Clickjacking: Attacks and Defenses
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IYIR for HTML
INFOSEC UPDATE 2006 Student Workbook Norwich University June 19-20, 2006 M. E. Kabay, PhD, CISSP-ISSMP Assoc. Prof. Information Assurance Program Director, MSIA BSIA Division of Business Management Norwich University [email protected] Copyright © 2006 M. E. Kabay. All rights reserved. Page 1 INFOSEC UPDATE 2006 -- June 19-20, 2006 01 Introduction Category 01 Introduction 2006-06-12 Introduction M. E. Kabay, PhD, CISSP WELCOME Welcome to the 2005 edition of the Information Security Year in Review (IYIR) project. In 1993 and 1994, I was an adjunct professor in the Institute for Government Informatics Professionals in Ottawa, Canada under the aegis of the University of Ottawa. I taught a one-semester course introducting information security to government personnel and enjoyed the experience immensely. Many of the chapters of my 1996 textbook, _The NCSA Guide to Enterprise Security_ published by McGraw-Hill were field-tested by my students. In 1995, I was asked if I could run a seminar for graduates of my courses to bring them up to date on developments across the entire field of information security. Our course had twenty students and I so enjoyed it that I continued to develop the material and teach the course with the NCSA (National Computer Security Association; later called ICSA and then eventually renamed TruSecure Corporation and finally CyberTrust, its current name) all over the United States, Canada, Europe, Asia and the Caribbean. After a few years of working on this project, it became obvious that saving abstracts in a WordPerfect file was not going to cut it as an orderly method for organizing the increasing mass of information that I was encountering in my research. -
Clickjacking
Security Now! Transcript of Episode #168 Page 1 of 18 Transcript of Episode #168 ClickJacking Description: Steve and Leo discuss yet another challenge to surfing safely in the web world: Known as "ClickJacking," or more formally as "UI Redressing," this class of newly popular threats tricks web users into performing web-based actions they don't intend by leading them to believe they are doing something else entirely. High quality (64 kbps) mp3 audio file URL: http://media.GRC.com/sn/SN-168.mp3 Quarter size (16 kbps) mp3 audio file URL: http://media.GRC.com/sn/sn-168-lq.mp3 INTRO: Netcasts you love, from people you trust. This is TWiT. Leo Laporte: Bandwidth for Security Now! is provided by AOL Radio at AOL.com/podcasting. This is Security Now! with Steve Gibson, Episode 168 for October 30, 2008: Clickjacking. It's time for Security Now!, the show where we cover everything you'd ever want to know about security. And we do it with a guy who is the king, really, as far as I'm concerned, the man who discovered spyware, named spyware, wrote some of the most used security utilities out there, including ShieldsUP!, Mr. Steve Gibson of GRC.com. Hi, Steve. Steve Gibson: Yes, in fact sometimes we're discussing things that you'd rather wish weren't the case. I mean... Leo: Well, lately it's been kind of bleak because it's like, there's bad stuff, and there doesn't really seem like there's any cure. Steve: Yes, that's true. -
Predicting Search Task Difficulty
Predicting Search Task Difficulty Jaime Arguello University of North Carolina at Chapel Hill [email protected] Abstract. Search task difficulty refers to a user's assessment about the amount of effort required to complete a search task. Our goal in this work is to learn predictive models of search task difficulty. We evaluate features derived from the user's interaction with the search engine as well as features derived from the user's level of interest in the task and level of prior knowledge in the task domain. In addition to user-interaction fea- tures used in prior work, we evaluate features generated from scroll and mouse-movement events on the SERP. In some situations, we may prefer a system that can predict search task difficulty early in the search session. To this end, we evaluate features in terms of whole-session evidence and first-round evidence, which excludes all interactions starting with the second query. Our results found that the most predictive features were different for whole-session vs. first-round prediction, that mouseover fea- tures were effective for first-round prediction, and that level of interest and prior knowledge features did not improve performance. 1 Introduction Search engine users engage in a wide variety of search tasks. A large body of prior research focused on characterizing different types of search tasks (see Li and Belkin [10]). The motivation behind this prior work is to understand how task characteristics influence search behavior and how search systems can provide customized interactions for different task types. One important search task characteristic is search task difficulty. -
How to Analyze the Cyber Threat from Drones
