Unified Anti-Spam Analyser Specialized to Protect from Russian Spam
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Learning Apache Mahout Classification Table of Contents
Learning Apache Mahout Classification Table of Contents Learning Apache Mahout Classification Credits About the Author About the Reviewers www.PacktPub.com Support files, eBooks, discount offers, and more Why subscribe? Free access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Classification in Data Analysis Introducing the classification Application of the classification system Working of the classification system Classification algorithms Model evaluation techniques The confusion matrix The Receiver Operating Characteristics (ROC) graph Area under the ROC curve The entropy matrix Summary 2. Apache Mahout Introducing Apache Mahout Algorithms supported in Mahout Reasons for Mahout being a good choice for classification Installing Mahout Building Mahout from source using Maven Installing Maven Building Mahout code Setting up a development environment using Eclipse Setting up Mahout for a Windows user Summary 3. Learning Logistic Regression / SGD Using Mahout Introducing regression Understanding linear regression Cost function Gradient descent Logistic regression Stochastic Gradient Descent Using Mahout for logistic regression Summary 4. Learning the Naïve Bayes Classification Using Mahout Introducing conditional probability and the Bayes rule Understanding the Naïve Bayes algorithm Understanding the terms used in text classification Using the Naïve Bayes algorithm in Apache Mahout Summary 5. Learning the Hidden Markov Model Using Mahout Deterministic and nondeterministic patterns The Markov process Introducing the Hidden Markov Model Using Mahout for the Hidden Markov Model Summary 6. Learning Random Forest Using Mahout Decision tree Random forest Using Mahout for Random forest Steps to use the Random forest algorithm in Mahout Summary 7. -
Linux VDS Release Notes
Linux VDS Release Notes Linux VDS Release Notes Introduction Important: This document is based on the most recent available information regarding the release of Linux VDS. Future revisions of this document will include updates not only to the status of features, but also an overview of updates to customer documentation. The Linux VDS platform provides a set of features designed and implemented to suit the value-added reseller (VAR), small hosting service provider (HSP), application service provider (ASP), independent software vendor (ISV), and any small- and medium-sized business (SMB). This document provides administrators who possess minimal to moderate technical skills with a guide to the latest documentation and other sources of information available at the time of the release. This document also includes any cautions and warnings specific to this release. For more information, refer to the following documents: • Linux VDS User’s Guide • Linux VDS Getting Started Guide In addition, you can also refer to the following Web content: • Frequently-asked questions (FAQ) Status of Features Linux VDS introduces new features and enhancements. What’s New? The following table describes the status of features included with this release: Feature Status Accrisoft This release introduces support for Accrisoft e-commerce software (http://www.accrisoft.com/ ). You can use the software to build and manage profitable Web sites. Accrisoft is a fee-based, optional application. This release also provides an installation script to assure the software operates fully and correctly on your account. An uninstall script assures that you can remove the software completely and effectively if you need to do so. -
Trusting Spam Reporters: a Reporter-Based Reputation System for Email Filtering
Trusting Spam Reporters: A Reporter-based Reputation System for Email Filtering ELENA ZHELEVA University of Maryland, College Park ALEKSANDER KOLCZ Microsoft Live Labs LISE GETOOR University of Maryland, College Park Spam is a growing problem; it interferes with valid email and burdens both email users and service providers. In this work, we propose a reactive spam-filtering system based on reporter reputation for use in conjunction with existing spam-filtering techniques. The system has a trust- maintenance component for users, based on their spam-reporting behavior. The challenge that we consider is that of maintaining a reliable system, not vulnerable to malicious users, that will provide early spam-campaign detection to reduce the costs incurred by users and systems. We report on the utility of a reputation system for spam filtering that makes use of the feedback of trustworthy users. We evaluate our proposed framework, using actual complaint feedback from a large population of users, and validate its spam-filtering performance on a collection of real email traffic over several weeks. To test the broader implication of the system, we create a model of the behavior of malicious reporters, and we simulate the system under various assumptions using a synthetic dataset. Categories and Subject Descriptors: H.1 [Information Systems]: Models and Principles General Terms: Algorithms Additional Key Words and Phrases: spam filtering; reputation systems; trust. Author’s address: E. Zheleva, Computer Science Department, AV Williams Bldg., University of Maryland, College Park, MD, 20742. c ACM, 2009. This is the author’s version of the work. It is posted here by permission of ACM for your personal use. -
