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
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
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
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages
MINIMIZING THE TIME OF SPAM MAIL DETECTION BY RELOCATING FILTERING SYSTEM TO ...IJNSA Journal
Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to massive number of users. Spam emails consume the network resources and cause lots of security uncertainties. As we studied, the location where the spam filter operates in is an important parameter to preserve network resources. Although there are many different methods to block spam emails, most of program developers only intend to block spam emails from being delivered to their clients. In this paper, we will introduce a new and efficient approach to prevent spam emails from being transferred. The result shows that if we focus on developing a filtering method for spams emails in the sender mail server rather than the receiver mail server, we can detect the spam emails in the shortest time consequently to avoid wasting network resources.
Evaluation of Spam Detection and Prevention Frameworks for Email and Image Sp...Pedram Hayati
In recent years, online spam has become a major problem for
the sustainability of the Internet. Excessive amounts of spam
are not only reducing the quality of information available on
the Internet but also creating concern amongst search engines
and web users. This paper aims to analyse existing works in
two different categories of spam domains - email spam and
image spam to gain a deeper understanding of this problem.
Future research directions are also presented in these spam
domains.
More info: http://debii.curtin.edu.au/~pedram/research/publications/76-evaluation-of-spam-detection-and-prevention-frameworks-for-email-and-image-spam-a-state-of-art.html
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Understand how a SPAM filter works. In this interactive webinar, we follow the path of an email from your server to the recipient's inbox and explain the end-to-end trials and tribulations of an email message as it flows from your outbox to (hopefully) the recipients inbox. This webinar is more technical than our previous email marketing webinars.-
Topics Covered:
• How current enterprise email filters work
• Tips to avoid getting accidentally blocked
• Tracking an email from send to delivery with possible pitfalls along the way
Presenters: Craig Stouffer, GM | Pinpointe and Mark Feldman, Marketing VP | NetProspex
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.
Spams are unwanted and also undesirable emails which are mass sent to the numerous victims. Further
penetration of spams into electronic processors and communication equipments such as computers and
mobiles as well as lack of control on the information shared on the internet and other communication
networks and also inefficiency of the spam detecting methods developed for Persian contexts are among the
main challenging issues of the Persian subscribers. This paper presents a novel and efficient method for
thematic identification of Persian spams. The proposed method is capable of identifying the Persian, spams
and also “Penglish” spams. “Penglish” is made up of two words Persian and English and demonstrates a
Persian text which is written by English alphabetic letters. Based on the experimental analysis of the 10000
spams of different type the efficiency of the proposed method is evaluated to be more than 98%. The
presented method is also capable of updating its databases taking the advantage of the feedbacks received
from the users.
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
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages
MINIMIZING THE TIME OF SPAM MAIL DETECTION BY RELOCATING FILTERING SYSTEM TO ...IJNSA Journal
Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to massive number of users. Spam emails consume the network resources and cause lots of security uncertainties. As we studied, the location where the spam filter operates in is an important parameter to preserve network resources. Although there are many different methods to block spam emails, most of program developers only intend to block spam emails from being delivered to their clients. In this paper, we will introduce a new and efficient approach to prevent spam emails from being transferred. The result shows that if we focus on developing a filtering method for spams emails in the sender mail server rather than the receiver mail server, we can detect the spam emails in the shortest time consequently to avoid wasting network resources.
Evaluation of Spam Detection and Prevention Frameworks for Email and Image Sp...Pedram Hayati
In recent years, online spam has become a major problem for
the sustainability of the Internet. Excessive amounts of spam
are not only reducing the quality of information available on
the Internet but also creating concern amongst search engines
and web users. This paper aims to analyse existing works in
two different categories of spam domains - email spam and
image spam to gain a deeper understanding of this problem.
Future research directions are also presented in these spam
domains.
