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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. -
HHS Lesson 8: Forensics
LESSON 8 DIGITAL FORENSICS AND COUNTER FORENSICS Lesson 8: Forensics WARNING The Hacker Highschool Project is a learning tool and as with any learning tool there are dangers. Some lessons, if abused, may result in physical injury. Some additional dangers may also exist where there is not enough research on possible effects of emanations from particular technologies. Students using these lessons should be supervised yet encouraged to learn, try, and do. However ISECOM cannot accept responsibility for how any information herein is abused. The following lessons and workbooks are open and publicly available under the following terms and conditions of ISECOM: All works in the Hacker Highschool Project are provided for non-commercial use with elementary school students, junior high school students, and high school students whether in a public institution, private institution, or a part of home-schooling. These materials may not be reproduced for sale in any form. The provision of any class, course, training, or camp with these materials for which a fee is charged is expressly forbidden without a license, including college classes, university classes, trade-school classes, summer or computer camps, and similar. To purchase a license, visit the LICENSE section of the HHS web page at http://www.hackerhighschool.org/licensing.html. The Hacker Highschool Project is an open community effort and if you find value in this project, we ask that you support us through the purchase of a license, a donation, or sponsorship. 2 Lesson 8: Forensics Table of -
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. -
Kali Linux Penetration Testing Bible
Telegram Channel : @IRFaraExam Table of Contents Cover Title Page Introduction What Does This Book Cover? Companion Download Files How to Contact the Publisher How to Contact the Author CHAPTER 1: Mastering the Terminal Window Kali Linux File System Managing Users and Groups in Kali Files and Folders Management in Kali Linux Remote Connections in Kali Kali Linux System Management Networking in Kali Linux Summary CHAPTER 2: Bash Scripting Basic Bash Scripting Printing to the Screen in Bash Variables Script Parameters User Input Functions Conditions and Loops Summary CHAPTER 3: Network Hosts Scanning Basics of Networking Network Scanning DNS Enumeration Telegram Channel : @IRFaraExam Summary CHAPTER 4: Internet Information Gathering Passive Footprinting and Reconnaissance Summary CHAPTER 5: Social Engineering Attacks Spear Phishing Attacks Payloads and Listeners Social Engineering with the USB Rubber Ducky Summary CHAPTER 6: Advanced Enumeration Phase Transfer Protocols E‐mail Protocols Database Protocols CI/CD Protocols Web Protocols 80/443 Graphical Remoting Protocols File Sharing Protocols Summary CHAPTER 7: Exploitation Phase Vulnerabilities Assessment Services Exploitation Summary CHAPTER 8: Web Application Vulnerabilities Web Application Vulnerabilities Summary CHAPTER 9: Web Penetration Testing and Secure Software Development Lifecycle Web Enumeration and Exploitation Secure Software Development Lifecycle Summary CHAPTER 10: Linux Privilege Escalation Telegram Channel : @IRFaraExam Introduction to Kernel Exploits and Missing Configurations -
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. -
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). -
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 -
Android Mobile Hacking Using Linux Arulpradeep S
S. P. Arulpradeep et al.; International Journal of Advance Research, Ideas and Innovations in Technology ISSN: 2454-132X Impact factor: 4.295 (Volume 5, Issue 2) Available online at: www.ijariit.com Android mobile hacking using Linux Arulpradeep S. P. Vinothkumar P. Nilavarasan G. S. [email protected] [email protected] [email protected] SRM Institute of Science and Technology, SRM Institute of Science and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu Ramapuram, Chennai, Tamil Nadu Ramapuram, Chennai, Tamil Nadu Sai Harshith Kumar S. Naveen P. [email protected] [email protected] SRM Institute of Science and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu Ramapuram, Chennai, Tamil Nadu ABSTRACT Backdoors are one of the most complicated types of Android malware. A normal backdoor carries out its functionalities such as installing itself into the system directory, disabling system apps, or gaining access to app’s data, to steal and upload sensitive info, download and ask to install applications and set up mobile botnets when setting proper Android permissions. This project focus on how Android devices are hacked using backdoors and how they can be stopped from doing so. The backdoor application when installed and turned on the mobile allows an attacker to read, write and modify the data. Due to Backdoor attacks Confidentiality, Integrity, and Accountability of the information security are lost. When the application is installed on the victim's mobile and the victim opens the application it creates the meter-preter session which permits the attacker to access functions like webcam, contacts, read SMS, send SMS, read call log, write call log, access storage, install applications. -
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 -
Scams and Spam to Avoid on Facebook
Security Response in association with: Scams and Spam to Avoid on Facebook Candid Wüest Principal Software Engineer foreword by the Facebook Security Team Foreword Contents At Facebook, we work hard to protect the people that use our service. We in- Foreword ............................................................ 1 vest in equipment, develop proprietary tools, create innovative security fea- Summary ............................................................ 2 tures and devote hundreds of employees around the world to keep you and Introduction ...................................................... 2 your data safe. But we can’t do this alone, which is why we worked on a guide Attack types ....................................................... 2 with our friends at Symantec to educate people on the most prevalent threats Like/share-baiting ........................................ 3 we are seeing and the best ways for users to keep their data safe while online. Like clickjacking ........................................... 4 We process a huge amount of content to protect the people on Facebook – Malicious (iFrame) application ................... 5 every single day we vet over 26 billion pieces of content and 2 trillion link Self-XSS copy/paste attacks ....................... 6 clicks. But with more than 800 million people visiting our site each month Tagging ......................................................... 8 and more than 400 million visiting every day, Facebook has become an Phishing ......................................................