Éric FREYSSINET Lutte Contre Les Botnets
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Symantec White Paper
QUARTERLY REPORT: SYMANTEC ENTERPRISE SECURITY SYMANTEC REPORT: QUARTERLY Symantec Intelligence Quarterly July - September, 2009 Published October 2009 Technical Brief: Symantec Enterprise Security Symantec Intelligence Quarterly July - September, 2009 Contents Introduction . 1 Highlights . 2 Metrics. 2 Meeting the Challenge of Sophisticated Attacks . 8 Timeline of a zero-day event . 8 How secure are security protocols?. 11 Why attackers use packers. 14 Protection and Mitigation . 16 Appendix A—Best Practices . 18 Appendix B—Methodologies. 20 Credits . 24 Symantec Intelligence Quarterly July - September, 2009 Introduction Symantec has established some of the most comprehensive sources of Internet threat data in the world through the Symantec™ Global Intelligence Network. More than 240,000 sensors in over 200 countries monitor attack activity through a combination of Symantec products and services such as Symantec DeepSight™ Threat Management System, Symantec™ Managed Security Services and Norton™ consumer products, as well as additional third-party data sources. Symantec also gathers malicious code intelligence from more than 130 million client, server, and gateway systems that have deployed its antivirus products. Additionally, the Symantec distributed honeypot network collects data from around the globe, capturing previously unseen threats and attacks and providing valuable insight into attacker methods. Spam data is captured through the Symantec probe network, a system of more than 2.5 million decoy email accounts, Symantec MessageLabs™ Intelligence, and other Symantec technologies in more than 86 countries from around the globe. Over 8 billion email messages, as well as over 1 billion Web requests, are scanned per day across 16 data centers. Symantec also gathers phishing information through an extensive antifraud community of enterprises, security vendors, and more than 50 million consumers. -
Ilomo Botnet a Study of the Ilomo / Clampi Botnet
Ilomo A study of the Ilomo / Clampi botnet Ilomo Botnet A study of the Ilomo / Clampi Botnet by Alice Decker: Network Analysis David Sancho: Reverse Engineering Max Goncharov: Network Analysis Robert McArdle: Project Coordinator Release Date: 20 August 2009 Classification: Public Ilomo A study of the Ilomo / Clampi botnet Table of Contents Introduction ........................................................................................................................................................... 3 Ilomo Analysis ....................................................................................................................................................... 4 Stage 1: Dropper ....................................................................................................................................... 4 Stage 2: Main Executable ........................................................................................................................ 7 Stage 3: Injected Code ............................................................................................................................ 12 VMProtect Obfuscator ........................................................................................................................................ 17 Background Information .......................................................................................................................... 17 Technical Information ............................................................................................................................. -
Ronald L. Chichester*
30990-txb_44-1 Sheet No. 4 Side A 02/01/2012 14:53:16 ZOMBIES (DO NOT DELETE) 10/27/2011 12:39 PM SLAYING ZOMBIES IN THE COURTROOM:TEXAS ENACTS THE FIRST LAW DESIGNED SPECIFICALLY TO COMBAT BOTNETS Ronald L. Chichester* I. INTRODUCTION ............................................................................................... 2 II. WHAT IS A BOTNET? ...................................................................................... 2 III. ZOMBIFICATION—CREATING THE BOTNET ............................................ 3 IV. OTHER CURRENT COMPUTER MISUSE STATUTES ................................. 4 V. A SHORT DESCRIPTION OF THE FIRST ANTI-BOTNET STATUTE ........ 6 VI. UTILIZING S.B. 28—THE CAUSE OF ACTION ............................................ 8 VII. GATHERING THE EVIDENCE ........................................................................ 9 VIII. FINDING THE PERPETRATOR(S) .................................................................. 9 IX. CONCLUSIONS ............................................................................................... 10 X. APPENDIX A ................................................................................................... 10 30990-txb_44-1 Sheet No. 4 Side A 02/01/2012 14:53:16 * Ron Chichester is an attorney, a certified computer forensic examiner, and an Adjunct Professor at the University of Houston Law Center, where he teaches “Digital Transaction” (www.digitaltransactions.info). Ron is admitted to practice in the State of Texas, the U.S. Courts for the Southern District of -