More info: http://debii.curtin.edu.au/~pedram/research/publications/76-evaluation-of-spam-detection-and-prevention-frameworks-for-email-and-image-spam-a-state-of-art.html
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Understand how a SPAM filter works. In this interactive webinar, we follow the path of an email from your server to the recipient's inbox and explain the end-to-end trials and tribulations of an email message as it flows from your outbox to (hopefully) the recipients inbox. This webinar is more technical than our previous email marketing webinars.-
Topics Covered:
• How current enterprise email filters work
• Tips to avoid getting accidentally blocked
• Tracking an email from send to delivery with possible pitfalls along the way
Presenters: Craig Stouffer, GM | Pinpointe and Mark Feldman, Marketing VP | NetProspex
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.
Spams are unwanted and also undesirable emails which are mass sent to the numerous victims. Further
penetration of spams into electronic processors and communication equipments such as computers and
mobiles as well as lack of control on the information shared on the internet and other communication
networks and also inefficiency of the spam detecting methods developed for Persian contexts are among the
main challenging issues of the Persian subscribers. This paper presents a novel and efficient method for
thematic identification of Persian spams. The proposed method is capable of identifying the Persian, spams
and also “Penglish” spams. “Penglish” is made up of two words Persian and English and demonstrates a
Persian text which is written by English alphabetic letters. Based on the experimental analysis of the 10000
spams of different type the efficiency of the proposed method is evaluated to be more than 98%. The
presented method is also capable of updating its databases taking the advantage of the feedbacks received
from the users.
My presentation from Nordic APIs 2014 in Stockholm, Sweden.
How can the architecture of one API platform look like? How can you break down things to make this challenge easier?
Building an An AI Startup with MongoDB at x.aiMongoDB
x.ai is a personal assistant that schedules meetings for you. You speak to amy@x.ai as you would to any other person – and you can have her do all the tedious email ping pong that comes along with arranging a meeting. In this session, we'll describe where our data model began and how it evolved over time. We're solving a problem that involves multiple collections, types of documents and schemas – meetings, participants and emails, for instance – and we often have to merge them together to create different representations of the data. We'll discuss the different queries we use and how we've managed to keep our data model sane. We will also describe the high-level architecture of x.ai, including access patterns from internal Node.js apps using Mongoose and Scala modules for analytics. Finally, the core of x.ai is data science, and we'll discuss how our data science modules interact with MongoDB.
AN ANALYSIS OF EFFECTIVE ANTI SPAM PROTOCOL USING DECISION TREE CLASSIFIERSijsrd.com
As the internet usage increases in day to day activities, there is an inherent corresponding increase in usage of communication through it with email being the mainstay or rather in the forefront of modern day communication methodologies for businesses and general persons as well. This has led to get customer attention in the form of unwanted and unsolicited bombarding of the customers mail accounts with advertisements, offers, phishing activities, viruses, worms, trojans, generating hate crimes, making the customer to part with sensitive information like passwords, and other media as well which is known as spam. Spam is mass mailing or flooding of mail account servers with unwanted trash data causing damage some times. Spam filters have been in use from the time such mail flooding happens. Most of the spam filters are manual meaning which the user after identifying a mail in his account blocks the sender and henceforth the system will not allow mails to the inbox from such addresses. However the spammers are resilient and send spam mails from different identities and flood the inboxes. This study focuses on algorithms and data mining techniques used to unearth spam mails. They filter the inbox mails as they arrive at the server depending on certain rules which are already defined known as supervised learning methods. Such technologies are known as knowledge engineering techniques. Here a decision classifier is used to train such mails with varying words to filter and identify the words in the mail as spam. The Decision Tree model is used to analyze the mails and identify spam mails and block them. The number of mails sent, content, subject, type whether reply or forward, language etc. are identified using the decision classifier like Naves Bayes and analyzed accordingly to filter the emails.
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.
