Emerging Threats and Attack Trends
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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. -
Synthesizing Near-Optimal Malware Specifications from Suspicious
Synthesizing Near-Optimal Malware Specifications from Suspicious Behaviors Somesh Jha∗, Matthew Fredrikson∗, Mihai Christodoresu†, Reiner Sailer‡, Xifeng Yan§ ∗University of Wisconsin–Madison, †Qualcomm Research Silicon Valley, ‡IBM T.J Watson Research Center, §University of California–Santa Barbara Abstract—Behavior-based detection techniques are a promis- and errors. ing solution to the problem of malware proliferation. However, We make the observation that behavioral specifications they require precise specifications of malicious behavior that do not result in an excessive number of false alarms, while still are best viewed as a form of discriminative specification.A remaining general enough to detect new variants before tradi- discriminative specification describes the unique properties tional signatures can be created and distributed. In this paper, of a given class, in contrast to the properties exhibited by we present an automatic technique for extracting optimally discriminative specifications a second mutually-exclusive class. This paper presents an , which uniquely identify a class automated technique that combines program analysis, graph of programs. Such a discriminative specification can be used by a behavior-based malware detector. Our technique, based mining, and stochastic optimization to synthesize malware on graph mining and stochastic optimization, scales to large behavior specifications. We represent program behaviors as classes of programs. When this work was originally published, graphs that are mined for discriminative patterns. As there the technique yielded favorable results on malware targeted are many ways in which malware can accomplish the same towards workstations (~86% detection rates on new malware). goal, we use these patterns as building blocks for construct- We believe that it can be brought to bear on emerging malware- based threats for new platforms, and discuss several promising ing discriminative specifications that are general across vari- avenues for future work in this direction. -
Cyber Warfare a “Nuclear Option”?
CYBER WARFARE A “NUCLEAR OPTION”? ANDREW F. KREPINEVICH CYBER WARFARE: A “NUCLEAR OPTION”? BY ANDREW KREPINEVICH 2012 © 2012 Center for Strategic and Budgetary Assessments. All rights reserved. About the Center for Strategic and Budgetary Assessments The Center for Strategic and Budgetary Assessments (CSBA) is an independent, nonpartisan policy research institute established to promote innovative thinking and debate about national security strategy and investment options. CSBA’s goal is to enable policymakers to make informed decisions on matters of strategy, secu- rity policy and resource allocation. CSBA provides timely, impartial, and insight- ful analyses to senior decision makers in the executive and legislative branches, as well as to the media and the broader national security community. CSBA encour- ages thoughtful participation in the development of national security strategy and policy, and in the allocation of scarce human and capital resources. CSBA’s analysis and outreach focus on key questions related to existing and emerging threats to US national security. Meeting these challenges will require transforming the national security establishment, and we are devoted to helping achieve this end. About the Author Dr. Andrew F. Krepinevich, Jr. is the President of the Center for Strategic and Budgetary Assessments, which he joined following a 21-year career in the U.S. Army. He has served in the Department of Defense’s Office of Net Assessment, on the personal staff of three secretaries of defense, the National Defense Panel, the Defense Science Board Task Force on Joint Experimentation, and the Defense Policy Board. He is the author of 7 Deadly Scenarios: A Military Futurist Explores War in the 21st Century and The Army and Vietnam. -
The Botnet Chronicles a Journey to Infamy
The Botnet Chronicles A Journey to Infamy Trend Micro, Incorporated Rik Ferguson Senior Security Advisor A Trend Micro White Paper I November 2010 The Botnet Chronicles A Journey to Infamy CONTENTS A Prelude to Evolution ....................................................................................................................4 The Botnet Saga Begins .................................................................................................................5 The Birth of Organized Crime .........................................................................................................7 The Security War Rages On ........................................................................................................... 8 Lost in the White Noise................................................................................................................. 10 Where Do We Go from Here? .......................................................................................................... 11 References ...................................................................................................................................... 12 2 WHITE PAPER I THE BOTNET CHRONICLES: A JOURNEY TO INFAMY The Botnet Chronicles A Journey to Infamy The botnet time line below shows a rundown of the botnets discussed in this white paper. Clicking each botnet’s name in blue will bring you to the page where it is described in more detail. To go back to the time line below from each page, click the ~ at the end of the section. 3 WHITE -