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Scott Wu Point in time cleaning vs. RTP MSRT vs. Microsoft Security Essentials Threat events & impacts More on MSRT / Security Essentials MSRT Microsoft Windows Malicious Software Removal Tool Deployed to Windows Update, etc. monthly since 2005 On-demand scan on prevalent malware Microsoft Security Essentials Full AV RTP Inception in Oct 2009 RTP is the solution One-off cleaner has its role Quiikck response Workaround Baseline ecosystem cleaning Industrypy response & collaboration Threat Events Worms (some are bots) have longer lifespans Rogues move on quicker MarMar 2010 2010 Apr Apr 2010 2010 May May 2010 2010 Jun Jun 2010 2010 Jul Jul 2010 2010 Aug Aug 2010 2010 1,237,15 FrethogFrethog 979,427 979,427 Frethog Frethog 880,246880,246 Frethog Frethog465,351 TaterfTaterf 5 1,237,155Taterf Taterf 797,935797,935 TaterfTaterf 451,561451,561 TaterfTaterf 497,582 497,582 Taterf Taterf 393,729393,729 Taterf Taterf447,849 FrethogFrethog 535,627535,627 AlureonAlureon 493,150 493,150 AlureonAlureon 436,566 436,566 RimecudRimecud 371,646 371,646 Alureon Alureon 308,673308,673 Alureon Alureon 441,722 RimecudRimecud 341,778341,778 FrethogFrethog 473,996473,996 BubnixBubnix 348,120 348,120 HamweqHamweq 289,603 289,603 Rimecud Rimecud289,629 289,629 Rimecud Rimecud318,041 AlureonAlureon 292,810 292,810 BubnixBubnix 471,243 471,243 RimecudRimecud 287,942287,942 ConfickerConficker 286,091286, 091 Hamwe Hamweqq 250,286250, 286 Conficker Conficker220,475220, 475 ConfickerConficker 237237,348, 348 RimecudRimecud 280280,440, 440 VobfusVobfus 251251,335, 335 -
Zerohack Zer0pwn Youranonnews Yevgeniy Anikin Yes Men
Zerohack Zer0Pwn YourAnonNews Yevgeniy Anikin Yes Men YamaTough Xtreme x-Leader xenu xen0nymous www.oem.com.mx www.nytimes.com/pages/world/asia/index.html www.informador.com.mx www.futuregov.asia www.cronica.com.mx www.asiapacificsecuritymagazine.com Worm Wolfy Withdrawal* WillyFoReal Wikileaks IRC 88.80.16.13/9999 IRC Channel WikiLeaks WiiSpellWhy whitekidney Wells Fargo weed WallRoad w0rmware Vulnerability Vladislav Khorokhorin Visa Inc. Virus Virgin Islands "Viewpointe Archive Services, LLC" Versability Verizon Venezuela Vegas Vatican City USB US Trust US Bankcorp Uruguay Uran0n unusedcrayon United Kingdom UnicormCr3w unfittoprint unelected.org UndisclosedAnon Ukraine UGNazi ua_musti_1905 U.S. Bankcorp TYLER Turkey trosec113 Trojan Horse Trojan Trivette TriCk Tribalzer0 Transnistria transaction Traitor traffic court Tradecraft Trade Secrets "Total System Services, Inc." Topiary Top Secret Tom Stracener TibitXimer Thumb Drive Thomson Reuters TheWikiBoat thepeoplescause the_infecti0n The Unknowns The UnderTaker The Syrian electronic army The Jokerhack Thailand ThaCosmo th3j35t3r testeux1 TEST Telecomix TehWongZ Teddy Bigglesworth TeaMp0isoN TeamHav0k Team Ghost Shell Team Digi7al tdl4 taxes TARP tango down Tampa Tammy Shapiro Taiwan Tabu T0x1c t0wN T.A.R.P. Syrian Electronic Army syndiv Symantec Corporation Switzerland Swingers Club SWIFT Sweden Swan SwaggSec Swagg Security "SunGard Data Systems, Inc." Stuxnet Stringer Streamroller Stole* Sterlok SteelAnne st0rm SQLi Spyware Spying Spydevilz Spy Camera Sposed Spook Spoofing Splendide -
Improved Detection for Advanced Polymorphic Malware James B
Nova Southeastern University NSUWorks CEC Theses and Dissertations College of Engineering and Computing 2017 Improved Detection for Advanced Polymorphic Malware James B. Fraley Nova Southeastern University, [email protected] This document is a product of extensive research conducted at the Nova Southeastern University College of Engineering and Computing. For more information on research and degree programs at the NSU College of Engineering and Computing, please click here. Follow this and additional works at: https://nsuworks.nova.edu/gscis_etd Part of the Computer Sciences Commons Share Feedback About This Item NSUWorks Citation James B. Fraley. 2017. Improved Detection for Advanced Polymorphic Malware. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. (1008) https://nsuworks.nova.edu/gscis_etd/1008. This Dissertation is brought to you by the College of Engineering and Computing at NSUWorks. It has been accepted for inclusion in CEC Theses and Dissertations by an authorized administrator of NSUWorks. For more information, please contact [email protected]. Improved Detection for Advanced Polymorphic Malware by James B. Fraley A Dissertation Proposal submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Assurance College of Engineering and Computing Nova Southeastern University 2017 ii An Abstract of a Dissertation Submitted to Nova Southeastern University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Improved Detection for Advanced Polymorphic Malware by James B. Fraley May 2017 Malicious Software (malware) attacks across the internet are increasing at an alarming rate. Cyber-attacks have become increasingly more sophisticated and targeted. These targeted attacks are aimed at compromising networks, stealing personal financial information and removing sensitive data or disrupting operations. -
