US-CERT Security Trends Report: 2012 in Retrospect US-CERT Security Trends Report: 2012 in Retrospect
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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. -
Internet Security Threat Report Volume 24 | February 2019
ISTRInternet Security Threat Report Volume 24 | February 2019 THE DOCUMENT IS PROVIDED “AS IS” AND ALL EXPRESS OR IMPLIED CONDITIONS, REPRESENTATIONS AND WARRANTIES, INCLUDING ANY IMPLIED WARRANTY OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE OR NON-INFRINGEMENT, ARE DISCLAIMED, EXCEPT TO THE EXTENT THAT SUCH DISCLAIMERS ARE HELD TO BE LEGALLY INVALID. SYMANTEC CORPORATION SHALL NOT BE LIABLE FOR INCIDENTAL OR CONSEQUENTIAL DAMAGES IN CONNECTION WITH THE FURNISHING, PERFORMANCE, OR USE OF THIS DOCUMENT. THE INFORMATION CONTAINED IN THIS DOCUMENT IS SUBJECT TO CHANGE WITHOUT NOTICE. INFORMATION OBTAINED FROM THIRD PARTY SOURCES IS BELIEVED TO BE RELIABLE, BUT IS IN NO WAY GUARANTEED. SECURITY PRODUCTS, TECHNICAL SERVICES, AND ANY OTHER TECHNICAL DATA REFERENCED IN THIS DOCUMENT (“CONTROLLED ITEMS”) ARE SUBJECT TO U.S. EXPORT CONTROL AND SANCTIONS LAWS, REGULATIONS AND REQUIREMENTS, AND MAY BE SUBJECT TO EXPORT OR IMPORT REGULATIONS IN OTHER COUNTRIES. YOU AGREE TO COMPLY STRICTLY WITH THESE LAWS, REGULATIONS AND REQUIREMENTS, AND ACKNOWLEDGE THAT YOU HAVE THE RESPONSIBILITY TO OBTAIN ANY LICENSES, PERMITS OR OTHER APPROVALS THAT MAY BE REQUIRED IN ORDER FOR YOU TO EXPORT, RE-EXPORT, TRANSFER IN COUNTRY OR IMPORT SUCH CONTROLLED ITEMS. TABLE OF CONTENTS 1 2 3 BIG NUMBERS YEAR-IN-REVIEW FACTS AND FIGURES METHODOLOGY Formjacking Messaging Cryptojacking Malware Ransomware Mobile Living off the land Web attacks and supply chain attacks Targeted attacks Targeted attacks IoT Cloud Underground economy IoT Election interference MALICIOUS -
1.Computer Virus Reported (1) Summary for This Quarter
Attachment 1 1.Computer Virus Reported (1) Summary for this Quarter The number of the cases reported for viruses*1 in the first quarter of 2013 decreased from that of the fourth quarter of 2012 (See Figure 1-1). As for the number of the viruses detected*2 in the first quarter of 2013, W32/Mydoom accounted for three-fourths of the total (See Figure 1-2). Compared to the fourth quarter of 2012, however, both W32/Mydoom and W32/Netsky showed a decreasing trend. When we looked into the cases reported for W32/Netsky, we found that in most of those cases, the virus code had been corrupted, for which the virus was unable to carry out its infection activity. So, it is unlikely that the number of cases involving this virus will increase significantly in the future As for W32/IRCbot, it has greatly decreased from the level of the fourth quarter of 2012. W32/IRCbot carries out infection activities by exploiting vulnerabilities within Windows or programs, and is often used as a foothold for carrying out "Targeted Attack". It is likely that that there has been a shift to attacks not using this virus. XM/Mailcab is a mass-mailing type virus that exploits mailer's address book and distributes copies of itself. By carelessly opening this type of email attachment, the user's computer is infected and if the number of such users increases, so will the number of the cases reported. As for the number of the malicious programs detected in the first quarter of 2013, Bancos, which steals IDs/Passwords for Internet banking, Backdoor, which sets up a back door on the target PC, and Webkit, which guides Internet users to a maliciously-crafted Website to infect with another virus, were detected in large numbers. -
Éric FREYSSINET Lutte Contre Les Botnets
