scholarly journals SPONGY (SPam ONtoloGY): Email Classification Using Two-Level Dynamic Ontology

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Seongwook Youn
Keyword(s):  
Experimental System ◽  
Experimental Results ◽  
The Internet ◽  
Spam Filtering ◽  
Spam Filter ◽  
Spam Filters ◽  
Filtering Technique ◽  
Communication Methods ◽  
Email Classification

Email is one of common communication methods between people on the Internet. However, the increase of email misuse/abuse has resulted in an increasing volume of spam emails over recent years. An experimental system has been designed and implemented with the hypothesis that this method would outperform existing techniques, and the experimental results showed that indeed the proposed ontology-based approach improves spam filtering accuracy significantly. In this paper, two levels of ontology spam filters were implemented: a first level global ontology filter and a second level user-customized ontology filter. The use of the global ontology filter showed about 91% of spam filtered, which is comparable with other methods. The user-customized ontology filter was created based on the specific user’s background as well as the filtering mechanism used in the global ontology filter creation. The main contributions of the paper are (1) to introduce an ontology-based multilevel filtering technique that uses both a global ontology and an individual filter for each user to increase spam filtering accuracy and (2) to create a spam filter in the form of ontology, which is user-customized, scalable, and modularized, so that it can be embedded to many other systems for better performance.

scholarly journals CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xin Liu ◽  
Pingjun Zou ◽  
Weishan Zhang ◽  
Jiehan Zhou ◽  
Changying Dai ◽  
...  
Keyword(s):  
Social Trust ◽  
Experimental Results ◽  
Bayesian Filtering ◽  
Spam Filtering ◽  
Network Resources ◽  
Accuracy Rate ◽  
Spam Filters ◽  
Interest Similarity ◽  
Client Side ◽  
Email Spam

Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.

Detection Approaches for Categorization of Spam and Legitimate E-Mail

2018 ◽  
pp. 274-296
Author(s):  
Rachnana Dubey ◽  
Jay Prakash Maurya ◽  
R. S. Thakur
Keyword(s):  
Data Mining ◽  
Electronic Mail ◽  
The Internet ◽  
Data Sets ◽  
Detection Approach ◽  
Supervised Learning Algorithms ◽  
Spam Filters ◽  
Filtering Technique ◽  
Single Data ◽  
E Mail

The internet has become very popular, and the concept of electronic mail has made it easy and cheap to communicate with many people. But, many undesired mails are also received by users and the higher percentage of these e-mails is termed spam. The goal of spam classification is to distinguish between spam and legitimate e-mail messages. But, with the popularization of the internet, it is challenging to develop spam filters that can effectively eliminate the increasing volumes of unwanted e-mails automatically before they enter a user's mailbox. The main objective of this chapter is to examine and identify the best detection approach for spam categorization. Different types of algorithms and data mining models are proposed, implemented, and evaluated on data sets. For improvement of spam filtering technique, the authors analyze the methods of feature selection and give recommendations of their use. The chapter concludes that the data mining models using a combination of supervised learning algorithms provide better results than single data models.

scholarly journals Boyer Moore string-match framework for a hybrid short message service spam filtering technique

2021 ◽  
Vol 10 (3) ◽  
pp. 519
Author(s):  
Arnold Adimabua Ojugo ◽  
David Ademola Oyemade
Keyword(s):  
Network Structure ◽  
Text Message ◽  
Disruptive Technology ◽  
Spam Filtering ◽  
Markov Network ◽  
String Match ◽  
Short Text ◽  
Spam Filter ◽  
Message Service ◽  
Filtering Technique

Advances in technology and the proliferation of mobile device have continued to advance the ubiquitous nature of computing alongside their many prowess and improved features it brings as a disruptive technology to aid information sharing amongst many online users. This popularity, usage and adoption ease, mobility, and portability of the mobile smartphone devices have allowed for its acceptability and popularity. Mobile smartphones continue to adopt the use of short messages services accompanied with a scenario for spamming to thrive. Spams are unsolicited message or inappropriate contents. An effective spam filter studies are limited as short-text message service (SMS) are 140bytes, 160-characters, and rippled with abbreviation and slangs that further inhibits the effective training of models. The study proposes a string match algorithm used as deep learning ensemble on a hybrid spam filtering technique to normalize noisy features, expand text and use semantic dictionaries of disambiguation to train underlying learning heuristics and effectively classify SMS into legitimate and spam classes. Study uses a profile hidden Markov network to select and train the network structure and employs the deep neural network as a classifier network structure. Model achieves an accuracy of 97% with an error rate of 1.2%.

scholarly journals Can Faulty Modules Be Predicted by Warning Messages of Static Code Analyzer?