C O R P O R A T I O N KATHARINA LEY BEST, JON SCHMID, SHANE TIERNEY, JALAL AWAN, NAHOM M. BEYENE, MAYNARD A. HOLLIDAY, RAZA KHAN, KAREN LEE How to Analyze the Cyber Threat from Drones Background, Analysis Frameworks, and Analysis Tools For more information on this publication, visit www.rand.org/t/RR2972 Library of Congress Cataloging-in-Publication Data is available for this publication. ISBN: 978-1-9774-0287-5 Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2020 RAND Corporation R® is a registered trademark. Cover design by Rick Penn-Kraus Cover images: drone, Kadmy - stock.adobe.com; data, Getty Images. Limited Print and Electronic Distribution Rights This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors. Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org Preface This report explores the security implications of the rapid growth in unmanned aerial systems (UAS), focusing specifically on current and future vulnerabilities. -
The Fancytooltips Package∗†
The fancytooltips package∗y Robert Maˇr´ık [email protected] May 4, 2009 1 Introduction The package fancytooltips is a package for LATEX. The pdf can be created by pdflatex or by latex + dvips + AdobeDistiller1 + Adobe Acrobat2. It allows to create tooltips in a similar way like cooltooltips package, but the tooltip is a page from another PDF file. In this way you can use mathematics, pictures and animations in your tooltips. The resulting PDF file can be used also with free Abobe Reader. The tooltips are activated by clicking the active area on the screen and deac- tivated after closing page or by moving mouse outside the link. You can try the links here (Einstein's formula) and also here (animation { numbers from 1 to 6). You have to use the free Adobe Reader or nonfree Adobe Acrobat to see the effect (xpdf, evince and others fail to work with JavaScripts). For more examples how the presentation may look like see the example.pdf and example-min.pdf files in the examples subdirectory. The buttons are created using eforms.sty which is a part of AcroTeX bundle. 2 Usage 2.1 The file with tooltips The file with tooltips is an ordinary pdf file, one tooltip per page, tooltips should be in the top right corner at the page, in a colored box and the rest of the page should be transparent. If you consider to use movetips option (see below), then every page should have the dimensions equal to the dimen- sions of the colored box with tooltip3. -
A Semi-Automated Security Advisory System to Resist Cyber-Attack in Social Networks
A Semi-Automated Security Advisory System to Resist Cyber-attack in Social Networks Samar Muslah Albladi and George R S Weir University of Strathclyde, Glasgow G1 1XH, UK {samar.albladi; george.weir}@strath.ac.uk Abstract. Social networking sites often witness various types of social engi- neering (SE) attacks. Yet, limited research has addressed the most severe types of social engineering in social networks (SNs). The present study in- vestigates the extent to which people respond differently to different types of attack in a social network context and how we can segment users based on their vulnerability. In turn, this leads to the prospect of a personalised security advisory system. 316 participants have completed an online-ques- tionnaire that includes a scenario-based experiment. The study result re- veals that people respond to cyber-attacks differently based on their de- mographics. Furthermore, people’s competence, social network experience, and their limited connections with strangers in social networks can decrease their likelihood of falling victim to some types of attacks more than others. Keywords: Advisory System, Social Engineering, Social Networks. 1 Introduction Individuals and organisations are becoming increasingly dependent on working with computers, accessing the World Wide Web and, more importantly, sharing data through virtual communication. This makes cyber-security one of today’s greatest issues. Pro- tecting people and organisations from being targeted by cybercriminals is becoming a priority for industry and academia [1]. This is due to the huge potential damage that could be associated with losing valuable data and documents in such attacks. Previous research focuses on identifying factors that influence people’s vulnerability to cyber-attack [2] as the human has been characterised as the weakest link in infor- mation security research. -
An Email Application with Active Spoof Monitoring and Control
2016 International Conference on Computer Communication and Informatics (ICCCI -2016), Jan. 07 – 09, 2016, Coimbatore, INDIA An Email Application with Active Spoof Monitoring and Control T.P. Fowdur, Member IEEE and L.Veerasoo [email protected] [email protected] Department of Electrical and Electronic Engineering University of Mauritius Mauritius Abstract- Spoofing is a serious security issue for email overview of some recent anti-spoofing mechanisms is now applications. Although several anti-email spoofing techniques presented have been developed, most of them do not provide users with sufficient control and information on spoof attacks. In this paper In [11], the authors proposed an anti-spoofing scheme for IP a web-based client oriented anti-spoofing email application is packets