THÈSE DE DOCTORAT DE L’UNIVERSITÉ PIERRE ET MARIE CURIE Spécialité Informatique École doctorale Informatique, Télécommunications et Électronique (Paris) Présentée par Éric FREYSSINET Pour obtenir le grade de DOCTEUR DE L’UNIVERSITÉ PIERRE ET MARIE CURIE Sujet de la thèse : Lutte contre les botnets : analyse et stratégie Présentée et soutenue publiquement le 12 novembre 2015 devant le jury composé de : Rapporteurs : M. Jean-Yves Marion Professeur, Université de Lorraine M. Ludovic Mé Enseignant-chercheur, CentraleSupélec Directeurs : M. David Naccache Professeur, École normale supérieure de thèse M. Matthieu Latapy Directeur de recherche, UPMC, LIP6 Examinateurs : Mme Clémence Magnien Directrice de recherche, UPMC, LIP6 Mme Solange Ghernaouti-Hélie Professeure, Université de Lausanne M. Vincent Nicomette Professeur, INSA Toulouse Cette thèse est dédiée à M. Celui qui n’empêche pas un crime alors qu’il le pourrait s’en rend complice. — Sénèque Remerciements Je tiens à remercier mes deux directeurs de thèse. David Naccache, officier de réserve de la gendarmerie, contribue au développement de la recherche au sein de notre institution en poussant des personnels jeunes et un peu moins jeunes à poursuivre leur passion dans le cadre académique qui s’impose. Matthieu Latapy, du LIP6, avec qui nous avions pu échanger autour d’une thèse qu’il encadrait dans le domaine difficile des atteintes aux mineurs sur Internet et qui a accepté de m’accueillir dans son équipe. Je voudrais remercier aussi, l’ensemble de l’équipe Réseaux Complexes du LIP6 et sa responsable d’équipe actuelle, Clémence Magnien, qui m’ont accueilli à bras ouverts, accom- pagné à chaque étape et dont j’ai pu découvrir les thématiques et les méthodes de travail au fil des rencontres et des discussions. -
Diapositiva 1
Feliciano Intini Responsabile dei programmi di Sicurezza e Privacy di Microsoft Italia • NonSoloSecurity Blog: http://blogs.technet.com/feliciano_intini • Twitter: http://twitter.com/felicianointini 1. Introduction - Microsoft Security Intelligence Report (SIR) 2. Today‘s Threats - SIR v.8 New Findings – Italy view 3. Advancements in Software Protection and Development 4. What the Users and Industry Can Do The 8th volume of the Security Intelligence Report contains data and intelligence from the past several years, but focuses on the second half of 2009 (2H09) Full document covers Malicious Software & Potentially Unwanted Software Email, Spam & Phishing Threats Focus sections on: Malware and signed code Threat combinations Malicious Web sites Software Vulnerability Exploits Browser-based exploits Office document exploits Drive-by download attacks Security and privacy breaches Software Vulnerability Disclosures Microsoft Security Bulletins Exploitability Index Usage trends for Windows Update and Microsoft Update Microsoft Malware Protection Center (MMPC) Microsoft Security Response Center (MSRC) Microsoft Security Engineering Center (MSEC) Guidance, advice and strategies Detailed strategies, mitigations and countermeasures Fully revised and updated Guidance on protecting networks, systems and people Microsoft IT ‗real world‘ experience How Microsoft IT secures Microsoft Malware patterns around the world with deep-dive content on 26 countries and regions Data sources Malicious Software and Potentially Unwanted Software MSRT has a user base -
Proposal of N-Gram Based Algorithm for Malware Classification
SECURWARE 2011 : The Fifth International Conference on Emerging Security Information, Systems and Technologies Proposal of n-gram Based Algorithm for Malware Classification Abdurrahman Pektaş Mehmet Eriş Tankut Acarman The Scientific and Technological The Scientific and Technological Computer Engineering Dept. Research Council of Turkey Research Council of Turkey Galatasaray University National Research Institute of National Research Institute of Istanbul, Turkey Electronics and Cryptology Electronics and Cryptology e-mail:[email protected] Gebze, Turkey Gebze, Turkey e-mail:[email protected] e-mail:[email protected] Abstract— Obfuscation techniques degrade the n-gram malware used by the previous work. Similar methodologies features of binary form of the malware. In this study, have been used in source authorship, information retrieval methodology to classify malware instances by using n-gram and natural language processing [5], [6]. features of