This document summarizes a student project on building a spam classifier. It defines spam and the problems it causes. It then introduces the goal of building a tool to identify spam messages. It reviews literature on spamming and organized cybercrime. The proposed solution discusses features of a modern spam filter, including threat detection using AI and machine learning. It provides a block diagram of the spam classifier that includes collecting an email data set, pre-processing email content, extracting and selecting features, implementing a K-Nearest Neighbors algorithm, and analyzing performance.
This is the presentation for Machine Learning Assignment in Dublin City University for Spring 2017. In this Project, we made an email spam filtering code using Enron Dataset
Now a days Short Message Service(SMS) is most popular way to communication for mobile user because it is cheapest mode or version for communication than other mode.SMS is used for transmitting short length msg of around 160 character to different devices such as smart phones, cellular phones, PDAs using standardized communication protocols. The amount of Short Message Service (SMS) spam is increasing. SMS spam should be put into the spam folder, not the inbox. The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. To avoid this problem SMS filtering Techniques are used. Our proposed approach filters SMS spam on an independent mobile phone on a large dataset and acceptable processing time. There are different approaches able to automatically detect and remove most of these messages, and the best-known ones are based on Bayesian decision theory and Support Vector Machines. Riya Mehta | Ankita Gandhi"A Survey: SMS Spam Filtering" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12850.pdf http://www.ijtsrd.com/computer-science/data-miining/12850/a-survey-sms-spam-filtering/riya-mehta
This is the presentation for Machine Learning Assignment in Dublin City University for Spring 2017. In this Project, we made an email spam filtering code using Enron Dataset
Now a days Short Message Service(SMS) is most popular way to communication for mobile user because it is cheapest mode or version for communication than other mode.SMS is used for transmitting short length msg of around 160 character to different devices such as smart phones, cellular phones, PDAs using standardized communication protocols. The amount of Short Message Service (SMS) spam is increasing. SMS spam should be put into the spam folder, not the inbox. The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. To avoid this problem SMS filtering Techniques are used. Our proposed approach filters SMS spam on an independent mobile phone on a large dataset and acceptable processing time. There are different approaches able to automatically detect and remove most of these messages, and the best-known ones are based on Bayesian decision theory and Support Vector Machines. Riya Mehta | Ankita Gandhi"A Survey: SMS Spam Filtering" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12850.pdf http://www.ijtsrd.com/computer-science/data-miining/12850/a-survey-sms-spam-filtering/riya-mehta
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages
Identifying Valid Email Spam Emails Using Decision TreeEditor IJCATR
The increasing use of e-mail and the growing trend of Internet users sending unsolicited bulk e-mail, the need for an antispam
filtering or have created, Filter large poster have been produced in this area, each with its own method and some parameters are
to recognize spam. The advantage of this method is the simultaneous use of two algorithms decision tree ID3 - Mamdani and Naive
Bayesian is fuzzy. The first two algorithms are then used to detect spam Bagging approach is to identify spam. In the evaluation of this
dataset contains a thousand letters have been analyzed by the software Weka charts provided in spam detection accuracy than previous
methods of improvement
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages
Identifying Valid Email Spam Emails Using Decision TreeEditor IJCATR
The increasing use of e-mail and the growing trend of Internet users sending unsolicited bulk e-mail, the need for an antispam
filtering or have created, Filter large poster have been produced in this area, each with its own method and some parameters are
to recognize spam. The advantage of this method is the simultaneous use of two algorithms decision tree ID3 - Mamdani and Naive
Bayesian is fuzzy. The first two algorithms are then used to detect spam Bagging approach is to identify spam. In the evaluation of this
dataset contains a thousand letters have been analyzed by the software Weka charts provided in spam detection accuracy than previous
methods of improvement
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in
fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data,
incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard
challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier
with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant
features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in
fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data,
incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard
challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier
with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant
features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Identification of Spam Emails from Valid Emails by Using VotingEditor IJCATR
In recent years, the increasing use of e-mails has led to the emergence and increase of problems caused by mass unwanted
messages which are commonly known as spam. In this study, by using decision trees, support vector machine, Naïve Bayes theorem
and voting algorithm, a new version for identifying and classifying spams is provided. In order to verify the proposed method, a set of
a mails are chosen to get tested. First three algorithms try to detect spams, and then by using voting method, spams are identified. The
advantage of this method is utilizing a combination of three algorithms at the same time: decision tree, support vector machine and
Naïve Bayes method. During the evaluation of this method, a data set is analyzed by Weka software. Charts prepared in spam
detection indicate improved accuracy compared to the previous methods.