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
OPTIMIZING HYPERPARAMETERS FOR ENHANCED EMAIL CLASSIFICATION AND FORENSIC ANA...IJNSA Journal
Electronic mail, commonly known as email, is a crucial technology that enables streamlined operations and communications in corporate environments. Empowering swift and dependable transactions, email is a driving force behind heightened productivity and organizational effectiveness. However, its versatility also renders it susceptible to misuse by cybercriminals engaging in activities such as hacking, spoofing, phishing, email bombing, whaling, and spamming. As a result, effective and efficient data analysis is important in avoiding and detecting cyber-attacks and crime on times. To overcome the above challenges, a novel approach named Aquila Optimization (AO) is used in this paper to find the best set of hyperparameters of the Stacked Auto Encoder (SAE) classifier. The purpose of increasing the hyperparameters of the SAE using the AO is to obtain a higher text classification accuracy. Then the optimized SAE classifies the selected features into different classes. The experimental results showed that the proposed AO-SAE model outperforms the existing models such as Logistic Regression (LR) and Long Short-Term Model based Gated Current Unit (LSTM based GRU) in terms of Accuracy.
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
EMAIL SPAM CLASSIFICATION USING HYBRID APPROACH OF RBF NEURAL NETWORK AND PAR...IJNSA Journal
Email is one of the most popular communication media in the current century; it has become an effective and fast method to share and information exchangeall over the world. In recent years, emails users are facing problem which is spam emails. Spam emails are unsolicited, bulk emails are sent by spammers. It consumes storage of mail servers, waste of time and consumes network bandwidth.Many methods used for
spam filtering to classify email messages into two groups spam and non-spam. In general, one of the most powerful tools used for data classification is Artificial Neural Networks (ANNs); it has the capability of dealing a huge amount of data with high dimensionality in better accuracy. One important type of ANNs is the Radial Basis Function Neural Networks (RBFNN) that will be used in this work to classify spam message. In this paper, we present a new approach of spam filtering technique which combines RBFNN and Particles Swarm Optimization (PSO) algorithm (HC-RBFPSO). The proposed approach uses PSO
algorithm to optimize the RBFNN parameters, depending on the evolutionary heuristic search process of PSO. PSO use to optimize the best position of the RBFNN centers c. The Radii r optimize using K-Nearest Neighbors algorithmand the weights w optimize using Singular Value Decomposition algorithm within
each iterative process of PSO depending the fitness (error) function. The experiments are conducted on spam dataset namely SPAMBASE downloaded from UCI Machine Learning Repository. The experimental results show that our approach is performed in accuracy compared with other approaches that use the same dataset.
EMAIL SPAM CLASSIFICATION USING HYBRID APPROACH OF RBF NEURAL NETWORK AND PAR...IJNSA Journal
Email is one of the most popular communication media in the current century; it has become an effective
and fast method to share and information exchangeall over the world. In recent years, emails users are
facing problem which is spam emails. Spam emails are unsolicited, bulk emails are sent by spammers. It
consumes storage of mail servers, waste of time and consumes network bandwidth.Many methods used for
spam filtering to classify email messages into two groups spam and non-spam. In general, one of the most
powerful tools used for data classification is Artificial Neural Networks (ANNs); it has the capability of
dealing a huge amount of data with high dimensionality in better accuracy. One important type of ANNs is
the Radial Basis Function Neural Networks (RBFNN) that will be used in this work to classify spam
message. In this paper, we present a new approach of spam filtering technique which combinesRBFNN and
Particles Swarm Optimization (PSO) algorithm (HC-RBFPSO). The proposed approach uses PSO
algorithm to optimize the RBFNN parameters, depending on the evolutionary heuristic search process of
PSO. PSO use to optimize the best position of the RBFNN centers c. The Radii r optimize using K-Nearest
Neighbors algorithmand the weights w optimize using Singular Value Decomposition algorithm within
each iterative process of PSO depending the fitness (error) function. The experiments are conducted on
spam dataset namely SPAMBASE downloaded from UCI Machine Learning Repository. The experimental
results show that our approach is performed in accuracy compared with other approaches that use the
same dataset.