its disassembled code is presented. The presented The first known use of machine learning in malware statistical method uses the n-gram features of the malware to detection is presented by the work of Tesauro et al. in [7]. classify its instance with respect to their families. n-gram is a fixed size sliding window of byte array, where n is the size of This detection algorithm was successfully implemented in the window. The contribution of the presented method is IBM’s antivirus scanner. They used 3-grams as a feature set capability of using only one vector to represent malware and neural networks as a classification model. When the 3- subfamily which is called subfamily centroid. Using only one grams parameter is selected, the number of all n-gram vector for classification simply reduces the dimension of the n- features becomes 2563, which leads to some spacing gram space. -
Iptrust Botnet / Malware Dictionary This List Shows the Most Common Botnet and Malware Variants Tracked by Iptrust
ipTrust Botnet / Malware Dictionary This list shows the most common botnet and malware variants tracked by ipTrust. This is not intended to be an exhaustive list, since new threat intelligence is always being added into our global Reputation Engine. NAME DESCRIPTION Conficker A/B Conficker A/B is a downloader worm that is used to propagate additional malware. The original malware it was after was rogue AV - but the army's current focus is undefined. At this point it has no other purpose but to spread. Propagation methods include a Microsoft server service vulnerability (MS08-067) - weakly protected network shares - and removable devices like USB keys. Once on a machine, it will attach itself to current processes such as explorer.exe and search for other vulnerable machines across the network. Using a list of passwords and actively searching for legitimate usernames - the ... Mariposa Mariposa was first observed in May 2009 as an emerging botnet. Since then it has infected an ever- growing number of systems; currently, in the millions. Mariposa works by installing itself in a hidden location on the compromised system and injecting code into the critical process ͞ĞdžƉůŽƌĞƌ͘ĞdžĞ͘͟/ƚŝƐknown to affect all modern Windows versions, editing the registry to allow it to automatically start upon login. Additionally, there is a guard that prevents deletion while running, and it automatically restarts upon crash/restart of explorer.exe. In essence, Mariposa opens a backdoor on the compromised computer, which grants full shell access to ... Unknown A botnet is designated 'unknown' when it is first being tracked, or before it is given a publicly- known common name. -
Symantec Intelligence Report: June 2011
Symantec Intelligence Symantec Intelligence Report: June 2011 Three-quarters of spam send from botnets in June, and three months on, Rustock botnet remains dormant as Cutwail becomes most active; Pharmaceutical spam in decline as new Wiki- pharmacy brand emerges Welcome to the June edition of the Symantec Intelligence report, which for the first time combines the best research and analysis from the Symantec.cloud MessageLabs Intelligence Report and the Symantec State of Spam & Phishing Report. The new integrated report, the Symantec Intelligence Report, provides the latest analysis of cyber security threats, trends and insights from the Symantec Intelligence team concerning malware, spam, and other potentially harmful business risks. The data used to compile the analysis for this combined report includes data from May and June 2011. Report highlights Spam – 72.9% in June (a decrease of 2.9 percentage points since May 2011): page 11 Phishing – One in 330.6 emails identified as phishing (a decrease of 0.05 percentage points since May 2011): page 14 Malware – One in 300.7 emails in June contained malware (a decrease of 0.12 percentage points since May 2011): page 15 Malicious Web sites – 5,415 Web sites blocked per day (an increase of 70.8% since May 2011): page 17 35.1% of all