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. -
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 ......................................................................................................................................... -
Computer Security Fundamentals Third Edition
Computer Security Fundamentals Third Edition Chuck Easttom 800 East 96th Street, Indianapolis, Indiana 46240 USA Computer Security Fundamentals, Third Edition Executive Editor Brett Bartow Copyright © 2016 by Pearson Education, Inc. All rights reserved. No part of this book shall be reproduced, stored in a retrieval system, or Acquisitions Editor transmitted by any means, electronic, mechanical, photocopying, recording, or otherwise, Betsy Brown without written permission from the publisher. No patent liability is assumed with respect Development Editor to the use of the information contained herein. Although every precaution has been taken in Christopher Cleveland the preparation of this book, the publisher and author assume no responsibility for errors or omissions. Nor is any liability assumed for damages resulting from the use of the information Managing Editor contained herein. Sandra Schroeder ISBN-13: 978-0-7897-5746-3 ISBN-10: 0-7897-5746-X Senior Project Editor Tonya Simpson Library of Congress control number: 2016940227 Copy Editor Printed in the United States of America Gill Editorial Services First Printing: May 2016 Indexer Brad Herriman Trademarks All terms mentioned in this book that are known to be trademarks or service marks have Proofreader been appropriately capitalized. Pearson IT Certification cannot attest to the accuracy of this Paula Lowell information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark. Technical Editor Dr. Louay Karadsheh Warning and Disclaimer Publishing Coordinator Every effort has been made to make this book as complete and as accurate as possible, but Vanessa Evans no warranty or fitness is implied. -
Modeling of Computer Virus Spread and Its Application to Defense
University of Aizu, Graduation Thesis. March, 2005 s1090109 1 Modeling of Computer Virus Spread and Its Application to Defense Jun Shitozawa s1090109 Supervised by Hiroshi Toyoizumi Abstract 2 Two Systems The purpose of this paper is to model a computer virus 2.1 Content Filtering spread and evaluate content filtering and IP address blacklisting with a key parameter of the reaction time R. Content filtering is a containment system that has a We model the Sasser worm by using the Pure Birth pro- database of content signatures known to represent par- cess in this paper. Although our results require a short ticular worms. Packets containing one of these signa- reaction time, this paper is useful to obviate the outbreak tures are dropped when a containment system member of the new worms having high reproduction rate λ. receives the packets. This containment system is able to stop computer worm outbreaks immediately when the systems obtain information of content signatures. How- 1 Introduction ever, it takes too much time to create content signatures, and this system has no effect on polymorphic worms In recent years, new computer worms are being created at a rapid pace with around 5 new computer worms per a [10]. A polymorphic worm is one whose code is trans- day. Furthermore, the speed at which the new computer formed regularly, so no single signature identifies it. worms spread is amazing. For example, Symantec [5] 2.2 The IP Address Blacklisting received 12041 notifications of an infection by Sasser.B in 7 days. IP address blacklisting is a containment system that has Computer worms are a kind of computer virus. -
Executive Summary a High-Performance Preventive Security System Protects Your Network from Infections
A Multi-Layered Approach W H I T E P A P ER to Preventing Viruses Executive Summary A high-performance preventive security system protects your network from infections. As virus writers create increasingly sophisticated malicious code and find TABLE OF CONTENTS ever more effective methods to propagate, enterprises find themselves 1 Executive Summary scrambling to keep their networks, servers, and end-user computers safe from new threats. 2 Emerging Anti-Virus Strategies 3 A Comprehensive Approach Traditional anti-virus applications work by searching the contents of files Global Traffic Data 3 and looking for a recognized pattern of data (a “signature”) that is the virus Threat Operations Center 4 Dynamic Quarantining 4 program itself. However, virus writers have come up with various methods The Essence of Time 5 to escape detection by changing their programs, making it harder for virus 6 IronPort Systems Email Security scanners to recognize them as viruses. Today’s viruses are either polymor- Solutions phic or metamorphic and can actually change themselves as they propagate. 7 Appendix: The Sober Virus/ The increasing sophistication of malicious code is therefore making pattern Worm/Trojan recognition technologies less and less effective. This decline in effective virus defense is taking a toll on businesses. The often long delay between the time when a virus attack is launched and when a signature is available can result in hundreds of thousands of infected mes- sages being delivered to enterprise networks and communities of ISP users. Even when the end effects of the virus are minimal, such widespread infec- tion results in major costs. -