Universidad Carlos Iii De Madrid Signal Processing
UNIVERSIDAD CARLOS III DE MADRID ESCUELA POLITÉCNICA SUPERIOR BACHELOR THESIS SIGNAL PROCESSING FOR MALWARE ANALYSIS Computer Engineering Department AUTHOR: Raquel Tabuyo Benito TUTOR: Pedro Peris Lopez June, 2016 Bachelor Thesis. Signal Processing for Malware Analysis “Perseverance is not a long race. It is many short races one aftr te oter” -Walter Elliot - Page .2 of 134. - Bachelor Thesis. Signal Processing for Malware Analysis Acknowledgements To my whole family, specially my sister, for whom I have an unconditionally love. I am really grateful for their dedication, patience, support and encouragement to follow my dreams. To Pedro, my Bachelor Thesis tutor, whose kindness and guidance have helped me during this wonderful trip. To my friends, thank you very much for showing me the meaning of true friendship. Without all of you, this would have never been possible. - Page .3 of 134. - Bachelor Thesis. Signal Processing for Malware Analysis Abstract This Project is an experimental analysis of Android malware through images. The analysis is based on classifying the malware into families or differentiating between goodware and malware. This analysis has been done considering two approaches. These two approaches have a common starting point, which is the transformation of Android applications into PNG images. After this conversion, the first approach was subtracting each image from the testing set with the images of the training set, in order to establish which unknown malware belongs to a specific family or to distinguish between goodware and malware. Although the accuracy was higher than the one defined in the requirements, this approach was a time consuming task, so we consider another approach to reduce the time and get the same or better accuracy. -
Improving the Effectiveness of Behaviour-Based Malware Detection
Improving the Effectiveness of Behaviour-based Malware Detection Mohd Fadzli Marhusin BSc. Information Studies (Hons) (Information Systems Management) UiTM, Malaysia Master of Information Technology (Computer Science) UKM, Malaysia A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at the School of Engineering and Information Technology University of New South Wales Australian Defence Force Academy Copyright 2012 by Mohd Fadzli Marhusin PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name: MARHUSIN First name: MOHD FADZLI Other name/s: Abbreviation for degree as given in the University calendar: PhD (Computer Science) School: School of Engineering and Information Technology (SEIT) Faculty: Title: Improving the Effectiveness of Behaviour-based Malware Detection Abstract 350 words maximum: (PLEASE TYPE) Malware is software code which has malicious intent but can only do harm if it is allowed to execute and propagate. Detection based on signature alone is not the answer, because new malware with new signatures cannot be detected. Thus, behaviour-based detection is needed to detect novel malware attacks. Moreover, malware detection is a challenging task when most of the latest malware employs some protection and evasion techniques. In this study, we present a malware detection system that addresses both propagation and execution. Detection is based on monitoring session traffic for propagation, and API call sequences for execution. For malware detection during propagation, we investigate the effectiveness of signature-based detection, anomaly-based detection and the combination of both. The decision-making relies upon a collection of recent signatures of session-based traffic data collected at the endpoint level. -
Computer Viruses and Malware Advances in Information Security
Computer Viruses and Malware Advances in Information Security Sushil Jajodia Consulting Editor Center for Secure Information Systems George Mason University Fairfax, VA 22030-4444 email: [email protected] The goals of the Springer International Series on ADVANCES IN INFORMATION SECURITY are, one, to establish the state of the art of, and set the course for future research in information security and, two, to serve as a central reference source for advanced and timely topics in information security research and development. The scope of this series includes all aspects of computer and network security and related areas such as fault tolerance and software assurance. ADVANCES IN INFORMATION SECURITY aims to publish thorough and cohesive overviews of specific topics in information security, as well as works that are larger in scope or that contain more detailed background information than can be accommodated in shorter survey articles. The series also serves as a forum for topics that may not have reached a level of maturity to warrant a comprehensive textbook treatment. Researchers, as well as developers, are encouraged to contact Professor Sushil Jajodia with ideas for books under this series. Additional tities in the series: HOP INTEGRITY IN THE INTERNET by Chin-Tser Huang and Mohamed G. Gouda; ISBN-10: 0-387-22426-3 PRIVACY PRESERVING DATA MINING by Jaideep Vaidya, Chris Clifton and Michael Zhu; ISBN-10: 0-387- 25886-8 BIOMETRIC USER AUTHENTICATION FOR IT SECURITY: From Fundamentals to Handwriting by Claus Vielhauer; ISBN-10: 0-387-26194-X IMPACTS AND RISK ASSESSMENT OF TECHNOLOGY FOR INTERNET SECURITY.'Enabled Information Small-Medium Enterprises (TEISMES) by Charles A. -