which provides an extended inter-domain packet filter proposed which actively detects, monitors and controls email architecture along with an algorithm for filter placement. A spoofing attacks. When the application detects a spoofed security key is first placed in the identification field of the IP message, it triggers an alert message and sends the spoofed header and a border router checks the key on the source message into a spoof filter. Moreover, the user who has received packet. If this key corresponds to the key of the target packet, the spoofed message is given the option of notifying the real sender of the spoofing attack. In this way an active spoof control the packet is considered valid, else it is flagged as a spoofed is achieved. The application is hosted using the HTTPS protocol packet. A Packet Resonance Strategy (PRS) which detects and uses notification messages that are sent in parallel with email different types of spoofing attacks that use up the resources of messages via a channel that has been secured by the Secure the server or commit data theft at a datacenter was proposed in Socket Layer (SSL) protocol. -
Financial Fraud and Internet Banking: Threats and Countermeasures
Report Financial Fraud and Internet Banking: Threats and Countermeasures By François Paget, McAfee® Avert® Labs Report Financial Fraud and Internet Banking: Threats and Countermeasures Table of Contents Some Figures 3 U.S. Federal Trade Commission Statistics 3 CyberSource 4 Internet Crime Complaint Center 4 In Europe 5 The Many Faces of Fraud 6 Small- and large-scale identity theft 7 Carding and skimming 8 Phishing and pharming 8 Crimeware 9 Money laundering 10 Mules 10 Virtual casinos 11 Pump and dump 12 Nigerian advance fee fraud (419 fraud) 12 Auctions 14 Online shopping 16 Anonymous payment methods 17 Protective Measures 18 Scoring 18 Europay, MasterCard, and Visa (EMV) standard 18 PCI-DSS 19 Secure Sockets Layer (SSL) and Transport Secured Layer (TLS) protocols 19 SSL extended validation 20 3-D Secure technology 21 Strong authentication and one-time password devices 22 Knowledge-based authentication 23 Email authentication 23 Conclusion 24 About McAfee, Inc. 26 Report Financial Fraud and Internet Banking: Threats and Countermeasures Financial fraud has many faces. Whether it involves swindling, debit or credit card fraud, real estate fraud, drug trafficking, identity theft, deceptive telemarketing, or money laundering, the goal of cybercriminals is to make as much money as possible within a short time and to do so inconspicuously. This paper will introduce you to an array of threats facing banks and their customers. It includes some statistics and descriptions of solutions that should give readers—whether they are responsible for security in a financial organization or a customer—an overview of the current situation. Some Figures U.S. -
Click Fraud,' Personal, Non-Commercial Use Only
April 6, 2005 PAGE ONE Traffic Jam DOW JONES REPRINTS This copy is for your In 'Click Fraud,' personal, non-commercial use only. To order presentation-ready copies Web Outfits Have for distribution to your colleagues, clients or customers, use the Order A Costly Problem Reprints tool at the bottom of any article or visit: www.djreprints.com. Marketers Worry About Bills Inflated by People Gaming • See a sample reprint in PDF format . The Search-Ad System • Order a reprint of this article now. Mr. McKelvey Turns Detective RELATED ARTICLE By KEVIN J. DELANEY • Internet Firms Face Legal Test on Staff Reporter of THE WALL STREET JOURNAL Advertising Fees3 April 6, 2005; Page A1 Nathan McKelvey began to worry about foul play when Yahoo Inc. refunded him $69.28 early last year. He grew more suspicious when a $16.91 refund arrived from Google Inc. The refunds were for "unusual clicks" and "invalid click activity" and they suggested someone was sabotaging Mr. McKelvey's advertising strategy. He pitches his charter-jet brokerage the way companies increasingly do: contracting with Yahoo and Google to serve up small text ads to anyone searching the Web using certain words, such as "private jet" or "air charter." He pays the search companies a fee every time someone clicks on his ads. But Yahoo and Google determined someone was clicking on CharterAuction.com Inc.'s ads with no intention of doing business, thus unfairly driving up the company's advertising costs. Mr. McKelvey, turning detective, combed through lists of "IP" addresses, the identifying codes supplied by computers when they access Web sites. -
Visualizing User Interface Events Event Stream Summarization Through Signs