A Rule Based Approach for Spam Detection
A RULE BASED APPROACH FOR SPAM DETECTION Thesis submitted in partial fulfillment of the requirements for the award of degree of Master of Engineering In Computer Science & Engineering By: Ravinder Kamboj (Roll No. 800832030) Under the supervision of: Dr. V.P Singh Mrs. Sanmeet Bhatia Assistant Professor Assistant Professor Computer Science & Engineering Department of SMCA COMPUTER SCIENCE AND ENGINEERING DEPARTMENT THAPAR UNIVERSITY PATIALA – 147004 JULY- 2010 i ii Abstract Spam is defined as a junk Email or unsolicited Email. Spam has increased tremendously in the last few years. Today more than 85% of e-mails that are received by e-mail users are spam. The cost of spam can be measured in lost human time, lost server time and loss of valuable mail. Spammers use various techniques like spam via botnet, localization of spam and image spam. According to the mail delivery process anti-spam measures for Email Spam can be divided in to two parts, based on Emails envelop and Email data. Black listing, grey listing and white listing techniques can be applied on the Email envelop to detect spam. Techniques based on the data part of Email like heuristic techniques and Statistical techniques can be used to combat spam. Bayesian filters as part of statistical technique divides the income message in to words called tokens and checks their probability of occurrence in spam e-mails and ham e-mails. Two types of approaches can be followed for the detection of spam e-mails one is learning approach other is rule based approach. Learning approach required a large dataset of spam e-mails and ham e-mails is required for the training of spam filter; this approach has good time characteristics filter can be retrained quickly for new Spam. -
Baruwa Documentation Release 1.1.2
Baruwa Documentation Release 1.1.2 Andrew Colin Kissa June 27, 2016 Contents 1 Introduction 3 2 Features 5 3 Screenshots 7 4 Requirements 9 4.1 Baruwa requirements.........................................9 4.2 MailScanner requirements......................................9 5 Source Installation 11 5.1 Install Baruwa............................................ 11 5.2 Configure RabbitMQ......................................... 12 5.3 Configure Baruwa.......................................... 12 5.4 Configure MailScanner........................................ 14 5.5 Testing................................................ 15 5.6 Need help............................................... 16 5.7 Distribution / OS installation..................................... 16 6 Baruwa on RHEL/SL/Centos 17 6.1 Install EPEL............................................. 17 6.2 Baruwa installation.......................................... 17 6.3 Configure RabbitMQ......................................... 17 6.4 Configure Baruwa.......................................... 18 6.5 Configure MailScanner........................................ 19 6.6 Testing................................................ 20 6.7 Need help............................................... 20 7 Baruwa on Fedora 21 7.1 Baruwa Installation.......................................... 21 7.2 Configure RabbitMQ......................................... 21 7.3 Configure Baruwa.......................................... 22 7.4 Configure MailScanner........................................ 23 7.5 Testing............................................... -
Return of Organization Exempt from Income
OMB No. 1545-0047 Return of Organization Exempt From Income Tax Form 990 Under section 501(c), 527, or 4947(a)(1) of the Internal Revenue Code (except black lung benefit trust or private foundation) Open to Public Department of the Treasury Internal Revenue Service The organization may have to use a copy of this return to satisfy state reporting requirements. Inspection A For the 2011 calendar year, or tax year beginning 5/1/2011 , and ending 4/30/2012 B Check if applicable: C Name of organization The Apache Software Foundation D Employer identification number Address change Doing Business As 47-0825376 Name change Number and street (or P.O. box if mail is not delivered to street address) Room/suite E Telephone number Initial return 1901 Munsey Drive (909) 374-9776 Terminated City or town, state or country, and ZIP + 4 Amended return Forest Hill MD 21050-2747 G Gross receipts $ 554,439 Application pending F Name and address of principal officer: H(a) Is this a group return for affiliates? Yes X No Jim Jagielski 1901 Munsey Drive, Forest Hill, MD 21050-2747 H(b) Are all affiliates included? Yes No I Tax-exempt status: X 501(c)(3) 501(c) ( ) (insert no.) 4947(a)(1) or 527 If "No," attach a list. (see instructions) J Website: http://www.apache.org/ H(c) Group exemption number K Form of organization: X Corporation Trust Association Other L Year of formation: 1999 M State of legal domicile: MD Part I Summary 1 Briefly describe the organization's mission or most significant activities: to provide open source software to the public that we sponsor free of charge 2 Check this box if the organization discontinued its operations or disposed of more than 25% of its net assets. -