Monthly Report on Online Threats in The
MONTHLY REPORT ON ONLINE THREATS REPORTING PERIOD: IN THE BANKING SECTOR 19.04–19.05.2014 One of the main events during the reporting period was the leakage of payment credentials belonging to eBay users. Details of the incident and other detected threats can be found in the section ‘Key events in the online banking sphere’ below. Overall statistics During the reporting period, Kaspersky Lab solutions blocked 341,216 attempts on user computers to launch malware capable of stealing money from online banking accounts. This figure represents a 36.6% increase compared to the previous reporting period (249,812). This increase in banking malware activity is most likely related to the onset of the vacation season, when customers actively use their payment data to make all types of purchases online. 24 001 - 78 000 16 001 - 24 000 7101 - 16 000 2101 - 7100 1 - 2100 Number of users targeted by banking malware The number of users attacked using these types of programs during the reporting period is shown in the diagram below (Top 10 rating based on the number of users attacked, in descending order): 77,412 27,071 21,801 22,115 13,876 15,651 17,333 5417 6883 7347 France Vietnam Austria India Germany United USA Russian Italy Brazil Kingdom Federation © 1997-2014 Kaspersky Lab ZAO. All Rights Reserved. The table below shows the programs most commonly used to attack online banking users, based on the number of infection attempts: Total notifications of Verdict* Number of users Number of notifications attempted infections by Trojan-Spy.Win32.Zbot 198 -
Cybercrime Hits the Unexpected
TrendLabsSM 1Q 2014 Security Roundup Cybercrime Hits the Unexpected Bitcoin- and PoS-System-Related Attacks Trouble Users Distributed by: TREND MICRO | TrendLabs 1Q 2014 Security Roundup TREND MICRO | TrendLabs 1Q 2014 Security Roundup Contents It gives me immense pleasure to share this report, developed by Trend Micro and distributed by ITU based on our fruitful partnership. This report is part of ITU’s overall support to its 193 Member States within the framework of the Global Cybersecurity Agenda. This is clear result of our partnership with Private Sector in our effort to tackle this growing Global phenomenon. 1 | Cybercrime and the Cybercriminal Underground These efforts are aimed at equipping Member States with information on the latest cybersecurity threats and corresponding counter-measures, and eventually contribute to the creation of a safe and secure cyberspace for consumers, businesses and youth everywhere. This Trend Micro quarterly report underlines the existing and emerging malicious cyber activities observed during the first quarter of 2014. Some highlights of this 10 | Mobile Threat Landscape report: • Cybercriminals are continuing to innovate and create new ways to commit digital crimes. Crypto-currency exchanges and wallets are being compromised for the purpose of theft. In addition, online banking malware are being enhanced with new technologies to exact the maximum damage. 15 | Targeted Attack Campaigns • With the exponential growth in the mobile industry, cyber-criminals are and Cyber Attacks increasingly targeting mobile devices. There is a proliferation of maliciously tampered and re-packaged apps that circumvent the security firewalls in mobile devices. • We are also witnessing cases of attacks directly aimed at organizations that rely on specific Point of Sales (Pos) systems features. -
The Malware Book 2016
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/305469492 Handbook of Malware 2016 - A Wikipedia Book Book · July 2016 DOI: 10.13140/RG.2.1.5039.5122 CITATIONS READS 0 13,014 2 authors, including: Reiner Creutzburg Brandenburg University of Applied Sciences 489 PUBLICATIONS 472 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: NDT CE – Assessment of structures || ZfPBau – ZfPStatik View project 14. Nachwuchswissenschaftlerkonferenz Ost- und Mitteldeutscher Fachhochschulen (NWK 14) View project All content following this page was uploaded by Reiner Creutzburg on 20 July 2016. The user has requested enhancement of the downloaded file. Handbook of Malware 2016 A Wikipedia Book By Wikipedians Edited by: Reiner Creutzburg Technische Hochschule Brandenburg Fachbereich Informatik und Medien PF 2132 D-14737 Brandenburg Germany Email: [email protected] Contents 1 Malware - Introduction 1 1.1 Malware .................................................. 1 1.1.1 Purposes ............................................. 1 1.1.2 Proliferation ........................................... 2 1.1.3 Infectious malware: viruses and worms ............................. 3 1.1.4 Concealment: Viruses, trojan horses, rootkits, backdoors and evasion .............. 3 1.1.5 Vulnerability to malware ..................................... 4 1.1.6 Anti-malware strategies ..................................... 5 1.1.7 Grayware ............................................