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Statistical Structures: Fingerprinting Malware for Classification and Analysis
Statistical Structures: Fingerprinting Malware for Classification and Analysis Daniel Bilar Wellesley College (Wellesley, MA) Colby College (Waterville, ME) bilar <at> alum dot dartmouth dot org Why Structural Fingerprinting? Goal: Identifying and classifying malware Problem: For any single fingerprint, balance between over-fitting (type II error) and under- fitting (type I error) hard to achieve Approach: View binaries simultaneously from different structural perspectives and perform statistical analysis on these ‘structural fingerprints’ Different Perspectives Idea: Multiple perspectives may increase likelihood of correct identification and classification Structural Description Statistical static / Perspective Fingerprint dynamic? Assembly Count different Opcode Primarily instruction instructions frequency static distribution Win 32 API Observe API calls API call vector Primarily call made dynamic System Explore graph- Graph structural Primarily Dependence modeled control and properties static Graph data dependencies Fingerprint: Opcode frequency distribution Synopsis: Statically disassemble the binary, tabulate the opcode frequencies and construct a statistical fingerprint with a subset of said opcodes. Goal: Compare opcode fingerprint across non- malicious software and malware classes for quick identification and classification purposes. Main result: ‘Rare’ opcodes explain more data variation then common ones Goodware: Opcode Distribution 1, 2 ---------.exe Procedure: -------.exe 1. Inventoried PEs (EXE, DLL, ---------.exe etc) on XP box with Advanced Disk Catalog 2. Chose random EXE samples size: 122880 with MS Excel and Index totalopcodes: 10680 3, 4 your Files compiler: MS Visual C++ 6.0 3. Ran IDA with modified class: utility (process) InstructionCounter plugin on sample PEs 0001. 002145 20.08% mov 4. Augmented IDA output files 0002. 001859 17.41% push with PEID results (compiler) 0003. 000760 7.12% call and general ‘functionality 0004. -
Trendmicro™ Hosted Email Security
TrendMicro™ Hosted Email Security Best Practice Guide Trend Micro Incorporated reserves the right to make changes to this document and to the products described herein without notice. The names of companies, products, people, characters, and/or data mentioned herein are fictitious and are in no way intended to represent any real individual, company, product, or event, unless otherwise noted. Complying with all applicable copyright laws is the responsibility of the user. Copyright © 2016 Trend Micro Incorporated. All rights reserved. Trend Micro, the Trend Micro t-ball logo, and TrendLabs are trademarks or registered trademarks of Trend Micro, Incorporated. All other brand and product names may be trademarks or registered trademarks of their respective companies or organizations. No part of this publication may be reproduced, photocopied, stored in a retrieval system, or transmitted without the express prior written consent of Trend Micro Incorporated. Authors : Michael Mortiz, Jefferson Gonzaga Editorial : Jason Zhang Released : June 2016 Table of Contents 1 Best Practice Configurations ................................................................................................................................. 8 1.1 Activating a domain ....................................................................................................................................... 8 1.2 Adding Approved/Blocked Sender ................................................................................................................ 8 1.3 HES order -
A the Hacker
A The Hacker Madame Curie once said “En science, nous devons nous int´eresser aux choses, non aux personnes [In science, we should be interested in things, not in people].” Things, however, have since changed, and today we have to be interested not just in the facts of computer security and crime, but in the people who perpetrate these acts. Hence this discussion of hackers. Over the centuries, the term “hacker” has referred to various activities. We are familiar with usages such as “a carpenter hacking wood with an ax” and “a butcher hacking meat with a cleaver,” but it seems that the modern, computer-related form of this term originated in the many pranks and practi- cal jokes perpetrated by students at MIT in the 1960s. As an example of the many meanings assigned to this term, see [Schneier 04] which, among much other information, explains why Galileo was a hacker but Aristotle wasn’t. A hack is a person lacking talent or ability, as in a “hack writer.” Hack as a verb is used in contexts such as “hack the media,” “hack your brain,” and “hack your reputation.” Recently, it has also come to mean either a kludge, or the opposite of a kludge, as in a clever or elegant solution to a difficult problem. A hack also means a simple but often inelegant solution or technique. The following tentative definitions are quoted from the jargon file ([jargon 04], edited by Eric S. Raymond): 1. A person who enjoys exploring the details of programmable systems and how to stretch their capabilities, as opposed to most users, who prefer to learn only the minimum necessary. -
