John P., Fang Yu, Yinglian Xie, Martin Abadi, Arvind Krishnamurthy University of California, Santa Cruz USENIX SECURITY SYMPOSIUM, August, 2010 John P.,

Slides:



Advertisements
Similar presentations
Incident Handling & Log Analysis in a Web Driven World Manindra Kishore.
Advertisements

Code-Red : a case study on the spread and victims of an Internet worm David Moore, Colleen Shannon, Jeffery Brown Jonghyun Kim.
Reporter: Jing Chiu Advisor: Yuh-Jye Lee /7/181Data Mining & Machine Learning Lab.
AUTOMATED DISCOVERY OF PARAMETER POLLUTION VULNERABILITIES IN WEB APPLICATIONS Marco Balduzzi, Carmen Torrano Gimenez, Davide Balzarotti, and Engin Kirda,
All Your Contacts Are Belong to Us: Automated Identity Theft Attacks on Social Networks Reporter : 鄭志欣 Advisor: Hsing-Kuo Pao Date : 2010/12/06 1.
WebGoat & WebScarab “What is computer security for $1000 Alex?”
Attacking Session Management Juliette Lessing
1 BotGraph: Large Scale Spamming Botnet Detection Yao Zhao EECS Department Northwestern University.
Detecting Botnets Using Hidden Markov Models on Network Traces Wade Gobel Bio-Grid, Summer 2008.
BotGraph: Large Scale Spamming Botnet Detection Yao Zhao Yinglian Xie *, Fang Yu *, Qifa Ke *, Yuan Yu *, Yan Chen and Eliot Gillum ‡ EECS Department,
Verma - ICISS 2014 R easoning M ining NLP Defense Rakesh M. Verma ReMiND Laboratory Catching Classical and Hijack-based Phishing Attacks.
Presenter Deddie Tjahjono.  Introduction  Website Application Layer  Why Web Application Security  Web Apps Security Scanner  About  Feature  How.
Norman SecureSurf Protect your users when surfing the Internet.
PROJECT IN COMPUTER SECURITY MONITORING BOTNETS FROM WITHIN FINAL PRESENTATION – SPRING 2012 Students: Shir Degani, Yuval Degani Supervisor: Amichai Shulman.
S PAMMING B OTNETS : S IGNATURES AND C HARACTERISTICS Introduction of AutoRE Framework.
Presentation by Kathleen Stoeckle All Your iFRAMEs Point to Us 17th USENIX Security Symposium (Security'08), San Jose, CA, 2008 Google Technical Report.
11 The Ghost In The Browser Analysis of Web-based Malware Reporter: 林佳宜 Advisor: Chun-Ying Huang /3/29.
資安新聞簡報 報告者:劉旭哲、曾家雄. Spam down, but malware up 報告者:劉旭哲.
Outline  Infections  1) r57 shell  2) rogue software  What Can We Do?  1) Seccheck  2) Virus total  3) Sandbox  Prevention  1) Personal Software.
GONE PHISHING ECE 4112 Final Lab Project Group #19 Enid Brown & Linda Larmore.
PhishNet: Predictive Blacklisting to Detect Phishing Attacks Pawan Prakash Manish Kumar Ramana Rao Kompella Minaxi Gupta Purdue University, Indiana University.
JOHN P. JOHN FANG YU YINGLIAN XIE MARTÍN ABADI ARVIND KRISHNAMURTHY PRESENTATION BY SAM KLOCK Searching the Searchers with SearchAudit.
Prevent Cross-Site Scripting (XSS) attack
+ Websites Vulnerabilities. + Content Expand of The Internet Use of the Internet Examples Importance of the Internet How to find Security Vulnerabilities.
BotNet Detection Techniques By Shreyas Sali
Authors: Gianluca Stringhini Christopher Kruegel Giovanni Vigna University of California, Santa Barbara Presenter: Justin Rhodes.
Niels Provos and Panayiotis Mavrommatis Google Google Inc. Moheeb Abu Rajab and Fabian Monrose Johns Hopkins University 17 th USENIX Security Symposium.
1 All Your iFRAMEs Point to Us Mike Burry. 2 Drive-by downloads Malicious code (typically Javascript) Downloaded without user interaction (automatic),
Network and Systems Security By, Vigya Sharma (2011MCS2564) FaisalAlam(2011MCS2608) DETECTING SPAMMERS ON SOCIAL NETWORKS.
Speaker:Chiang Hong-Ren Botnet Detection by Monitoring Group Activities in DNS Traffic.
Reporter: Li, Fong Ruei National Taiwan University of Science and Technology 9/19/2015Slide 1 (of 32)
11 CANTINA: A Content- Based Approach to Detecting Phishing Web Sites Reporter: Gia-Nan Gao Advisor: Chin-Laung Lei 2010/6/7.
PERSONALIZED SEARCH Ram Nithin Baalay. Personalized Search? Search Engine: A Vital Need Next level of Intelligent Information Retrieval. Retrieval of.
Detecting Semantic Cloaking on the Web Baoning Wu and Brian D. Davison Lehigh University, USA WWW 2006.