Combatting Spam Using Mimedefang, Spamassassin and Perl
Combating Spam Using SpamAssassin, MIMEDefang and Perl Copyright 2003 David F. Skoll Roaring Penguin Software Inc. (Booth #23) Administrivia Please turn off or silence cell phones, pagers, Blackberry devices, etc... After the tutorial, please be sure to fill out an evaluation form and return it to the USENIX folks. 2 Overview After this tutorial, you will: Understand how central mail filtering works. Know how to use MIMEDefang to filter mail. Be able to integrate SpamAssassin into your mail filter. Know how to implement mail filtering policies with MIMEDefang and Perl. Know how to fight common spammer tactics. 3 Outline Introduction to Mail Filtering Sendmail's Milter API MIMEDefang Introduction, Architecture Writing MIMEDefang Filters SpamAssassin Integration Advanced Filter Writing Fighting Common Spammer Tactics Advanced Topics Policy Suggestions 4 Assumptions I assume that you: Are familiar with Sendmail configuration. You don't need to be a sendmail.cf guru, but should know the basics. Are familiar with Perl. Again, you don't need to be able to write an AI program in a Perl one- liner, but should be able to read simple Perl scripts. Are running the latest version of Sendmail 8.12 on a modern UNIX or UNIX-like system. 5 Why Filter Mail? The old reason: to stop viruses. The new reason: to stop spam and inappropriate content. Blocking viruses is easy. Block .exe and similar files, and test against signature databases. Blocking spam is hard, but becoming increasingly important. Organizations can even face lawsuits over inappropriate content. 6 Mail filtering is required for many reasons. In addition to the reasons given on the slide, you might need to filter outgoing mail as well to prevent virus propagation, dissemination of sensitive information, etc. -
Keep Receiving Scam Calls
Keep Receiving Scam Calls Osseous Fritz lauds endways while Buddy always dishonors his hamlets incardinate everywhen, he invading so shoddily. Kurtis receding bearably as evangelistic Jefferson sniff her canfield confesses gratingly. Lynn still nurture privatively while monachal Barnard outjockey that dandlers. You from still receive calls from entities you also given permission to contact you. If the scammers call you hop UP Do you talk too them whom are very plain at keeping people went the terrible and isolating the victim will not engage them shed a. Commission can give Americans a way of avoid getting telemarketing calls at home Adding. Protecting against fraudulent calls Google My objective Help. How rank I Stop Fake and Spam Calls at My CallSource. Social Security Administration scams There's a blatant way to. Or any email address and others like a wide range of keeping your knees give way in the federal rules for misuse of the app. Do receive several extensions, keep your phone. Don't Call Back FCC Warns of West African 'One satellite' Phone Scam North Texans report receiving a snort of one-ring calls from 232 country. Not Call Registry to both getting spam calls from reputable marketers. Scam phone calls are not cite new crusade but scammers are getting bolder and more. Malicious calls The caller makes threats or talks maliciously and calls repeatedly Scam calls Scams often sound like walking're getting her special offer before the screw is. Does * 67 still work? With scammers getting smarter you pet to fix how is stop these invasive annoying calls There are these few steps you hardly take leave your own. -
Synology Mailplus Server Administrator's Guide Based on Mailplus Server 1.4.0 Table of Contents
Synology MailPlus Server Administrator's Guide Based on MailPlus Server 1.4.0 Table of Contents Chapter 1: Introduction Chapter 2: Getting Started with MailPlus Server Connect Synology NAS to the Internet 5 Set up DNS 5 Set up MailPlus Server 7 Set up MailPlus Email Client 10 Third Party Email Clients 13 Troubleshoot 16 Chapter 3: Mail Migration Create a mail migration task on MailPlus Server 18 How to import system configurations from Microsoft Exchange to MailPlus Server 25 Chapter 4: User Licenses Purchase Licenses 27 Install Licenses 27 How to Use Licenses 30 Chapter 5: Account Settings Account System 31 Activate Accounts 32 Manage Privileges 34 Chapter 6: Protocol Settings SMTP 35 IMAP/POP3 36 Network Interface 37 Chapter 7: SMTP Settings Service Settings 38 SMTP Secure Connection 41 Mail Relay 47 Mail Management 48 Personal Settings 61 Chapter 8: Security Settings Spam 64 Antivirus Scan 73 Authentication 77 Content Protection 79 2 Chapter 9: Monitor Settings Monitor Server Status 84 Monitor Mail Queue 88 Monitor Mail Log 89 Chapter 10: Disaster Recovery High-availability Cluster 97 Backup and Restore Mail 103 Syno_UsersGuide_MailPlus_Server_20171114 3 Chapter Introduction 1 The Synology MailPlus suite is an advanced, secured email service with high usability. This suite includes two packages: MailPlus Server and MailPlus. MailPlus Server provides many management details and settings, while MailPlus provides client management and email services. This administrator's guide will guide you through setting up MailPlus Server and provide more detailed configuration instructions including DNS settings, mail service migration, and other security adjustments. MailPlus High-availability will help you achieve continuous email services, the mail queue feature provides management options for deferred messages, and the status monitoring feature provides you an overview of MailPlus health status. -