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of commun
ication so are the attempts to misuse it to
take undue advantage of its low cost and high reach
ability. However, as email communication
is very cheap, spammers are taking advantage of it
for advertising their products, for
committing cybercrimes. So, researchers are working
hard to combat with the spammers. Many
spam detections techniques and systems are built to
fight spammers. But the spammers are
continuously finding new ways to defeat the existin
g filters. This paper describes the existing
spam filters techniques and proposes a multi-level
architecture for spam email detection. We
present the analysis of the architecture to prove t
he effectiveness of the architecture
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of communication so are the attempts to misuse it to
take undue advantage of its low cost and high reachability. However, as email communication
is very cheap, spammers are taking advantage of it for advertising their products, for
committing cybercrimes. So, researchers are working hard to combat with the spammers. Many
spam detections techniques and systems are built to fight spammers. But the spammers are
continuously finding new ways to defeat the existing filters. This paper describes the existing
spam filters techniques and proposes a multi-level architecture for spam email detection. We
present the analysis of the architecture to prove the effectiveness of the architecture.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Analysis of an image spam in email based on content analysisijnlc
Researchers initially have addressed the problem of spam detection as a text classification or
categorization problem. However, as spammers’ continue to develop new techniques and the type of email
content becomes more disparate, text-based anti-spam approaches alone are not sufficiently enough in
preventing spam. In an attempt to defeat the anti-spam development technologies, spammers have recently
adopted the image spam trick to make the scrutiny of emails’ body text inefficient. The main idea behind
this project is to design a spam detection system. The system will be enabled to analyze the content of
emails, in particular the artificially generated image sent as attachment in an email. The system will
analyze the image content and classify the embedded image as spam or legitimate hence classify the email
accordingly.
Detecting Spambot as an Antispam Technique for Web Internet BBSijsrd.com
Spam which is one of the most popular and also the most relevant topic that needs to be understood in the current scenario. Everyone whether it may be a small child or an old person are using emails everyday all around the world. The scenario which we are seeing is that almost no one is aware or in simple sentence they do not know what actually the spam is and what they will do in their systems. Spam in general means unsolicited or unwanted mails. Botnets are considered one of the main source of the spam. Botnet means the group of software's called bots and the function of these bots is to run on several compromised computers autonomously and automatically. The main objective of this paper is to detect such a bot or spambots for the Bulletin Board System (BBS). BBS is a computer that is running software that allows users to leave a message and access information of general interest. Originally BBSes were accessed only over a phone line using a modem, but nowadays some BBSes allowed access via a Telnet, packet switched network, or packet radio connection. The main methodology that we are going to focus is on Behavioural-based Spam Detection (BSD) method. Behavioral-based Spam Detector (BSD) combines several behaviours of the spam bots at different stages including the behaviour of spam preparation before the spam session when the spammers search for an open relay SMTP service to send e-mails through, and the behaviour of spammers while connecting to the mail server. Detecting the abnormal behaviour produced by the spam activities gives a high rate of suspicion on the existence of bots.
Web 2.0: Making Email a Useful Web AppAndy Denmark
I gave this talk at Web 2.0 Expo in San Francisco on April 23, 2008. The presentation covers historical uses of email in applications as well as some of the new and innovative ways that companies such as TripIt are integrating email in to their applications. The presentation also goes over some of the practical concerns and implementation issues you will likely encounter while building an email based web application.
WORKLOAD CHARACTERIZATION OF SPAM EMAIL FILTERING SYSTEMSIJNSA Journal
Email systems have suffered from degraded quality of service due to rampant spam, phishing and fraudulent emails. This is partly because the classification speed of email filtering systems falls far behind the requirements of email service providers. We are motivated to address this issue from the perspective of computer architecture support. In this paper, as the first step towards novel architecture designs, we present extensive performance data collected from measurement and profiling experiments using representative email filtering systems including CRM114, DSPAM, SpamAssassin and TREC Bogofilter. We provide detailed analysis of the time consuming functions in the systems under study. We also show how the processor architecture parameters affect the performance of these email filters through simulation experiments.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2. Problem Definition
The term spam generally refers to unsolicited electronic
communications (typically email) or, in some cases,
unsolicited commercial bulk communications. Some
refer to this kind of email simply as junk email.