Cloak and Dagger: Dynamics of Web Search Cloaking David Y. Wang, Stefan Savage, and Geoffrey M. Voelker University of California, San Diego 左昌國 Seminar.
Web Application Security ECE ECE Internetwork Security What is a Web Application? An application generally comprised of a collection of scripts.
Click to edit Master title style Click to edit Master text styles –Second level Third level –Fourth level »Fifth level June 10 th, 2009Event details (title,
Automatically Generating Models for Botnet Detection Presenter: 葉倚任 Authors: Peter Wurzinger, Leyla Bilge, Thorsten Holz, Jan Goebel, Christopher Kruegel,
Web Attacks— Offense… The Whole Story Yuri & The Cheeseheads Mark Glubisz, Jason Kemble, Yuri Serdyuk, Kandyce Giordano.
Improving Cloaking Detection Using Search Query Popularity and Monetizability Kumar Chellapilla and David M Chickering Live Labs, Microsoft.
Not So Fast Flux Networks for Concealing Scam Servers Theodore O. Cochran; James Cannady, Ph.D. Risks and Security of Internet and Systems (CRiSIS), 2010.
Week 10-11c Attacks and Malware III. Remote Control Facility distinguishes a bot from a worm distinguishes a bot from a worm worm propagates itself and.
BotGraph: Large Scale Spamming Botnet Detection Yao Zhao, Yinglian Xie, Fang Yu, Qifa Ke, Yuan Yu, Yan Chen, and Eliot Gillum Speaker: 林佳宜.
By Gianluca Stringhini, Christopher Kruegel and Giovanni Vigna Presented By Awrad Mohammed Ali 1.
Spamming Botnets: Signatures and Characteristics Yinglian Xie, Fang Yu, Kannan Achan, Rina Panigrahy, Geoff Hulten, and Ivan Osipkov. SIGCOMM, Presented.
Spamming Botnets: Signatures and Characteristics Authors:Yinglian Xie, Fang Yu, Kannan Achan, Rina Panigrahy, Geoff Hulten+, Ivan Osipkov+ Presenter: Chia-Li.
SIMSWeb “Internet Remote Access” The most advanced central station software in the universe !
Studying Spamming Botnets Using Botlab
Search Worms, ACM Workshop on Recurring Malcode (WORM) 2006 N Provos, J McClain, K Wang Dhruv Sharma
The Koobface Botnet and the Rise of Social Malware Kurt Thomas David M. Nicol
Xinyu Xing, Wei Meng, Dan Doozan, Georgia Institute of Technology Alex C. Snoeren, UC San Diego Nick Feamster, and Wenke Lee, Georgia Institute of Technology.
What is Web Information retrieval from web Search Engine Web Crawler Web crawler policies Conclusion How does a web crawler work Synchronization Algorithms.
A Framework for Detection and Measurement of Phishing Attacks Reporter: Li, Fong Ruei National Taiwan University of Science and Technology 2/25/2016 Slide.
Spamming Botnets: Signatures and Characteristics Yinglian Xie, Fang Yu, Kannan Achan, Rina Panigrahy, Microsoft Research, Silicon Valley Geoff Hulten,
2009/6/221 BotMiner: Clustering Analysis of Network Traffic for Protocol- and Structure- Independent Botnet Detection Reporter : Fong-Ruei, Li Machine.
Fabricio Benevenuto, Gabriel Magno, Tiago Rodrigues, and Virgilio Almeida Universidade Federal de Minas Gerais Belo Horizonte, Brazil ACSAC 2010 Fabricio.
By Collin Donaldson. Hacking is only legal under the following circumstances: 1.You hack (penetration test) a device/network you own. 2.You gain explicit,
Unveiling Zeus Automated Classification of Malware Samples Abedelaziz Mohaisen Omar Alrawi Verisign Inc, VA, USA Verisign Labs, VA, USA
Heat-seeking Honeypots: Design and Experience John P. John, Fang Yu, Yinglian Xie, Arvind Krishnamurthy and Martin Abadi WWW 2011 Presented by Elias P.
DOWeR Detecting Outliers in Web Service Requests Master’s Presentation of Christian Blass.
Introduction SQL Injection is a very old security attack. It first came into existence in the early 1990's ex: ”Hackers” movie hero does SQL Injection.
Javascript worms By Benjamin Mossé SecPro
CSCE 548 Student Presentation Ryan Labrador
Chapter 7: Identifying Advanced Attacks
Worm Origin Identification Using Random Moonwalks
BotCatch: A Behavior and Signature Correlated Bot Detection Approach
De-anonymizing the Internet Using Unreliable IDs
De-anonymizing the Internet Using Unreliable IDs By Yinglian Xie, Fang Yu, and Martín Abadi Presented by Peng Cheng 03/22/2017.
Cybersecurity Simplified: Phishing
Presentation transcript:

