis System for Application in the Field of Anomaly Detection

2010 ◽  
Author(s):  
Harald Lampesberger
Keyword(s):  
Anomaly Detection ◽  
Empirical Evaluation ◽  
The Internet ◽  
Analysis System

A Novel HTTP Anomaly Detection Framework Based on Edge Intelligence for the Internet of Things (IoT)

2020 ◽  
pp. 1-7
Author(s):  
Yufei An ◽  
Jianqiang Li ◽  
F. Richard Yu ◽  
Jianyong Chen ◽  
Victor C. M. Leung
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
The Internet ◽  
The Internet Of Things

Design of Survey Analysis System Based on Data Visualization

2011 ◽  
Vol 403-408 ◽  
pp. 1491-1494
Author(s):  
Wei Yu
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Social Science ◽  
Data Visualization ◽  
The Internet ◽  
Survey Analysis ◽  
Science Study ◽  
Social Science Study ◽  
Analysis System ◽  
Three Phases

Questionnaire Survey is one of the most popular methods in Social Science study, the process of which consists of three phases: data collection, analysis and representation. At present, the practical operations of the three phases are still backward, and the methods used in data analysis and statistic are still simple. This paper designed a Survey Analysis System based on data visualization, which can not only realize survey on the internet, but also brought data visualization into the phases of data analysis and representation, so as to help users to obtain and operate data visually and handily.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

Data Analysis Using the Internet: the World Wide Web Scanning Probe Microscopy Data Analysis System

The Analyst ◽  
1997 ◽  
Vol 122 (10) ◽  
pp. 1001-1006 ◽  
Author(s):  
Philip M. Williams ◽  
Martyn C. Davies ◽  
Clive J. Roberts ◽  
Saul J. B. Tendler
Keyword(s):  
Data Analysis ◽  
World Wide Web ◽  
Scanning Probe Microscopy ◽  
World Wide ◽  
Scanning Probe ◽  
The Internet ◽  
The World ◽  
Analysis System ◽  
Microscopy Data ◽  
Data Analysis System

A Prototype Study on Electronic Rulemaking

2011 ◽  
pp. 2776-2783
Author(s):  
Gloria T. Lau ◽  
Kincho H. Law
Keyword(s):  
The Internet ◽  
Government Agencies ◽  
The Public ◽  
Huge Data ◽  
Tobacco Tax ◽  
Information And Communication ◽  
Relatedness Analysis ◽  
The Government ◽  
Analysis System ◽  
E Mail

The making of government regulations represents an important communication between the government and citizens. During the process of rulemaking, government agencies are required to inform and to invite the public to review the proposed rules. Interested and affected citizens participate by submitting comments accordingly. Electronic rulemaking, or e-rulemaking in short, redefines this process of rule drafting and commenting to effectively involve the public in the making of regulations. The goal of the e-rulemaking initiative is to integrate agency operations and technology investments; for instance, the electronic media, such as the Internet, is used as the means to provide a better environment for the public to comment on proposed rules and regulations. Based on the review of the received public comments, government agencies revise the proposed rules. With the proliferation of the Internet, it becomes a growing problem for government agencies to handle the comments submitted by the public. Large amounts of electronic data (i.e., the public comments) are easily generated, and they need to be reviewed and analyzed along with the drafted rules. As such, part of e-rulemaking involves a non-trivial task of sorting through a massive volume of electronically submitted textual comments. For example, the Federal Register (2003) documented a recent case where the U.S. Alcohol and Tobacco Tax and Trade Bureau (TTB) received over 14,000 comments in seven months, majority of which are e-mails, on a flavored malt beverages proposal. The call for public comments by the TTB included the following statement: All comments posted on our Web site will show the name of the commenter but will not show street addresses, telephone numbers, or e-mail addresses. (2003, p. 67388) However, due to the “unusually large number of comments received,” the Bureau announced later that it is difficult to remove all street addresses, telephone numbers, and e-mail addresses “in a timely manner” (2003, p. 67388). Instead, concerned individuals are asked to submit a request for removal of address information as opposed to the original statement posted in the call for comments. The example shows that an effortless electronic comment submission process has turned into a huge data processing problem for government agencies. Fortunately, the advance in information and communication technology (ICT) can help alleviate some of the barriers in e-rulemaking. This article will discuss a prototype of a comment analysis system, which classifies public comments according to related provisions in the drafted regulations. The automated relatedness analysis system can potentially save rule makers significant amount of time in reviewing public comments in regard to different provisions in the drafted regulations.

Design of Log Analysis System Based on Deep Learning for Operation System Anomaly Detection

2021 ◽  
pp. 884-892
Author(s):  
Jiaao Yu ◽  
Yanbin Jiao ◽  
Qing Guo ◽  
Chong Liang ◽  
Lanlan Rui ◽  
...  
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Log Analysis ◽  
Operation System ◽  
Analysis System

Document Analysis System Based on Awareness Learning

2011 ◽  
Vol 15 (9) ◽  
pp. 1230-1240
Author(s):  
Jie Ji ◽  
◽  
Rung-Ching Chen ◽  
Qiangfu Zhao ◽  
Keyword(s):  
Machine Learning ◽  
Human Behavior ◽  
Document Analysis ◽  
The Internet ◽  
Online Information ◽  
User Intent ◽  
Retrieval Systems ◽  
Spam Filters ◽  
Analysis System ◽  
Awareness System

The rapid growth of the Internet has naturally encouraged users to handle and process documents as online information rather than hard-copies, e.g., on paper. Dealing with large amounts of information efficiently requires classifying data into meaningful categories. Many machine-learning-based algorithms have been proposed for document classification, yielding a variety of applications such as spam filters, patent analyzers, and hot-topic retrieval systems. Different applications having different goals require different teacher signals even for the same dataset. It is not an easy task. In this study, we describe human-behavior-inspired awareness system for analyzing documents. This system starts learning with few or even no teacher signals, learning and understanding user intent through interaction with the user. We describe the structure of our proposed system and the basic steps required for analyzing documents.

scholarly journals An Empirical Evaluation of Deep Learning for Network Anomaly Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 140806-140817 ◽  
Author(s):  
Ritesh K. Malaiya ◽  
Donghwoon Kwon ◽  
Sang C. Suh ◽  
Hyunjoo Kim ◽  
Ikkyun Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Empirical Evaluation ◽  
Network Anomaly Detection
Sign in / Sign up

Export Citation Format

Share Document