Undergraduate Report
UNDERGRADUATE REPORT Attack Evolution: Identifying Attack Evolution Characteristics to Predict Future Attacks by MaryTheresa Monahan-Pendergast Advisor: UG 2006-6 IINSTITUTE FOR SYSTEMSR RESEARCH ISR develops, applies and teaches advanced methodologies of design and analysis to solve complex, hierarchical, heterogeneous and dynamic problems of engineering technology and systems for industry and government. ISR is a permanent institute of the University of Maryland, within the Glenn L. Martin Institute of Technol- ogy/A. James Clark School of Engineering. It is a National Science Foundation Engineering Research Center. Web site http://www.isr.umd.edu Attack Evolution 1 Attack Evolution: Identifying Attack Evolution Characteristics To Predict Future Attacks MaryTheresa Monahan-Pendergast Dr. Michel Cukier Dr. Linda C. Schmidt Dr. Paige Smith Institute of Systems Research University of Maryland Attack Evolution 2 ABSTRACT Several approaches can be considered to predict the evolution of computer security attacks, such as statistical approaches and “Red Teams.” This research proposes a third and completely novel approach for predicting the evolution of an attack threat. Our goal is to move from the destructive nature and malicious intent associated with an attack to the root of what an attack creation is: having successfully solved a complex problem. By approaching attacks from the perspective of the creator, we will chart the way in which attacks are developed over time and attempt to extract evolutionary patterns. These patterns will eventually -
CONTENTS in THIS ISSUE Fighting Malware and Spam
MARCH 2008 Fighting malware and spam CONTENTS IN THIS ISSUE 2 COMMENT EVASIVE ACTION Home (page) renovations Pandex has attracted very little attention from the media and generated little 3 NEWS discussion between malware Botherders herded researchers and among the 29A folds general populace. Chandra Prakash and Adam Thomas provide an overview of the Pandex operation and take an in-depth look at VIRUS PREVALENCE TABLE 3 the underlying code that has allowed this malware to evade detection for so long. 4 MALWARE ANALYSIS page 4 Pandex: the botnet that could PACKING A PUNCH In the fi nal part of the series on exepacker 9 FEATURE blacklisting, Robert Neumann takes a look at how all the processing and analysis techniques are put Exepacker blacklisting part 3 into practice in a real-life situation. page 9 15 CONFERENCE REPORT AVG TURNS 8 Black Hat DC and CCC 24C3 John Hawes gets his hands on a preview version of the latest offering from AVG. 18 PRODUCT REVIEW page 18 AVG Internet Security 8 22 END NOTES & NEWS This month: anti-spam news and events, and Ken Simpson considers the implications of rising spam volume despite increasing accuracy of content fi lters. ISSN 1749-7027 COMMENT ‘It is hoped that within all sizes of business. It is hoped that the comment facility will promote discussion among visitors and that the comment facility in some cases the more knowledgeable of VB’s readers will promote will be able to guide and assist those less well versed in discussion among the complexities of anti-malware technologies. -
Common Threats to Cyber Security Part 1 of 2
Common Threats to Cyber Security Part 1 of 2 Table of Contents Malware .......................................................................................................................................... 2 Viruses ............................................................................................................................................. 3 Worms ............................................................................................................................................. 4 Downloaders ................................................................................................................................... 6 Attack Scripts .................................................................................................................................. 8 Botnet ........................................................................................................................................... 10 IRCBotnet Example ....................................................................................................................... 12 Trojans (Backdoor) ........................................................................................................................ 14 Denial of Service ........................................................................................................................... 18 Rootkits ......................................................................................................................................... 20 Notices ......................................................................................................................................... -
Emerging Threats and Attack Trends