Beyond the annoyance and the time wasted sifting
through unwanted messages, spam can cause
significant harm by infecting users’ computers with
malicious software capable of damaging systems and
stealing personal information. It also can consume
network resources.
3. Introduction
A spam message classification is a step towards
building a tool for scam message identification
and early scam detection.
A piece of software that processes incoming emails
so as to prevent spam from reaching a user's inbox.
4. Review of Literature
Images Book Authors Description
Spamming
the
Spammers
Book by
Peter
Dabbene
Dieter P. Bieny resumes his campaign against e-mail
spammers, seeking justice and entertainment value at
every turn. Can he still convince the scammers to invest
their time and effort in an ultimately fruitless endeavor, or
have they caught on to his game?
Spam: A
Shadow
History of the
Internet
Book by
Finn
Brunton
The vast majority of all email sent every day is spam, a
variety of idiosyncratically spelled requests to provide
account information, invitations to spend money on
dubious products, and pleas to send cash overseas.
Spam
Nation: The
Inside Story
of Organized
Cybercrime
Book by
Brian Krebs
There is a Threat Lurking Online with the Power to Destroy
Your Finances, Steal Your Personal Data, and Endanger
Your Life.
5. Proposed Solution
What ShouldYou Expect fromYour Spam Filter?
Threat detection
Modern filters will often have some form of integrated threat
detection solution.
This means that it will use AI and machine learning to analyze
trillions of data points in order to get a better
understanding of how attackers shift their approach and
what should raise a red flag.
This involves the scanning of message content and
attributes, as well as domains and addresses associated
with malicious intent, and other anomalies to know what
to filter and what to allow.
7. Step 1: E-mail Data Collection
The dataset contained in a corpus plays a crucial role in assessing the
performance of any spam filter. Many open-source datasets are freely
available in the public domain. Below mentioned two datasets are
widely popular as they contain a huge amount of emails.
8. Step 2: Pre-processing of E-mail content
At this step, we mainly perform tokenization of mails. Tokenization is
a process where we break the content of an email into words and
transform big messages into a sequence of representative symbols
termed tokens. These tokens are extracted from the email body,
header, subject, and image.
9. Step 3: Feature Extraction and Selection
After pre-processing, we can have a large number of words. Here
we can maintain a database that contains the frequency of the
different words represented in each column. These attributes can
be categorized on a different basis, like:
Important attributes: Frequency of repeated words, Number of
semantic discrepancies, an Adult content bag of words, etc.
Additional Attributes: Sender account features like Sender
country, IP address, email, age of sender, Number of replies,
number of recipients, and website address.
Less important attributes: Geographical distance between
sender and receiver, Sender’s date of birth, Account lifespan, Sex
of sender, and Age of the recipient.
10. Step 4: Implementation
Similar to the Nearest Neighbour algorithm, the K-Nearest
Neighbour algorithm serves the purpose of clustering. Still,
instead of giving just one nearest instance, it looks at the closest
K instances to the new incoming instance. Based on the
frequency of those K instances, K-NN classifies the new
instances. The value of K is considered to be a hyperparameter
that needs tuning. To tune this, one can take one of the famous
Hit and Trial approaches where we try some K's values and then
check the model's performance.
11. Step 5: Performance Analysis
Now our algorithm is ready, so we must check the performance
of the model. Even a single missed important message may
cause a user to reconsider the value of spam filtering. So we
must be sure that our algorithm will be as close to 100%
accurate. But some researchers feel that considering only the
accuracy as the evaluation parameter for spam classification is
not enough.
12. References
David Strom, "'Phishing' IdentityTheft Is Gaining
Popularity," Security Pipeline, November 20, 2003.
Yahoo, Microsoft, AOL Sue Under New Anti-Spam
Law," Bloomberg News, March 10, 2004.
Jonathan Krim, "EarthLink to OfferAnti-Spam E-Mail
System," Washington Post, May 7, 2003.
EarthLinkWins Antispam Injunction," Associated
Press, May 7, 2003.