malicious domains blocked were new in June (a decrease of 1.7 percentage points since May 2011): page 17 20.3% of all Web-based malware blocked was new in June (a decrease of 4.3 percentage points since May 2011): page 17 Review of Spam-sending botnets in June 2011: page 3 Clicking to Watch Videos Leads to Pharmacy Spam: page 6 Wiki for Everything, Even for Spam: page 7 Phishers Return for Tax Returns: page 8 Fake Donations Continue to Haunt Japan: page 9 Spam Subject Line Analysis: page 12 Best Practices for Enterprises and Users: page 19 Introduction from the editor Since the shutdown of the Rustock botnet in March1, spam volumes have never quite recovered as the volume of spam in global circulation each day continues to fluctuate, as shown in figure 1, below. -
Contents in This Issue
FEBRUARY 2014 Covering the global threat landscape CONTENTS IN THIS ISSUE 2 COMMENT A LIFE OF GRIME It is time for defenders to go on the offence Cross-platform execution is one of the promises of Java – but cross-platform infection is probably 3 NEWS not what the designers had in mind. Nevertheless, that was clearly in the mind of the author of Law minister is former spammer W32/Java.Grimy, a virus for the Windows platform, Cash for hacks which infects Java class fi les. Peter Ferrie has the details. MALWARE ANALYSES page 4 4 Getting one’s hands dirty LAME DUCK 6 Salted algorithm – part 2 Sometimes what looks like a genuine MP3 encoder 11 Inside W32.Xpaj.B’s infection – part 2 library, and even works as a functional encoder, actually hides malicious code deep amongst a 19 FEATURE pile of clean code. Gabor Szappanos reveals the Needle in a haystack lengths to which one piece of malware goes to hide its tracks. page 19 27 BOOK REVIEW Don’t forget to write READING CORNER Industry veteran, prolifi c writer and educator David 30 SPOTLIGHT Harley reviews two recently published eBooks that aim to provide security guidance for consumers: Greetz from academe: Full frontal ‘Improve Your Security’ by Sorin Mustaca, and ‘One Parent to Another’ by Tony Anscombe. 31 END NOTES & NEWS page 27 ISSN 1749-7027 COMMENT ‘Challenge for example, an IPS or anti-virus product will still do some level of good if you do no more than install it on [defenders] to your network and make sure it gets updated occasionally, take a penetration a SIEM will not do anything except generate a (huge) bill. -
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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 -
Classification of Malware Models Akriti Sethi San Jose State University
San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-20-2019 Classification of Malware Models Akriti Sethi San Jose State University Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Information Security Commons Recommended Citation Sethi, Akriti, "Classification of Malware Models" (2019). Master's Projects. 703. DOI: https://doi.org/10.31979/etd.mrqp-sur9 https://scholarworks.sjsu.edu/etd_projects/703 This Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [email protected]. Classification of Malware Models A Project Presented to The Faculty of the Department of Computer Science San José State University In Partial Fulfillment of the Requirements for the Degree Master of Science by Akriti Sethi May 2019 © 2019 Akriti Sethi ALL RIGHTS RESERVED The Designated Project Committee Approves the Project Titled Classification of Malware Models by Akriti Sethi APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE SAN JOSÉ STATE UNIVERSITY May 2019 Dr. Mark Stamp Department of Computer Science Dr. Thomas Austin Department of Computer Science Dr. Philip Heller Department of Computer Science ABSTRACT Classification of Malware Models by Akriti Sethi Automatically classifying similar malware families is a challenging problem. In this research, we attempt to classify malware families by applying machine learning to machine learning models. Specifically, we train hidden Markov models (HMM) for each malware family in our dataset.