Emerging Threats and Attack Trends Paul Oxman Cisco Security Research and Operations PSIRT_2009 © 2009 Cisco Systems, Inc. All rights reserved. Cisco Public 1 Agenda What? Where? Why? Trends 2008/2009 - Year in Review Case Studies Threats on the Horizon Threat Containment PSIRT_2009 © 2009 Cisco Systems, Inc. All rights reserved. Cisco Public 2 What? Where? Why? PSIRT_2009 © 2009 Cisco Systems, Inc. All rights reserved. Cisco Public 3 What? Where? Why? What is a Threat? A warning sign of possible trouble Where are Threats? Everywhere you can, and more importantly cannot, think of Why are there Threats? The almighty dollar (or euro, etc.), the underground cyber crime industry is growing with each year PSIRT_2009 © 2009 Cisco Systems, Inc. All rights reserved. Cisco Public 4 Examples of Threats Targeted Hacking Vulnerability Exploitation Malware Outbreaks Economic Espionage Intellectual Property Theft or Loss Network Access Abuse Theft of IT Resources PSIRT_2009 © 2009 Cisco Systems, Inc. All rights reserved. Cisco Public 5 Areas of Opportunity Users Applications Network Services Operating Systems PSIRT_2009 © 2009 Cisco Systems, Inc. All rights reserved. Cisco Public 6 Why? Fame Not so much anymore (more on this with Trends) Money The root of all evil… (more on this with the Year in Review) War A battlefront just as real as the air, land, and sea PSIRT_2009 © 2009 Cisco Systems, Inc. All rights reserved. Cisco Public 7 Operational Evolution of Threats Emerging Threat Nuisance Threat Threat Evolution Unresolved Threat Policy and Process Reactive Process Socialized Process Formalized Process Definition Reaction Mitigation Technology Manual Process Human “In the Automated Loop” Response Evolution Burden Operational End-User “Help-Desk” Aware—Know End-User No End-User Increasingly Self- Awareness Knowledge Enough to Call Burden Reliant Support PSIRT_2009 © 2009 Cisco Systems, Inc. -
A Survey of Learning-Based Techniques of Email Spam Filtering
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Unitn-eprints Research A SURVEY OF LEARNING-BASED TECHNIQUES OF EMAIL SPAM FILTERING Enrico Blanzieri and Anton Bryl January 2008 (Updated version) Technical Report # DIT-06-056 A Survey of Learning-Based Techniques of Email Spam Filtering Enrico Blanzieri, University of Trento, Italy, and Anton Bryl University of Trento, Italy, Create-Net, Italy [email protected] January 11, 2008 Abstract vertising pornography, pyramid schemes, etc. [68]. The total worldwide financial losses caused by spam Email spam is one of the major problems of the to- in 2005 were estimated by Ferris Research Analyzer day’s Internet, bringing financial damage to compa- Information Service at $50 billion [31]. nies and annoying individual users. Among the ap- Lately, Goodman et al. [39] presented an overview proaches developed to stop spam, filtering is an im- of the field of anti-spam protection, giving a brief portant and popular one. In this paper we give an history of spam and anti-spam and describing major overview of the state of the art of machine learn- directions of development. They are quite optimistic ing applications for spam filtering, and of the ways in their conclusions, indicating learning-based spam of evaluation and comparison of different filtering recognition, together with anti-spoofing technologies methods. We also provide a brief description of and economic approaches, as one of the measures other branches of anti-spam protection and discuss which together will probably lead to the final victory the use of various approaches in commercial and non- over email spammers in the near future. -
Detecting Phishing in Emails Srikanth Palla and Ram Dantu, University of North Texas, Denton, TX
1 Detecting Phishing in Emails Srikanth Palla and Ram Dantu, University of North Texas, Denton, TX announcements. The third category of spammers is called Abstract — Phishing attackers misrepresent the true sender and phishers. Phishing attackers misrepresent the true sender and steal consumers' personal identity data and financial account steal the consumers' personal identity data and financial credentials. Though phishers try to counterfeit the websites in account credentials. These spammers send spoofed emails and the content, they do not have access to all the fields in the lead consumers to counterfeit websites designed to trick email header. Our classification method is based on the recipients into divulging financial data such as credit card information provided in the email header (rather than the numbers, account usernames, passwords and social security content of the email). We believe the phisher cannot modify numbers. By hijacking brand names of banks, e-retailers and the complete header, though he can forge certain fields. We credit card companies, phishers often convince the recipients based our classification on three kinds of analyses on the to respond. Legislation can not help since a majority number header: DNS-based header analysis, Social Network analysis of phishers do not belong to the United States. In this paper we and Wantedness analysis. In the DNS-based header analysis, present a new method for recognizing phishing attacks so that we classified the corpus into 8 buckets and used social the consumers can be vigilant and not fall prey to these network analysis to further reduce the false positives. We counterfeit websites. Based on the relation between credibility introduced a concept of wantedness and credibility, and and phishing frequency, we classify the phishers into i) derived equations to calculate the wantedness values of the Prospective Phishers ii) Suspects iii) Recent Phishers iv) Serial email senders. -