2012 ◽  
Vol 2012 ◽  
pp. 1-8
Author(s):  
Osamu Mizuno ◽  
Michi Nakai
Keyword(s):  
Conventional Method ◽  
Detection Method ◽  
Source Code ◽  
Spam Filtering ◽  
Good Source ◽  
Original Source ◽  
Warning Messages ◽  
Spam Filter ◽  
Module Prediction ◽  
Filtering Technique

We have proposed a detection method of fault-prone modules based on the spam filtering technique, “Fault-prone filtering.” Fault-prone filtering is a method which uses the text classifier (spam filter) to classify source code modules in software. In this study, we propose an extension to use warning messages of a static code analyzer instead of raw source code. Since such warnings include useful information to detect faults, it is expected to improve the accuracy of fault-prone module prediction. From the result of experiment, it is found that warning messages of a static code analyzer are a good source of fault-prone filtering as the original source code. Moreover, it is discovered that it is more effective than the conventional method (that is, without static code analyzer) to raise the coverage rate of actual faulty modules.

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

Effect of Fluidized Bed on Permeate Flux in Ceramic Membrane Cross-Flow Microfiltration

1995 ◽  
Vol 60 (12) ◽  
pp. 2074-2084
Author(s):  
Petr Mikulášek
Keyword(s):  
Fluidized Bed ◽  
Ceramic Membrane ◽  
Cross Flow ◽  
Experimental System ◽  
Experimental Results ◽  
Spherical Particles ◽  
Permeate Flux ◽  
Model Fluid ◽  
Optimum Porosity ◽  
Tubular Membranes

The microfiltration of a model fluid on an α-alumina microfiltration tubular membrane in the presence of a fluidized bed has been examined. Following the description of the basic characteristic of alumina tubular membranes, model dispersion and spherical particles used, some comments on the experimental system and experimental results for different microfiltration systems are presented. From the analysis of experimental results it may be concluded that the use of turbulence-promoting agents resulted in a significant increase of permeate flux through the membrane. It was found out that the optimum porosity of fluidized bed for which the maximum values of permeate flux were reached is approximately 0.8.

Real-Time Indicators of Vehicle Kilometers of Travel and Congestion: One Year of Experience

2000 ◽  
Vol 1719 (1) ◽  
pp. 209-214
Author(s):  
Ho-Ling Hwang ◽  
David L. Greene ◽  
Shih-Miao Chin ◽  
Angela A. Gibson
Keyword(s):  
Missing Data ◽  
National Laboratory ◽  
Experimental System ◽  
Automated System ◽  
The Internet ◽  
Seasonal Patterns ◽  
Traffic Data ◽  
Oak Ridge ◽  
Direct Measurements ◽  
One Year

Automated traffic data posted on the Internet by four cities have been continuously downloaded, processed, and archived for more than 1 year by an automated system developed by Oak Ridge National Laboratory and funded by the U.S. Bureau of Transportation Statistics. Although the experimental system is far from national in scale and scope, it has shown that automated collection and processing of local traffic data via the Internet for national purposes is feasible and practical. Strong seasonal patterns make it too early to estimate statistical models of traffic growth, but comparisons of the same months in 1998 and 1999 indicate changes ranging from 1 percent to 3 percent for the monitored systems. Direct measurements of delay on the monitored systems are lower than published estimates for previous years. Although some progress in the input of missing data has been made, missing data are still a major problem, and better methods are needed.

Study on the Truck Scale Cheating Automatic Monitoring System Based on the GPRS

2013 ◽  
Vol 703 ◽  
pp. 240-243 ◽  
Author(s):  
Yan Jun Zhao ◽  
Shou Guang Cheng ◽  
Bin Qu
Keyword(s):  
Monitoring System ◽  
Measurement Data ◽  
Experimental System ◽  
Automatic Monitoring ◽  
Experimental Results ◽  
Pneumatic Conveying ◽  
Automatic Monitoring System ◽  
On Line ◽  
Conveying System

The truck scale is more and more applied on the weighing system. To seek illegal profits, many kinds of truck scale cheating method is found in the weighing system. To monitoring the truck scale cheating method, the truck scale cheating automatic monitoring system based on the GPRS is brought out in this paper. The truck scale cheating automatic monitoring system is designed. The monitoring system includes three parts: the monitoring terminal, the GPRS transmission module and the upper monitoring system. The truck scale measurement data of the sensors are collected by the monitoring terminal and sent to the upper monitoring system through the GPRS module. The experimental system is established on the pneumatic conveying system and the experiment is carried out. The experimental results show that the automatic monitoring system can on-line monitor the truck scale cheating method and improves the security of the truck scale weighing system.

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