EMAIL SPAM CLASSIFICATION USING HYBRID APPROACH OF RBF NEURAL NETWORK AND PAR...IJNSA Journal
Email is one of the most popular communication media in the current century; it has become an effective and fast method to share and information exchangeall over the world. In recent years, emails users are facing problem which is spam emails. Spam emails are unsolicited, bulk emails are sent by spammers. It consumes storage of mail servers, waste of time and consumes network bandwidth.Many methods used for spam filtering to classify email messages into two groups spam and non-spam. In general, one of the most powerful tools used for data classification is Artificial Neural Networks (ANNs); it has the capability of dealing a huge amount of data with high dimensionality in better accuracy. One important type of ANNs is the Radial Basis Function Neural Networks (RBFNN) that will be used in this work to classify spam message. In this paper, we present a new approach of spam filtering technique which combinesRBFNN and Particles Swarm Optimization (PSO) algorithm (HC-RBFPSO). The proposed approach uses PSO algorithm to optimize the RBFNN parameters, depending on the evolutionary heuristic search process of PSO. PSO use to optimize the best position of the RBFNN centers c. The Radii r optimize using K-Nearest Neighbors algorithmand the weights w optimize using Singular Value Decomposition algorithm within each iterative process of PSO depending the fitness (error) function. The experiments are conducted on spam dataset namely SPAMBASE downloaded from UCI Machine Learning Repository. The experimental results show that our approach is performed in accuracy compared with other approaches that use the same dataset.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. 194 Computer Science & Information Technology (CS & IT)
spam filters are: GFI Anti Spam Filter,M-Switch Anti-Spam, Astaro Security Gateway, Hexamail
Guard, Symantec AntiSpam for SMTP, Accessio Server, SpamSentinel for Domino Server and
Kaspersky Anti-Spam Enterprise Edition by Alligate. In spite of active countermeasures
spamming is thriving. In November 2013, the percentage of spam email was 72.5% out of all
email traffic [1]. This calls for continuous efforts to discourage the spammers.
In this paper, we propose a multi-level architecture which will combine the existing spam filters
in different layers. This architecture could be used as a generic framework for spam email
detection. Existing techniques employed at each layer are also described. Our main contribution
can be summarized as,
1. A multi-layer architecture using the present day state of art spam detection technologies.
2. Analysis to prove the advantages of our novel method of having two thresholds over the
traditional single threshold based classification, in improving the accuracy and reducing
the false positives while keeping the computational load for filtering low when using each
of the filtering features.
The rest of the paper is organized as follows: Section 2 describes related works regarding multi-
level spam detection approaches, Section 3 describes the proposed architecture, Section 4
presents the performance evaluation measurements of spam detection and we conclude in Section
5.
2. RELATED WORKS
Many researchers have already given efforts to fight with the spammers. Some of the works are
related to our proposed multi-level architecture for spam detection. However, our proposed model
is different from these salient works. Jianying et al. [6] describe a multi layer framework for spam
detection. They divide the spam detection techniques between server and client side deployments.
Our proposed model does not differentiate between server and client. It can be equally applied to
both server and client side anti-spam countermeasures. Rafiul et al. [7] proposes a multi-tier
classification for phishing email. The classification result in the first tier is given to a second tier
classifier. If the classification of the second tier and first tier matches, then the result is considered
as the right output. But if the results differ, then a third tier classifier is used to classify the email.
The output of the third classifier is considered the correct classification of the email. Thus, best of
three classifiers are used to get the classification of an email. But in our proposed model, if any
layer can classify an email with the confidence above the threshold, then the lower level is not
invoked. And our proposed model can handle more than three levels of classifier to reduce the
false positive rate as much as possible. In [8], Xiao et al. proposed a hierarchical framework for
spam email detection. The first layer in their framework is a text classifier. But in our case, we
have considered other behavioural features of spam email like blacklisting sender, sender
reputation etc. Again, we have included negative selection based detection in the last layer which
will be more effective against new spam emails. Zhe et al. [9] presents an approach targeting
mainly at image spam email. The architecture presented by them is two layered. The first layer
classifies non image spam and the second layer classifies image spam. The second layer involves
multiple spam filters which will take longer time for training the detectors. Our proposed multi-
level architecture is presented in Figure 1. The purpose of this model is to put the existing
techniques in an organized way so that the detection of the spam would be faster and the false
positive rate would be lower. If the detection of the spam could be possible in the upper layer,
then the lower layer would not be invoked, and thus it will reduce the computational load. And in
each layer, we can increase the threshold value so that the rate of the false positive would be
minimum. As a result the overall performance of the spam filter detection process would be
better.