Computer Viruses, in Order to Detect Them
Behaviour-based Virus Analysis and Detection PhD Thesis Sulaiman Amro Al amro This thesis is submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy Software Technology Research Laboratory Faculty of Technology De Montfort University May 2013 DEDICATION To my beloved parents This thesis is dedicated to my Father who has been my supportive, motivated, inspired guide throughout my life, and who has spent every minute of his life teaching and guiding me and my brothers and sisters how to live and be successful. To my Mother for her support and endless love, daily prayers, and for her encouragement and everything she has sacrificed for us. To my Sisters and Brothers for their support, prayers and encouragements throughout my entire life. To my beloved Family, My Wife for her support and patience throughout my PhD, and my little boy Amro who has changed my life and relieves my tiredness and stress every single day. I | P a g e ABSTRACT Every day, the growing number of viruses causes major damage to computer systems, which many antivirus products have been developed to protect. Regrettably, existing antivirus products do not provide a full solution to the problems associated with viruses. One of the main reasons for this is that these products typically use signature-based detection, so that the rapid growth in the number of viruses means that many signatures have to be added to their signature databases each day. These signatures then have to be stored in the computer system, where they consume increasing memory space. Moreover, the large database will also affect the speed of searching for signatures, and, hence, affect the performance of the system. -
Efficient Spam Filtering System Based on Smart Cooperative Subjective and Objective Methods*
Int. J. Communications, Network and System Sciences, 2013, 6, 88-99 http://dx.doi.org/10.4236/ijcns.2013.62011 Published Online February 2013 (http://www.scirp.org/journal/ijcns) Efficient Spam Filtering System Based on Smart * Cooperative Subjective and Objective Methods Samir A. Elsagheer Mohamed1,2 1College of Computer, Qassim University, Qassim, KSA 2Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt Email: [email protected], [email protected] Received September 17, 2012; revised January 16, 2013; accepted January 25, 2013 ABSTRACT Most of the spam filtering techniques are based on objective methods such as the content filtering and DNS/reverse DNS checks. Recently, some cooperative subjective spam filtering techniques are proposed. Objective methods suffer from the false positive and false negative classification. Objective methods based on the content filtering are time con- suming and resource demanding. They are inaccurate and require continuous update to cope with newly invented spammer’s tricks. On the other side, the existing subjective proposals have some drawbacks like the attacks from mali- cious users that make them unreliable and the privacy. In this paper, we propose an efficient spam filtering system that is based on a smart cooperative subjective technique for content filtering in addition to the fastest and the most reliable non-content-based objective methods. The system combines several applications. The first is a web-based system that we have developed based on the proposed technique. A server application having extra features suitable for the enter- prises and closed work groups is a second part of the system. Another part is a set of standard web services that allow any existing email server or email client to interact with the system. -
Download the Microsoft Tag and We’Ll Consider Them for the Magazine
Fall 2014 Magazine Giant Creative Strategy Output @ 100% x 10"h Live: 7.375"w 8.25”w x 10.75”h Trim: Bleed: 8.75"w x 11.25"h Colors: 4CP + AD WINTER HB PATIENT PRINT GILPT11284 Speak from the PRINT live/trim_DO NOT heart about your angina “Tell your cardiologist exactly how you’re feeling. Don’t hold anything back.” Donnette, angina patient If you have been limiting your work or your activities because of your chronic angina, be sure to talk about it with your cardiologist. Your cardiologist is listening www.SpeakFromTheHeart.com Tips, information, and more from real angina patients Donnette, Ralph, and Claudia. Claudia, angina patient Speak From the Heart is a trademark, and the Speak From the Heart logo Ralph, angina patient is a registered trademark, of Gilead Sciences, Inc. © 2011 Gilead Sciences, Inc. All rights reserved. UN7951 1/11 D19528_1a_Donette.indd 11.04.2013 A17041x01G_300ucr_RBlk.tif 133 linescreen B19528x01A_3u.tif jn B19528x02A_3u.tif Dedicated to inspiring hope in heart disease patients and their families. Mission: Inspiring hope and improving the quality of life for heart patients and their families through ongoing peer-to-peer support THE MENDED HEARTS, INC. BOARD OF DIRECTORS 2013-2015 President Gus Littlefield Executive Vice President Donnette Smith Vice President Lynn Berringer Treasurer Dale Briggs Mended Little Hearts Vice President Andrea Baer Fall 2014 Regional Directors Central Jana Stewart Mid-Atlantic Gerald Kemp Midwest Cathy Byington Northeast Margaret Elbert Rocky Mountain Randy Gay Southern Fredonia Williams