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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. -
Symantec Report on Rogue Security Software July 08 – June 09
REPORT: SYMANTEC ENTERPRISE SECURITY SYMANTEC REPORT: Symantec Report on Rogue Security Software July 08 – June 09 Published October 2009 Confidence in a connected world. White Paper: Symantec Enterprise Security Symantec Report on Rogue Security Software July 08 – June 09 Contents Introduction . 1 Overview of Rogue Security Software. 2 Risks . 4 Advertising methods . 7 Installation techniques . 9 Legal actions and noteworthy scam convictions . 14 Prevalence of Rogue Security Software . 17 Top reported rogue security software. 17 Additional noteworthy rogue security software samples . 25 Top rogue security software by region . 28 Top rogue security software installation methods . 29 Top rogue security software advertising methods . 30 Analysis of Rogue Security Software Distribution . 32 Analysis of Rogue Security Software Servers . 36 Appendix A: Protection and Mitigation. 45 Appendix B: Methodologies. 48 Credits . 50 Symantec Report on Rogue Security Software July 08 – June 09 Introduction The Symantec Report on Rogue Security Software is an in-depth analysis of rogue security software programs. This includes an overview of how these programs work and how they affect users, including their risk implications, various distribution methods, and innovative attack vectors. It includes a brief discussion of some of the more noteworthy scams, as well as an analysis of the prevalence of rogue security software globally. It also includes a discussion on a number of servers that Symantec observed hosting these misleading applications. Except where otherwise noted, the period of observation for this report was from July 1, 2008, to June 30, 2009. Symantec has established some of the most comprehensive sources of Internet threat data in the world through the Symantec™ Global Intelligence Network. -
Rootkit- Rootkits.For.Dummies 2007.Pdf
01_917106 ffirs.qxp 12/21/06 12:04 AM Page i Rootkits FOR DUMmIES‰ 01_917106 ffirs.qxp 12/21/06 12:04 AM Page ii 01_917106 ffirs.qxp 12/21/06 12:04 AM Page iii Rootkits FOR DUMmIES‰ by Larry Stevenson and Nancy Altholz 01_917106 ffirs.qxp 12/21/06 12:04 AM Page iv Rootkits For Dummies® Published by Wiley Publishing, Inc. 111 River Street Hoboken, NJ 07030-5774 www.wiley.com Copyright © 2007 by Wiley Publishing, Inc., Indianapolis, Indiana Published by Wiley Publishing, Inc., Indianapolis, Indiana Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permit- ted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Legal Department, Wiley Publishing, Inc., 10475 Crosspoint Blvd., Indianapolis, IN 46256, (317) 572-3447, fax (317) 572-4355, or online at http://www.wiley.com/go/permissions. Trademarks: Wiley, the Wiley Publishing logo, For Dummies, the Dummies Man logo, A Reference for the Rest of Us!, The Dummies Way, Dummies Daily, The Fun and Easy Way, Dummies.com, and related trade dress are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries, and may not be used without written permission. -
PC Anti-Virus Protection 2011
PC Anti-Virus Protection 2011 12 POPULAR ANTI-VIRUS PROGRAMS COMPARED FOR EFFECTIVENESS Dennis Technology Labs, 03/08/2010 www.DennisTechnologyLabs.com This test aims to compare the effectiveness of the most recent releases of popular anti-virus software1. The products include those from Kaspersky, McAfee, Microsoft, Norton (Symantec) and Trend Micro, as well as free versions from Avast, AVG and Avira. Other products include those from BitDefender, ESET, G-Data and K7. The tests were conducted between 07/07/2010 and 22/07/2010 using the most up to date versions of the software available. A total of 12 products were exposed to genuine internet threats that real customers could have encountered during the test period. Crucially, this exposure was carried out in a realistic way, reflecting a customer’s experience as closely as possible. For example, each test system visited real, infected websites that significant numbers of internet users were encountering at the time of the test. These results reflect what would have happened if those users were using one of the seven products tested. EXECUTIVE SUMMARY Q Products that block attacks early tended to protect the system more fully The nature of web-based attacks means that the longer malware has access to a system, the more chances it has of downloading and installing further threats. Products that blocked the malicious and infected websites from the start reduced the risk of compromise by secondary and further downloads. Q 100 per cent protection is rare This test recorded an average protection rate of 87.5 per cent. New threats appear online frequently and it is inevitable that there will be times when specific security products are unable to protect from some of these threats. -