3. Computer Science & Information Technology (CS & IT) 195
This model comprises of the following layers: 1) Blacklist/Whitelist layer, 2) Content based filter,
3) Image based filter, 4) Negative Selection of Unknown Spam Email, and 5) Recipient Decision.
The description of each layer is given below.
3. MULTI-LAYERED ARCHITECTURE
Figure 1. Multi-Layer spam detection Architecture
Our proposed multi-level architecture is presented in Figure 1. This model organizes the existing
techniques for better detection of spam with lower false positive rate. If the detection of the spam
could be possible in the upper layer, then the lower layer would not be invoked, and hence
reduces computational expenditure. And in each layer, we can increase the threshold value so that
the rate of the false positive would be minimum. As a result the overall performance of the spam
filter detection process would be better. This model comprises of the following layers: 10 Black
list/White list Layer, 20. Content based Filter, 3). Image Based Filter, 4). Negative Selection of
Spam email and 5). Recipient decision. The description of each layer is given below.
3.1. Blacklist / Whitelist
Blacklist and Whitelist filters can classify the emails without reading the messages. Based on the
senders reputations, blacklist and whitelist are prepared. Blacklisted senders' emails are classified
as spam whereas whitelisted senders' emails are classified as ham emails. Any user can add email
sender's email address( and in advanced cases IP addresses) in the blacklist or whitelist. The
advantage of this approach is that the classification is very fast as it does not require to go through
the messages. However, one disadvantage of this approach is that it requires the blacklist/whitelist
to be updated regularly. Otherwise the false positive rate would be higher.
Duncan et al. [10] proposes a mechanism by which the blacklist will be updated dynamically. By
checking the log files of a particular IP address and then other suspicious activities of the sender,
the sender’s IP address is added in the blacklist. Anirudh et al. [11] proposes a technique with
which the blacklist is updated based on how the sender is sending email, rather than not relying
4. 196 Computer Science & Information Technology (CS & IT)
on IP address, the sending pattern is observed and the IP address of the sender is added in the
blacklist.
3.2. Content Based Filter
Content based filter requires the whole message to be read before taking the decision. As a result
it will take computationally more time than the Black list /White List layer. Machine learning and
fingerprint based filters are popular among the content based filters.
Fingerprint filters generate unique fingerprints for known spam messages and store in a database.
It compares the fingerprint of the incoming email to that of stored spam messages. If a match is
found for the spam messages' fingerprint, the email is classified as a spam. However the matching
should be above a certain threshold level. Damiani et al. [12] proposes a robust way to generate
the fingerprint of an email.
There are several methodologies in machine learning techniques. Statistical and artificial immune
systems are notable among them. Support Vector Machine (SVM) is a famous statistical tool. In
SVM, each email is considered as an n-dimensional vector. Each dimension could be the
frequency of a certain word. Harris et al. [13] has compared among different algorithms for
statistical filtering. The results of their experiments proves that SVM is better than Ripper,
Rocchio, and Boosting Decision Trees algorithm. Mehran et al. [14] presented bayesian
classification for spam email where some domain specific features like the domain of the
sender(.edu or .gov), the time of sending the email, whether the email contains attachment are
taken into consideration. Their experiments show that bayesian classifier works better when the
classification is done with additional domain specific features along with the contents of the
message. Dat et al. [15] have proposed an approach to detect misspelled spam words in spam
email. For example, the word 'viagra' is commonly used in spam emails. So, this word is a
blacklisted word. To defeat the spam filter, spammer can use the word 'viaaagra'. The possibility
theory will calculate all the possibilities of misspelling of spam keyword and thus can classify the
email accurately. Clotilde et al. [16] presents a symbiotic filtering approach where trained filters
are exchanged among the users. As it exchange filters, not emails, so the communication and
computational cost is minimum in this approach while it achieves better performance.