John P., Fang Yu, Yinglian Xie, Martin Abadi, Arvind Krishnamurthy University of California, Santa Cruz USENIX SECURITY SYMPOSIUM, August, 2010 John P., Fang Yu, Yinglian Xie, Martin Abadi, Arvind Krishnamurthy University of California, Santa Cruz USENIX SECURITY SYMPOSIUM, August, 2010 A Presentation at Advanced Defense Lab

Outline Introduction Related Work Architecture Implementation – Stage 1 Implementation – Stage 2 Attack 1: Indentifying Vulnerable Web Sites Attack 2: Forum Spamming Attack 3: Windows Live Messenger Phishing Conclusion Advanced Defense Lab2

Introduction A framework that identifies malicious queries from massive search engine logs to uncover their relationship with potential attacks. Use a small set of malicious queries as seed, and generates regular expressions for detecting new malicious queries. Advanced Defense Lab3

Introduction Two stage: Identification Investigation SearchAudit identifies malicious queries. Analyzing those queries and the attacks of which they are part. Advanced Defense Lab4

Introduction Enhanced detection capability 400 becomes 4 million. Low false-positive rates. 2% Ability to detect new attacks Forum spaming Facilitation of attack analysis Analyze a series of phishing attacks that lasted for more than one year. Advanced Defense Lab5

Outline Introduction Related Work Architecture Implementation – Stage 1 Implementation – Stage 2 Attack 1: Indentifying Vulnerable Web Sites Attack 2: Forum Spamming Attack 3: Windows Live Messenger Phishing Conclusion Advanced Defense Lab6

Related Work Advanced Defense Lab7 There’s a significant amount of automated Web traffic on the Internet. Another research showed that more than 3% of the entire search traffic may be generated by stealthy search bots. What’s the motivation of those search bots? Search engine competitors Studying search quality Click fraud for monetary gain Spreading infection (MyDoom, Santy) Identifying victims

Related Work Advanced Defense Lab8 Using regular expression patterns Hon-eycomb Polygraph Hamsa AutoRE (A way to generate RE from another research)

Outline Introduction Related Work Architecture Implementation – Stage 1 Implementation – Stage 2 Attack 1: Indentifying Vulnerable Web Sites Attack 2: Forum Spamming Attack 3: Windows Live Messenger Phishing Conclusion Advanced Defense Lab9

Architecture Let attackers be our guides Follow their activities and predict their future attacks. Advanced Defense Lab10

Architecture Platform Dryad/DryadLINQ Query Expansion Taking a small set of seed queries and expand them Extract IPs and search again Regular Expression Generation Signature Generation (AutoRE)AutoRE Eliminating Redundancies Eliminating Proxies Advanced Defense Lab11

Arch. – Eliminating Redundancies Advanced Defense Lab12 Algorithm REGEX_CONSOLIDATE

Architecture – Eliminating Proxies Advanced Defense Lab13 Most users in a geographical region have similar query patterns. Mostly legitimate users’ queries will have a large overlap with the popular queries from the same /16 IP prefix. We label an IP as a proxy if K most popular queries from that IP and the K most popular queries from that prefix overlap in m queries. K = 100, m = 5

Outline Introduction Related Work Architecture Implementation – Stage 1 Implementation – Stage 2 Attack 1: Indentifying Vulnerable Web Sites Attack 2: Forum Spamming Attack 3: Windows Live Messenger Phishing Conclusion Advanced Defense Lab14

Data Description and Sys Setup Use 3 months of search logs from the Bing search engine.Bing search February 2009 (when it was known as Live Search) December 2009 January 2010 Each month of sampled data contains around 2 billion pageviews. The seed 500 malicious queries are obtained from a hacker Web site milw0rm.commilw0rm.com Takes about 7 hours to process the 1.2 TB of sampled data. Advanced Defense Lab15

Selection of RE Use Cookies to identify the malicious queries. Benign proxy are eliminated. Use a threshold to pick regular expressions based on their scores. Advanced Defense Lab16

Detection Results: Effect of Query Expansion and Regular Expression Matching Feed the 500 malicious queries into SearchAudit, we find that 122 of the 500 queries appear in the dataset. February 2009 dataset 174 IPs issued these queries Use the result to feed our system again 800 unique queries from 264 IPs Advanced Defense Lab17

Detection Results Advanced Defense Lab18

Effect of Incomplete Seeds Split the 122 seed queries into two sets 100 queries that were first posted on milw0rm.com before queries were posted in 2009 Advanced Defense Lab19

