Web Application Potentially Vulnerable to Clickjacking Solution
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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. -
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. -
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. -
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. -
Clickjacking: Attacks and Defenses
Clickjacking: Attacks and Defenses Lin-Shung Huang Alex Moshchuk Helen J. Wang Carnegie Mellon University Microsoft Research Microsoft Research [email protected] [email protected] [email protected] Stuart Schechter Collin Jackson Microsoft Research Carnegie Mellon University [email protected] [email protected] Abstract such as a “claim your free iPad” button. Hence, when Clickjacking attacks are an emerging threat on the web. the user “claims” a free iPad, a story appears in the user’s In this paper, we design new clickjacking attack variants Facebook friends’ news feed stating that she “likes” the using existing techniques and demonstrate that existing attacker web site. For ease of exposition, our description clickjacking defenses are insufficient. Our attacks show will be in the context of web browsers. Nevertheless, the that clickjacking can cause severe damages, including concepts and techniques described are generally applica- compromising a user’s private webcam, email or other ble to all client operating systems where display is shared private data, and web surfing anonymity. by mutually distrusting principals. We observe the root cause of clickjacking is that an Several clickjacking defenses have been proposed and attacker application presents a sensitive UI element of a deployed for web browsers, but all have shortcomings. target application out of context to a user (such as hiding Today’s most widely deployed defenses rely on frame- the sensitive UI by making it transparent), and hence the busting [21, 37], which disallows a sensitive page from user is tricked to act out of context. To address this root being framed (i.e., embedded within another web page). -
Cases of IP Address Spoofing and Web Spoofing —
3-3 StudiesonCountermeasuresforThwarting SpoofingAttacks—CasesofIPAddress SpoofingandWebSpoofing— MIYAMOTO Daisuke, HAZEYAMA Hiroaki, and KADOBAYASHI Youki This article intends to give case studies for of thwarting spoofing attack. Spoofing is widely used when attackers attempt to increase the success rate of their cybercrimes. In the context of Denial of Service (DoS) attacks, IP address spoofing is employed to camouflage the attackers’ location. In the context of social engineering, Web spoofing is used to persuade victims into giv- ing away personal information. A Web spoofing attacker creates a convincing but false copy of the legitimate enterprises’ web sites. The forged websites are also known as phishing sites. Our research group developed the algorithms, systems, and practices, all of which analyze cybercrimes that employ spoofing techniques. In order to thwart DoS attacks, we show the deployment scenario for IP traceback systems. IP traceback aims to locate attack source, regardless of the spoofed source IP addresses. Unfortunately, IP traceback requires that its sys- tems are widely deployed across the Internet. We argue the practical deployment scenario within Internets of China, Japan, and South Korea. We also develop a detection method for phishing sites. Currently, one of the most important research agenda to counter phishing is improving the accuracy for detecting phishing sites. Our approach, named HumanBoost, aims at improving the detection accuracy by utilizing Web users’ past trust decisions. Based on our subject experiments, we analyze the average detection accuracy of both HumanBoost and CANTINA. Keywords IP spoofing, Web spoofing, Internet emulation, IP traceback, Machine learning 1 Introduction measures. IP traceback aims to locate attack sources, regardless of the spoofed source IP DoS attacks exhaust the resources of addresses. -
A Case Study on Clickjacking Attack and Location Leakage
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 190 ISSN 2229-5518 A Case Study on Clickjacking Attack and Location Leakage Lim Chin Nei, Loo Yow Cherng, Manmeet Mahinderjit Singh Abstract— The advanced in technologies such as mobile devices, GSP, WIFI, and 3G has encourage the information sharing using social media. The large amount of information shared on social media often lead to security issues which are unaware by the users. This information may include sensitive information such as a person’s name, location, relationship status and many more and can be dangerous if is put in a wrong hand. In this research, we provide a review on the type of social media ad what are the current threat in social media. We then focus on two specific threat namely clickjacking and location leakage, and proposed a solution for each of it as well as the evaluation of the solution. Index Terms—Security; Social media; Attack; Defense Machine, Clickjacking, Location leakage —————————— —————————— 1 INTRODUCTION This rapid shift of technologies in this era of informa- media. With this large amount of information shared online, tion and communication has dramatically boosted the usage the social media actually has become a perfect platform for of the social media. With the advanced in technologies such hackers to find their victims. The information shared using as smart mobile devices, GPS, WIFI, and 3G services, user social media often contain sensitive and private information were able to connect to the internet regardless of time and which can be easily compromised by the hackers. -