Artificial Immune System uses machine learning methods inspired by the human immune
systems for fighting the spam. Human immune system distinguishes between self and non-self,
and artificial immune system distinguishes between a self of legitimate email and a non-self of
spam email. The heart of artificial immune system is detectors which are randomly generated
from a set of gene library. Oda et al. [17] proposes the approach of artificial immune system in
spam detection successfully.
3.3. Image Based Filter
As text based filters can classify emails successfully, spammers are taking resort to image spam
emails in order to defeat the existing text based filters. Initially, the optical character
recognition(OCR) was being used to detect the text embedded in the image. However, spammers
use randomization in creating image spam to defeat OCR. For example, spammers introduce
additional dots, frames, bars in the image. They can change the font type of the text included in
the image. Zhe et al. [9] proposes a technique which is effective in image spam detection. They
have involved three different types of image filters: Color Histogram Filter, Haar Wavelet Filter
and Orientation Histogram Feature. Each of the filters works better in different types of
randomization detection. After combining the output from three different filters, decision is taken
to classify the email. Their experiments have shown less than 0.001% false positive rate in image
5. Computer Science & Information Technology (CS & IT) 197
spam detection. Uemera et al. [18] proposes an image filtering technique based on Bayesian filter.
The filter will consider the image file size, file name, compressibility technique and area of the
image to classify the spam image email. Their experiment also exhibited low false positive rate.
3.4. Negative Selection of Unknown Spam Email
Negative selection is a part of Artificial Immune System. Dat et al. [19] proposed a novel
technique for spam email detection based on negative selection. The difference between this filter
and others is that negative selection does not require any prior knowledge of spam emails. It does
not require any prior training. As a result, this filter can be used readily. And as such the unknown
spam emails could be classified by the technique proposed by Dat.
3.5. Recipient Decision
If the above layers cannot classify the incoming email either as ham or spam with confidence
level above the required threshold, then the email could be tagged with a probability number of
being a ham or spam, but not classified and let into the inbox. The user can decide whether the
email should be forwarded to spam or inbox folder. Based on the decision of the user, the filters
of the upper layer can learn and use the knowledge for future classification of this sort of email.
Elena et al. [20] proposed a spam filtering technique based on the reputation of the reporters.
Whenever any of the users report any email as spam, the system maintains the trustworthiness of
the reporter and use the feedback to classify emails.
4. PERFORMANCE ANALYSIS
In spam email detection, if a spam is detected correctly, it is called true positive (TP), if a
legitimate email is classified correctly, it is called true negative (TN). Similarly, the
misclassification of legitimate email into spam email is called false positive (FP) and the
misclassification of spam email as a legitimate email is called false negative (FN). The goal of the
spam detection is to classify as many as possible emails correctly and at the same time to reduce
the false positive rate. Because if any legitimate email is classified as spam and the user overlooks
that email, he/she might miss valuable information. As a result the cost of a false positive
classification is very high. Description of various parameters [21] for spam detection are given
below:
a) Recall = TP/ (TP + FN). It explains how good a test is at detecting the positives. i.e. predicting
positive observations as positive. A high recall is desired for a good model. Recall is also known
as sensitivity or TP Rate.
b) FP Rate = FP/ (FP+ TN). It explains how good a model is at detecting the negatives. A low
value is desirable.
c) Precision = TP/ (TP + FP). It determines how many of the positively classified are relevant. It
is the percentage of positive classifications being correct. A high precision is desirable.
d) Accuracy = TP + TN/ (TP+TN+FP+FN). It tells how well a binary classification test correctly
i.e. what percentage of predictions that are correct. Accuracy alone is not a good indicator, as it
does not tell how well the model is in detecting positives or negatives separately.
In our proposed model the classification of the emails would be done as follows:
6. 198 Computer Science & Information Technology (CS & IT)
Each filter calculates the correlation factor of the incoming mail based on the known
characteristics of the spam based on the features of the filter. Then the correlation factor is
compared to two threshold values, which are calibrated to decide if the email is a spam, a ham or
not decidable with the information at hand. If the email is classified into the third category, it is
sent to another more rigorous and computationally expensive filtering layer. This architecture
leads us to have a classification which is having an acceptable accuracy and an acceptable false-
positive rate, while considering each of the features when situation requires. As we show in the
analysis below with the given technologies at hand this architecture provides an improvement
over using the filters individually as some of the features considered by one filter will not be
considered by the other filter.