The Most Common Blunder People Make When the Topic of a Computer Virus Arises Is to Refer to a Worm Or Trojan Horse As a Virus
Trojan And Email Forging 1) Introduction To Trojan&viruses: A Trojan horse, or Trojan, in computing is a generally non-self-replicating type of malware program containing malicious code that, when executed, carries out actions determined by the nature of the Trojan, typically causing loss or theft of data, and possible system harm. The term is derived from the story of the wooden horse used to trick defenders of Troy into taking concealed warriors into their city in ancient Anatolia, because computer Trojans often employ a form of social engineering, presenting themselves as routine, useful, or interesting in order to persuade victims to install them on their computers.[1][2][3][4][5] A Trojan often acts as a backdoor, contacting a controller which can then have unauthorized access to the affected computer.[6] While Trojans and backdoors are not easily detectable by themselves, computers may appear to run slower due to heavy processor or network usage. Malicious programs are classified as Trojans if they do not attempt to inject themselves into other files (computer virus) or otherwise propagate themselves (worm).[7] A computer may host a Trojan via a malicious program a user is duped into executing (often an e-mail attachment disguised to be unsuspicious, e.g., a routine form to be filled in) or by drive-by download. The Difference Between a Computer Virus, Worm and Trojan Horse The most common blunder people make when the topic of a computer virus arises is to refer to a worm or Trojan horse as a virus. One common mistake that people make when the topic of a computer virus arises is to refer to a worm or Trojan horse as a virus. -
CONTENTS in THIS ISSUE Fighting Malware and Spam
AUGUST 2010 Fighting malware and spam CONTENTS IN THIS ISSUE 2 COMMENT EXPLOIT KITS UNDER SCRUTINY Apple pie order? Mark Davis documents how to create LAMP and WAMP servers and how to approach the study of 3 NEWS exploit kits in a local lab. page 8 VB2010 call for last-minute papers VB seminar DETECTING PHISHING All change Marius Tibeica describes an automated method of detecting phishing at the browser level based on the 3 VIRUS PREVALENCE TABLE tag structure of the HTML. page 11 4 TECHNICAL FEATURE VB100 CERTIFICATION ON Anti-unpacker tricks – part eleven WINDOWS VISTA With another epic haul of 54 8 TUTORIAL products to test this month, the VB Advanced exploit framework lab set-up test team could have done without Aug 2010 the bad behaviour of a number FEATURES of products: terrible product 11 HTML structure-based proactive phishing design, lack of accountability for detection activities, blatant false alarms in major software, numerous 15 What’s the deal with sender authentication? Part 3 problems detecting the WildList set, and some horrendous instability under pressure. Happily, there were also some good performances 21 COMPARATIVE REVIEW to balance things out. John Hawes has the details. Windows Vista Business Edition Service page 21 Pack 2 60 END NOTES & NEWS ISSN 1749-7027 COMMENT ‘Over 40% [of I’d contend that while ‘somewhat vulnerable’ might be about right for systems/application vulnerabilities computer users] and exposure to current malware, the fi gures would be think [that Macs are] more alarming if the survey were more focused on the vulnerability of users rather than systems. -
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. -
Microsoft Security Intelligence Report
Microsoft Security Intelligence Report Volume 12 July through December, 2011 www.microsoft.com/sir Microsoft Security Intelligence Report This document is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED, OR STATUTORY, AS TO THE INFORMATION IN THIS DOCUMENT. This document is provided “as-is.” Information and views expressed in this document, including URL and other Internet website references, may change without notice. You bear the risk of using it. Copyright © 2012 Microsoft Corporation. All rights reserved. The names of actual companies and products mentioned herein may be the trademarks of their respective owners. JULY–DECEMBER 2011 i Authors Dennis Batchelder David Felstead Ken Malcolmson Tim Rains