Net of the Living Dead: Bots, Botnets and Zombies
:: Net of the Living Dead: Bots, Botnets and Zombies Is your PC a zombie? Here’s how to avoid the attentions of blacklisters and vampire slayers. David Harley BA CISSP FBCS CITP Andrew Lee CISSP Cristian Borghello CISSP Table of Contents Introduction 2 Bots 3 Drones and Zombies 5 Day of the (Un)Dead 6 Botnet 7 Command and Control (C&C) 10 Dynamic DNS (DDNS) 11 Botnet Attacks 12 Self-Propagation 12 Spam Dissemination 12 Email Fraud 13 DoS and DDoS 13 Click Fraud 14 Miscellaneous Attacks 15 Meet the Bots 15 Bot/Botnet Detection 16 Conclusion 19 References 20 Glossary 22 White Paper: Net of the Living Dead: Bots, Botnets and Zombies 1 Introduction Organized crime long ago discovered the Internet’s profi t potential, and has succeeded not only in recruiting the necessary expertise to exploit that potential, but in capturing and subverting a signifi cant quantity of innocent Internet-attached systems and, in the process, acquiring the owners of those systems as unwitting accomplices. They have done this, almost exclusively, through the building of botnets. Most people will have heard references to bots and botnets, but few people actually understand them, what they do or what the scale of the problem is. It was, for instance, reported on June 13th 2007 by the Department of Justice and FBI with reference to “Operation Bot Roast” that over 1 million victim computer IP addresses were identifi ed.1 Craig Schiller and Jim Binkley2 refer to Botnets as “arguably the biggest threat that the web community has faced.” Exactly how big that problem is, it’s diffi cult to say. -
Breach Detection: What You Need to Know
Breach Detection: What you need to know. Detection and Stopping Advanced Attacks eBook Table Of Contents From Computer Games to War Games . .. 4 Digital Data: The New Competitive Advantage . 5 Warp Speed of Attack . 6 Targeting . .. 7 Penetration via Endpoints . 8 Reconnaissance. .. 9 Paths of Attack . 10 From Digits to Diamonds . 11 Exfiltration. 12 Persistence, Cleanup and Cover-up . 13 Conventional Defenses are too Slow . 14 Detection must be Automatic . 15 Cb Enterprise Protection Advanced Threat Indicators . 16 Get Ahead of Advanced Threats. .. 17 Breach Detection: What You Need To Know 2 eBook Introduction Today’s cyber attacks have changed in sophistication, in focus, and in their potential impact on your business . This eBook will outline the tactics today’s advanced attackers are using to break into your organization and why you require a defense-in-depth cybersecurity program that incorporates automatic detection and incident response . The goal of this ebook is to leave you with the knowledge you need to effectively protect your business against today’s advanced attacks . Who should read this ebook? • CISO/IT Prepare a business case for effective security solutions • CFO Understand the financial implications posed by advanced threats • CXO Answer the concerns of your board and stockholders Breach Detection: What You Need To Know 3 eBookWhitepaper From Computer Games to War Games Hacking used to be a game: an opportunity for the most clever and ambitious in the tech community to show off their skills and superiority — this was -
Faculty Research Profile (Publications, Books, Distinctions and Awards)
Charutar Vidya Mandal’s S. G. M. English Medium College of Commerce and Management (SEMCOM) Vallabh Vidyanagar Faculty Research Profile (Publications, Books, Distinctions and Awards) Index Sr. No. Name Page No. 1 Dr. Nikhil M Zaveri 1 2 Dr. Kamini Shah 4 3 Dr. Waheeda Sheikh 8 4 Mr. Bhupendra Patel 12 5 Dr. Nehal Daulatjada 13 6 Dr. Yashasvi Rajpara 17 7 Dr. Subhash Joshi 21 8 Dr. Sarvesh Trivedi 22 9 Dr. Preethi Luhana 24 10 Dr. Ankur Amin 26 11 Dr. Reena Dave 28 12 Dr. Swati Parab 30 13 Ms. Palak Patel 32 14 Ms. Harshida Patel 33 15 Ms. Nishrin Pathan 34 16 Ms. Joe Marry George 37 17 Mr. Nimesh Raval 39 18 Mr. Sunil Chaudhary 40 19 Dr. Ajayraj Vyas 46 20 Mr. Chetan Patel 49 21 Mr. Pratik Shah 50 22 Mr. Renil Thomas 52 23 Mr. Yogesh Patel 53 24 Ms. Komal Mistry 54 25 Mr. Jay Nanavati 56 26 Ms. Hiral Patel 57 27 Mr. Bharat Tarapara 58 28 Mr. Sarvesh Soni 59 29 Dr. Jaimin Trivedi 60 30 Dr. Kamlesh Vaishnav 62 Dr. Nikhil Zaveri Publications Books (edited) i. “The Era of Commerce and E-commerce (2014)”, published by Oxford Book Company, New Delhi (co-authored) – ISBN 978-93-5030-168-5 ii. “Quality Footprints : Sustainable Development of Higher Education Institutions (2014)”, published by SEMCOM, Vallabh Vidyanagar (co- authored) – ISBN 978-93-81286-27-2 Articles There are 52 articles published on various aspects of Management Education as Editorial in the e-newsletter “DRIVE”, published by SEMCOM, ISSN No.