Let A= Accuracy, T = Percentage of Correctly Classified emails, F= Percentage of Incorrectly
classified emails = False positives + False Negatives. Then by definition we have, A =
1/(1+(F/T)). The effectiveness of the filtering depends on the nature of the incoming traffic and
the filter’s response to it. More precisely, when a filter can expect that, in the incoming set of
emails, if there are spams, and those spams have a particular feature, then it can check for high
correlation for that feature and classify successfully the spam emails and keep them out of
inboxes of the end users. The only thing that the filters can control is the threshold of correlation
factor to classify the incoming email as spam or ham. If the threshold for classification as spam is
high then many spam end up in the inbox. If it is low, then many hams may be misclassified as
spam, increasing the false positive. It is learnt from experience that false positive should be as
less as possible even if that allows some spams to enter the inbox.
Thus let us have α1, α2, α3 and α4 as the percentage of incoming emails expected to be classified as
spam emails and β1, β2, β3 and β4 be the expected to be classified as ham emails respectively at
each layer. Let and γ1, γ2, γ3 and γ4 be the percentage of false positives and δ1 , δ2, δ3 and δ4 be the
false negatives of each layer correspondingly from top layer to the bottom layer in Figure 1. Then
we get the total False Positive = α1γ1 + (1-( α1 + β1 )) α2 γ2 + (1-( α1+ α2+ β1+ β2)) α3 γ3 + (1-( α1+
α2+ α3 + β1+ β2 + β3) α4 γ4. Similarly we have the total False Negative = β1 δ1 + (1-( α1 + β1 )) β2 δ2
+ (1-( α1+ α2+ β1+ β2)) β3 δ3 + (1-( α1+ α2+ α3 + β1+ β2 + β3) β4 δ4 . On simplification we can verify
that the percentage of emails the effective false positives = (α1γ1 + α2 γ2 + α3 γ3 + α4 γ4) – ( non-
negative term) = Sum of False positives of individual filters – positive term. So, we know that
collectively the false positive and by symmetry false negative are better off than if we had
considered individual filters separately.
5. CONCLUSIONS
In this paper we have proposed a multi-layer architecture which provides a layered approach for
spam detection process using the existing techniques. As the spammers are coming up with new
ways to defeat the existing filters, continuous efforts are required to improve the filters in each
layer. Detecting spam email closer to the source will avoid wasting bandwidth and traffic
processing. With our analysis we show that our architecture yields more correct classifications for
the same given thresholds of spam without adversely affecting false positives and the threshold of
ham affecting the false negatives. Further research into the exact machine learning algorithms to
detect spam incorporating this overall architecture for the layers would lead to better preparedness
to fight the increasing spam traffic.
7. Computer Science & Information Technology (CS & IT) 199
REFERENCES
[1] http://www.securelist.com/en/analysis/204792321/Spam_in_November_2013
[2] http://www.sans.org/reading-room/whitepapers/email/is-affect-us-deal-spam-1111
[3] http://www.spamlaws.com/spam-stats.html
[4] http://www.spamexperts.com/en/news/motivation-spammers
[5] http://www.spamhelp.org/software/
[6] Jianying Zhou, Wee-Yung Chin, Rodrigo Roman, and Javier Lopez,(2007) "An Effective Multi-
Layered Defense Framework against Spam", Information Security Technical Report 01/2007.
[7] Rafiqul Islam,JemalAbawajy,"A multi-tier phishing detection and filtering approach (2013)",Journal
of Network and Computer Applications,Volume 36, Issue 1, January, pp. 324–335.
[8] Xiao Mang Li, Ung Mo Kim,(2012)"A hierarchical framework for content-based image spam
filtering", 8th International Conference on Information Science and Digital Content Technology
(ICIDT), Jeju,June , pp. 149-155.