Microsoft Protection Bing Microsoft Trustworthy Microsoft Trustworthy Technologies Computing Computing Paul Henry Shah Bawany Wadeware LLC Nam Ng Frank Simorjay Microsoft Windows Safety Microsoft Trustworthy Microsoft Trustworthy Platform Nitin Kumar Goel Computing Computing Microsoft Security Joe Blackbird Response Center Mark Oram Holly Stewart Microsoft Malware Microsoft Trustworthy Microsoft Malware Protection Center Jeff Jones Computing Protection Center Microsoft Trustworthy Eve Blakemore Computing Daryl Pecelj Matt Thomlinson Microsoft Trustworthy Microsoft IT Information Microsoft Trustworthy Computing Jimmy Kuo Security and Risk Computing Microsoft Malware Management Joe Faulhaber Protection Center Scott Wu Microsoft Malware Dave Probert Microsoft Malware Protection Center Marc Lauricella Microsoft -
Download Slides
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 -
Implementing Rootkits to Identify Vulnerabilities
Implementing Rootkits to Address Operating System Vulnerabilities Manuel Corregedor and Sebastiaan Von Solms Academy of Computer Science and Software Engineering, University of Johannesburg Johannesburg, South Africa {mrcorregedor, basievs}@uj.ac.za Abstract—Statistics show that although malware detection A rootkit is a malicious program or set of programs that techniques are detecting and preventing malware, they do not tries to hide its existence on an infected computer by attacking guarantee a 100% detection and / or prevention of malware. the Operating System (OS) by using one or a combination of This is especially the case when it comes to rootkits that can the following: modifying program binaries, hooking call tables manipulate the operating system such that it can distribute other such as the System Service Descriptor Table (SSDT) and the malware, hide existing malware, steal information, hide itself, Interrupt Descriptor Table (IDT) to hijack the kernel's control disable anti-malware software etc all without the knowledge of flow, modifying legitimate code to force a call to rootkit code the user. This paper will demonstrate the steps required in order or by using DKOM (Direct Kernel Object Manipulation) [9] to create two rootkits. We will demonstrate that by [10][11][12][13]. Rootkits are designed to fundamentally implementing rootkits or any other type of malware a researcher subvert the OS kernel and are capable of obtaining and will be able to better understand the techniques and maintaining unrestricted control and access within the vulnerabilities used by an attacker. Such information could then compromised system without even being detected by anti- be useful when implementing anti-malware techniques. -
Microsoft Security Intelligence Report
Microsoft Security Intelligence Report Volume 20 | July through December, 2015 Norway This document is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED, OR STATUTORY, AS TO THE INFORMATION IN THIS DOCUMENT. This document is provided “as-is.” Information and views expressed in this document, including URL and other Internet Web site references, may change without notice. You bear the risk of using it. Copyright © 2016 Microsoft Corporation. All rights reserved. The names of actual companies and products mentioned herein may be the trademarks of their respective owners. 2 NORWAY Norway The statistics presented here are generated by Microsoft security programs and services running on computers in Norway in 4Q15 and previous quarters. This data is provided from administrators or users who choose to opt in to provide data to Microsoft, using IP address geolocation to determine country or region. On computers running real-time security software, most attempts by malware to infect computers are blocked before they succeed. Therefore, for a comprehensive understanding of the malware landscape, it’s important to consider infection attempts that are blocked as well as infections that are removed. For this reason, Microsoft uses two different metrics to measure malware prevalence: Encounter rate is simply the percentage of computers running Microsoft real-time security products that report a malware encounter, whether the infection attempt succeds or not. Computers cleaned per mille, or CCM, is an infection rate metric that is defined as the number of computers cleaned for every 1,000 unique computers executing the Malicious Software Removal Tool (MSRT), a free tool distributed through Microsoft update services that removes more than 200 highly prevalent or serious threats from computers. -
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.