Trustwave SEG Evaluation Guide
EVALUATION GUIDE Trustwave MailMarshal Secure Email Gateway March 2020 Trustwave Secure Email Gateway Evaluation Guide - March 25, 2020 Legal Notice Copyright © 2020 Trustwave Holdings, Inc. All rights reserved. This document is protected by copyright and any distribution, reproduction, copying, or decompilation is strictly prohibited without the prior written consent of Trustwave. No part of this document may be reproduced in any form or by any means without the prior written authorization of Trustwave. While every precaution has been taken in the preparation of this document, Trustwave assumes no responsibility for errors or omissions. This publication and features described herein are subject to change without notice. While the authors have used their best efforts in preparing this document, they make no representation or warranties with respect to the accuracy or completeness of the contents of this document and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the author nor Trustwave shall be liable for any loss of profit or any commercial damages, including but not limited to direct, indirect, special, incidental, consequential, or other damages. The most current version of this document may be obtained from: www.trustwave.com/support/ Trademarks Trustwave and the Trustwave logo are trademarks of Trustwave. Such trademarks shall not be used, copied, or disseminated in any manner without the prior written permission of Trustwave. Legal Notice Copyright © 2020 Trustwave Holdings, Inc. -
Macsysadmin 2009 Presentatio
Slide 1 SpamAssassin Way more than the Mac OS X Server GUI shows Presented by: Kevin A. McGrail Project Management Committee Member of the Apache Software Foundation SpamAssassin Project & President, PCCC September 16, 2009 Good Afternoon, My name is Kevin A. McGrail. If you read my biographyi for this conference, you’ll know already that I hate Spam and enjoy greatly fighting spammers. You’ll also know that I love all types of computers and use a wide variety of machines & operating systems. But I’m definitely old-school in my love for the command line interface. This doesn’t mean I don’t think that Apple’s OS X is the most beautiful pairing of a rock-solid CLI with a beautifully polished GUI. But it does mean that while we are here to talk about Mac system administration, the configuration of SpamAssassin is largely not server specific and most of the heavy- handed configuration changes will be done behind the scenes using the CLI. So let’s get started by talking about the definition of Spam. Page 1 of 67 Slide 2 What is Spam? •Spam is NOT about content, its about CONSENT. – Consent: to give assent or approval : agree <consent to being tested> Merriam‐Webster Dictionary •What is SPAM vs. spam? September 16, 2009 Chris Santerre gave the best definition of Spam I’ve ever seen. He based the definition of Spam on CONSENT not content because consent is when you give approval to someone to send you e-mails. Many people try and use various legal definitions such as CAN-SPAM in the US. -
Mastering Spam a Multifaceted Approach with the Spamato Spam Filter System
DISS. ETH NO. 16839 Mastering Spam A Multifaceted Approach with the Spamato Spam Filter System A dissertation submitted to the SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH for the degree of Doctor of Sciences presented by KENO ALBRECHT Dipl. Inf. born June 4, 1977 citizen of Germany accepted on the recommendation of Prof. Dr. Roger Wattenhofer, examiner Prof. Dr. Gordon V. Cormack, co-examiner Prof. Dr. Christof Fetzer, co-examiner 2006 Abstract Email is undoubtedly one of the most important applications used to com- municate over the Internet. Unfortunately, the email service lacks a crucial security mechanism: It is possible to send emails to arbitrary people with- out revealing one’s own identity. Additionally, sending millions of emails costs virtually nothing. Hence over the past years, these characteristics have facilitated and even boosted the formation of a new business branch that advertises products and services via unsolicited bulk emails, better known as spam. Nowadays, spam makes up more than 50% of all emails and thus has become a major vexation of the Internet experience. Although this problem has been dealt with for a long time, only little success (measured on a global scale) has been achieved so far. Fighting spam is a cat and mouse game where spammers and anti-spammers regularly beat each other with sophisticated techniques of increasing complexity. While spammers try to bypass existing spam filters, anti-spammers seek to detect and block new spamming tricks as soon as they emerge. In this dissertation, we describe the Spamato spam filter system as a mul- tifaceted approach to help regain a spam-free inbox.