[9] Z. Wang, W. Josephson, Q. Lv, M. Charikar and K. Li.(2007) Filtering Image Spam with near-
Duplicate Detection, in Proceedings of the 4th Conference on Email and Anti-Spam CEAS.
[10] Duncan Cook, Jacky Hartnett, Kevin Manderson and Joel Scanlan(2006),"Catching spam before it
arrives: domain specific dynamic blacklists", ACSW Frontiers '06 Proceedings of the 2006
Australasian workshops on Grid computing and e-research - Volume 54, January , pp. 193-202.
[11] Anirudh Ramachandran, Nick Feamster, and Santosh Vempala,(2007)"Filtering spam with behavioral
blacklisting", Proceedings of the 14th ACM conference on Computer and communications security
CCS'07, NY, USA, pp. 342-351.
[12] Damiani E., Vimercati S. D. C. d. et al.,(2004) "An Open Digest-based Technique for Spam
Detection", San Francisco, CA, USA, pp. 1-6.
[13] Harris Drucker,Donghui Wu, and Vladimir N. Vapnik,(1999)"Support Vector Machines for Spam
Categorization", IEEE Transactions On Neural Networks, Vol. 10, No. 5, September.
[14] Mehran Sahami, Susan Dumais, David Heckerman, and Eric Horvitz(1998),"A Bayesian Approach to
Filtering Junk E-Mail",Learning for Text Categorization: Papers from the 1998 Workshop, Madison,
Wisconsin, AAAI Technical Report WS-98-05.
[15] Dat Tran, Wanli Ma, Dharmendra Sharma, and Thien Nguyen,(2007)"Possibility Theory-Based
Approach to Spam Email Detection",IEEE International Conference on Granular Computing.
[16] Clotilde Lopes, Paulo Cortez, Pedro Sousa, Miguel Rocha, and Miguel Rio,(2011) “Symbiotic
filtering for spam email detection”, Expert Systems with Applications: An International Journal,
Volume 38 Issue 8, August, pp.9365-9372.
[17] Oda, T. and T. White.(2005) Immunity from Spam: An Analysis of an Artificial Immune System for
Junk Email Detection. in 4th International Conference on Artificial Immune Systems (ICARIS).
[18] M. Uemura and T. Tabata,(2008)"Design and Evaluation of a Bayesian-filter based Image Spam
Filtering Technique", in Proceedings of the International Conference on Information Security and
Assurance(ISA).
[19] Dat Tran, Wanli Ma, and Dharmendra Sharma,(2009) "A Novel Spam Email Detection System Based
on Negative Selection",In Proceedings of the 4th International Conference on Computer Sciences and
Convergence Information Technology (ICCIT’09). Los Alamitos, CA, 2009,pp. 987-992.
[20] Elena Zheleva, Aleksander Kolcz and Lise Getoor,(2008) "Trusting spam reporters: A reporter-based
reputation system for email filtering", ACM Transactions on Information Systems (TOIS),Volume 27
Issue 1, December.
[21] P.K Panigrahi,(2012)"A Comparative Study of Supervised Machine Learning Techniques for Spam
E-mail Filtering", Fourth International Conference on Computational Intelligence and
Communication Networks (CICN), Mathura, Nov,pp.506-512.
8. 200 Computer Science & Information Technology (CS & IT)
AUTHORS
Vivek Shandilya holds a BE in Electronics and Communication Engineering from
Bangalore university, MS in Computer Science and is a PhD candidate in Computer Science
at University of Memphis. His research areas are optimization and security of stochastic
systems.
Fahad Polash is a PhD student at Department of Computer Science at university of
Memphis. He has a bachelor’s degree in computer science and worked in telecom industry
before starting his graduate studies. His research areas are computer networking, network
security and forensics.
Sajjan Shiva is the chair and a professor at Department of Computer Science at university of
Memphis. He was formerly chair of the Department of Computer Science at University of
Alabama, Huntsville. His research areas are computer organization and architecture, parallel
processing, software engineering, security systems and cloud computing. He is a fellow of
IEEE