Cisco 2017 Midyear Cybersecurity Report
Cisco 2017 Midyear Cybersecurity Report 1 Executive Summary Table of Contents Executive Summary ..........................................................03 Vulnerabilities update: Rise in attacks following key disclosures ................................................................ 47 Major Findings ..................................................................05 Don’t let DevOps technologies leave the Introduction ......................................................................07 business exposed ............................................................ 50 Attacker Behavior .............................................................09 Organizations not moving fast enough to patch Exploit kits: Down, but not likely out ................................. 09 known Memcached server vulnerabilities ......................... 54 How defender behavior can shift attackers’ focus ...........11 Malicious hackers head to the cloud to shorten the path to top targets ..................................................... 56 Web attack methods provide evidence of a mature Internet ............................................................. 12 Unmanaged infrastructure and endpoints leave organizations at risk ......................................................... 59 Web block activity around the globe ................................ 13 Security Challenges and Opportunities Spyware really is as bad as it sounds............................... 14 for Defenders ...................................................................61 -
Comparisons of Machine Learning Techniques for Detecting Malicious Webpages ⇑ H.B
Expert Systems with Applications 42 (2015) 1166–1177 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa Comparisons of machine learning techniques for detecting malicious webpages ⇑ H.B. Kazemian , S. Ahmed Intelligent Systems Research Centre, School of Computing, London Metropolitan University, United Kingdom article info abstract Article history: This paper compares machine learning techniques for detecting malicious webpages. The conventional Available online 16 September 2014 method of detecting malicious webpages is going through the black list and checking whether the web- pages are listed. Black list is a list of webpages which are classified as malicious from a user’s point of Keywords: view. These black lists are created by trusted organizations and volunteers. They are then used by modern K-Nearest Neighbor web browsers such as Chrome, Firefox, Internet Explorer, etc. However, black list is ineffective because of Support Vector Machine the frequent-changing nature of webpages, growing numbers of webpages that pose scalability issues Naive Bayes and the crawlers’ inability to visit intranet webpages that require computer operators to log in as authen- Affinity Propagation ticated users. In this paper therefore alternative and novel approaches are used by applying machine K-Means Supervised and unsupervised learning learning algorithms to detect malicious webpages. In this paper three supervised machine learning techniques such as K-Nearest Neighbor, Support Vector Machine and Naive Bayes Classifier, and two unsupervised machine learning techniques such as K-Means and Affinity Propagation are employed. Please note that K-Means and Affinity Propagation have not been applied to detection of malicious web- pages by other researchers. -
UI Redressing and Clickjacking: About Click Fraud and Data Theft
Introduction Attack vectors Counteractive measures Conclusion and outlook UI Redressing and Clickjacking: About click fraud and data theft Marcus Niemietz [email protected] Ruhr-University Bochum Chair for Network and Data Security 25th of November 2011 Marcus Niemietz, RUB @Zeronights UI Redressing and Clickjacking Introduction Attack vectors Counteractive measures Conclusion and outlook Short and crisp details about me Studying \IT-Security/Information Technology", RUB \Computer Science", Distance University Hagen B.Sc. in \IT-Security/Information Technology" Books Authentication Web Pages with Selenium ≥Feb. 2012: Clickjacking und UI-Redressing International speaker Work: RUB, Pixelboxx, ISP and IT-Security, Freelancer (trainings, penetration tests) Twitter: @mniemietz Marcus Niemietz, RUB @Zeronights UI Redressing and Clickjacking Introduction Attack vectors Counteractive measures Conclusion and outlook Contents 1 Introduction UI redressing Clickjacking 2 Attack vectors UI redressing Round up Clickjacking Tool 3 Counteractive measures Frame busting Busting frame busting Clickjacking statistics 4 Conclusion and outlook Marcus Niemietz, RUB @Zeronights UI Redressing and Clickjacking Introduction Attack vectors UI redressing Counteractive measures Clickjacking Conclusion and outlook Introduction Google Inc. can generate a profit of over $8.5 billion in 2010 Interesting for commercial companies to offer web applications shopping banking share status messages New attacks available that can bypass existing protection mechanisms CSRF