Looping Back Seed Queries Use derived RE as new seeds to feed back as an input to SearchAudit. Advanced Defense Lab20

Overall Matching Statistics Advanced Defense Lab21

Verification of Malicious Queries As we lack ground truth information about whether a query is malicious or not. Check whether the query is reported on any hacker Web sites Check query behavior whether the query matches individual bot or botnet features For each query q returned by SearchAudit Issue a query “q AND (dork OR vulnerability)” to search engine, and save the results. Advanced Defense Lab22

Verification of Queries Generated by Individual Bots Two features help us to distinguish bot queries from human queries Cookie: Most bot queries do not enable cookies, resulting in an empty cookie field. Normal users who do not clear their cookies, all the queries carry the old cookies. Link clicked Many bots do not click any link on the result page. Instead, they scrape the results off the page. Advanced Defense Lab23

Verification of Queries Generated by Individual Bots Advanced Defense Lab24

Verification of Queries Generated by Botnets If most of the IPs that issued malicious queries exhibit similar behavior, then it’s likely that all these IPs were running the same script. User agent Contains information about the browser and the version used Metadata Records certain metadata that comes with the request Pages per query Records the number of search result pages retrieved per query Inter-query interval Denotes the time between queries issued by the same IP Advanced Defense Lab25

Verification of Queries Generated by Botnets Advanced Defense Lab26

Verification of Queries Generated by Botnets Advanced Defense Lab27

Outline Introduction Related Work Architecture Implementation – Stage 1 Implementation – Stage 2 Attack 1: Indentifying Vulnerable Web Sites Attack 2: Forum Spamming Attack 3: Windows Live Messenger Phishing Conclusion Advanced Defense Lab28

Analysis of Detection Results Large countries such as USA, Russia, and China are responsible for almost half the IPs issuing malicious queries. Vulnerable Web Sites Try to exploit these web sites by SQL injection index.php?content=[ˆ?=#+;&:]{1,10} Try to find particular software with known vulnerabilities “Power by” Forum spamming “/includes/joomla.php” site:.[a-zA-Z]{2,3} Windows Live Messenger phishing Advanced Defense Lab29

Analysis of Detection Results Advanced Defense Lab30

Outline Introduction Related Work Architecture Implementation – Stage 1 Implementation – Stage 2 Attack 1: Indentifying Vulnerable Web Sites Attack 2: Forum Spamming Attack 3: Windows Live Messenger Phishing Conclusion Advanced Defense Lab31

Identifying Vulnerable Web Sites Applications of Vulnerability Searches Sample 5000 queries returned by SearchAudit. For every query q we issue a query “q –dork –vulnerability”. Obtain 80,490 URLs from 39,475 unique Web sites. Compare this list of random Web sites against a list of known phishing or malware sites. PhishTank Microsoft Test and show that many of these sites indeed have SQL injection vulnerabilities. Advanced Defense Lab32

Identifying Vulnerable Web Sites Advanced Defense Lab33

SQL Injection Vulnerabilities For the malicious queries, we look at the search results and crawl all of the links twice. First time, we crawl the link as is Second time, we add a single quote (‘) If the two pages are identical, then it suggests that there’s no obvious SQL injection vulnerability If the second page have any kind of SQL error, then there might exists an SQL injection vulnerability In 14,500 URLs, we find 1,760 URLs (12%) may have SQL injection vulnerability. Advanced Defense Lab34

Outline Introduction Related Work Architecture Implementation – Stage 1 Implementation – Stage 2 Attack 1: Indentifying Vulnerable Web Sites Attack 2: Forum Spamming Attack 3: Windows Live Messenger Phishing Conclusion Advanced Defense Lab35

Forum-Spamming Attacks We manually identified 46 REs that are associated with forum spamming. Advanced Defense Lab36

Advanced Defense Lab37

Forum-Spamming Attacks Advanced Defense Lab38

Apps of Forum Searching Queries Using Project Hony Pot to identify Web spammingProject Hony Pot Advanced Defense Lab39

Outline Introduction Related Work Architecture Implementation – Stage 1 Implementation – Stage 2 Attack 1: Indentifying Vulnerable Web Sites Attack 2: Forum Spamming Attack 3: Windows Live Messenger Phishing Conclusion Advanced Defense Lab40

Windows Live MSN Phishing What is a MSN Phishing ? / ?user=[a-zA-Z0-9._]* Advanced Defense Lab41

Windows Live MSN Phishing Advanced Defense Lab42

Characteristics of Compromised Accounts Advanced Defense Lab43

Outline Introduction Related Work Architecture Implementation – Stage 1 Implementation – Stage 2 Attack 1: Indentifying Vulnerable Web Sites Attack 2: Forum Spamming Attack 3: Windows Live Messenger Phishing Conclusion Advanced Defense Lab44

Conclusion Advanced Defense Lab45