Visualizing User Interface Events Event Stream Summarization through Signs Vagner Figueredo de Santana and Maria Cecília Calani Baranauskas Institute of Computing, University of Campinas (UNICAMP), Albert Einstein Av., 1251, Campinas, Brazil Keywords: Semiotics, User Interface Events, Visualization of Usage Data, Log Visualization, Event Icons, Icon Library, Image Library, User Interface Evaluation, Usability Evaluation, Accessibility Evaluation, Web Usage Mining. Abstract: Effective visual representation is related to how people interpret signs created to carry specific information. In the last years many user interface evaluation tools are considering detailed usage data to represent users’ actions. The volume of data gathered is leading developers to represent usage in a summarized way through graphical representations. If visual components used to represent complex data are not effective, then graphics used to summarize data may turn the interpretation of complex terms even harder. This work presents a study about graphical representations for user interface (UI) events and contributes with the validation of usage graph visualization and an open set of signs to support the summarization of client-side logs. The study involved 28 Information Technology specialists, potential users of UI evaluation tools. From the results one expects that evaluation tool developers, evaluators, and Web usage miners can reuse the validated usage graph representation and proposed set of signs to represent usage data in a summarized way. 1 INTRODUCTION these events is called, respectively, client-side logs and server-side logs (Santana and Baranauskas, The evaluation of user interface (UI) is a key task 2010a). when developing information systems and is part of In the last decade, website evaluation tools using a number of Software Engineering development server-side data (i.e., based on Web server logs) processes. -
GNSS Spoofing
COMPANY CONFIDENTIAL NLR-CR-2019-001-PT-1-RevEd-1 | June 2019 GNSS spoofing Revised Edition CUSTOMER: Agentschap Telecom NLR – Netherlands Aerospace Centre Netherlands Aerospace Centre NLR is a leading international research centre for aerospace. Bolstered by its multidisciplinary expertise and unrivalled research facilities, NLR provides innovative and integral solutions for the complex challenges in the aerospace sector. NLR's activities span the full spectrum of Research Development Test & Evaluation (RDT & E). Given NLR's specialist knowledge and facilities, companies turn to NLR for validation, verification, qualification, simulation and evaluation. NLR thereby bridges the gap between research and practical applications, while working for both government and industry at home and abroad. NLR stands for practical and innovative solutions, technical expertise and a long-term design vision. This allows NLR's cutting edge technology to find its way into successful aerospace programs of OEMs, including Airbus, Embraer and Pilatus. NLR contributes to (military) programs, such as ESA's IXV re-entry vehicle, the F-35, the Apache helicopter, and European programs, including SESAR and Clean Sky 2. Founded in 1919, and employing some 600 people, NLR achieved a turnover of 76 million euros in 2017, of which 81% derived from contract research, and the remaining from government funds. For more information visit: www.nlr.nl COMPANY CONFIDENTIAL NLR-CR-2019-001-PT-1-RevEd-1 | June 2019 GNSS spoofing Revised Edition CUSTOMER: Agentschap Telecom AUTHOR(S): J.J.P. van Es NLR J.D. van Bruggen-van Putten NLR H.D. Zelle NLR NLR - Netherlands Aerospace Centre June 2019 | NLR-CR-2019-001-PT-1-RevEd-1 COMPANY CONFIDENTIAL No part of this report may be reproduced and/or disclosed, in any form or by any means without the prior written permission of the owner. -
Pen Computer Technology
Pen Computer Technology Educates the reader about the technologies involved in a pen computer Fujitsu PC Corporation www.fujitsupc.com For more information: [email protected] © 2002 Fujitsu PC Corporation. All rights reserved. This paper is intended to educate the reader about the technologies involved in a pen computer. After reading this paper, the reader should be better equipped to make intelligent purchasing decisions about pen computers. Types of Pen Computers In this white paper, "pen computer" refers to a portable computer that supports a pen as a user interface device, and whose LCD screen measures at least six inches diagonally. This product definition encompasses five generally recognized categories of standard products, listed in Table 1 below. PRODUCT TARGET PC USER STORAGE OPERATING RUNS LOCAL EXAMPLE CATEGORY MARKET INTERFACE SYSTEM PROGRAMS Webpad Consumer & No Standard Flash Windows CE, Only via Honeywell Enterprise browser memory Linux, QNX browser WebPAD II plug-ins CE Tablet Enterprise No Specialized Flash Windows CE Yes Fujitsu applications memory PenCentra Pen Tablet Enterprise Yes Windows & Hard drive Windows 9x, Yes Fujitsu specialized NT-4, 2000, Stylistic applications XP Pen-Enabled Consumer Yes Windows Hard drive Windows 9x, Yes Fujitsu & Enterprise 2000, XP LifeBook B Series Tablet PC Consumer Yes Windows Hard drive Windows XP Yes Many under & Enterprise Tablet PC development Edition Table 1: Categories of Pen Computers with LCD Displays of Six Inches or Larger Since the different types of pen computers are often confused, the following paragraphs are intended to help explain the key distinguishing characteristics of each product category. Pen Computers Contrasted Webpad: A Webpad's primary characteristic is that its only user interface is a Web browser.