Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs

Dec 13, 2012

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Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs

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Learning to Detect Phishing Emails

Learning to Detect Phishing Emails. Ian Fette Norman Sadeh Anthony Tomasic Presented by – Bhavin Madhani. Authors. Ian Fette : - Masters degree from Carnegie Mellon University. - Product Manager at Google - works on the Google Chrome team .

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A Hybrid Model to Detect Malicious Executables

Presentation Outline. OverviewBackgroundOur approachFeature descriptionFeature extractionExperimentsResultsConclusion. Overview. GoalDetecting Malicious ExecutablesContributionA new Model that combines Binary, Assembly, and Library Call featuresAn efficient technique to retrieve Assembly

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From PLATO to Web Based Learning

From PLATO to Web Based Learning. Walter Sobkiw President DV Chapter 2011 University of Pennsylvania November 08, 2011. Three Topics. Computer used for situational awareness Computer used for simulation training systems Computer used for education systems. The Systems Engineer. History

586 views • 42 slides

UNDERSTANDING BLACKLISTS

This happens when subscribers are no more interested in your marketing and report you as spam out of sheer annoyance.

157 views • 7 slides

Q&A for “Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs”

Q&A for “Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs”. Justin Ma, Lawrence Saul, Stefan Savage, Geoff Voelker KDD 2009 By Fu-Chi Ao. Questions. What’s the error rate? What are the relevant/dominant features out of the selected 30783 features?

226 views • 11 slides

Suspicious

Suspicious. By: Krysten Collins. “Be suspicious of anything that works perfectly- it’s probably because two errors are canceling each other out” - Dave Bartley. Su spi ci ous Definition- Distrustful Synonym- Doubtful, Cautious Antonym- Innocent, Trustworthy

199 views • 2 slides

Evolving from Web 1.0 to Web 2.0 and beyond

Evolving from Web 1.0 to Web 2.0 and beyond. Created by Brett Oppegaard for Washington State University's DTC 375 class, spring 2009. As our society has transcended from orality to literacy to mediacy.

188 views • 7 slides

Learning to Detect A Salient Object

Learning to Detect A Salient Object. Reporter: 鄭綱 (3/2) . Outline. Introduction CRF Formulation of Static Salient Object Strong Contrast Center-Surround Histogram Color Spatial-Distribution Learning & Inference for the Model Formulation of Dynamic Salient Object Results Conclusion

516 views • 30 slides

An ANN approach to identify malicious URLs

An ANN approach to identify malicious URLs. ECE 539 – Final Project Jayneel Gandhi. Motivation. Prevent users from visiting malicious webpage Lot of effort into reducing internet crimes Try to learn which URL is malicious from different sources

228 views • 8 slides

Suspicious

Suspicious. Synonym Jealous, skeptical , and wondering . Definition Tending habitually to suspect , esp. to suspect evi l. Antonym Believable, undoubted . Sentence the girl around the corner was very suspicious yesterday, when I went to the mall at 8:00. .

239 views • 1 slides

Malicious URLs

Malicious URLs. Files Removal -- Single Quotes Command = netscape ‘ www.nba.com ’ ;rm -f <FN> -- Back Ticks Command = netscape www.nba.com ` rm -f <FN> ` -- No Quotes or Ticks Command = netscape www.nba.com;rm -f <FN> Note: <FN> == Filename . Results. Single Quote

184 views • 6 slides

Learning to Detect and Classify Malicious Executables in the Wild

Learning to Detect and Classify Malicious Executables in the Wild. Reporter: 林佳宜 Email: M98570015@mail.ntou.edu.tw 2014/10/3. 1. 1. References. Learning to Detect and Classify Malicious Executables in the Wild . J. Zico Kolter, Marcus A. Maloof, JMLR 2006. 2. Outline. Introduction

446 views • 25 slides

Presentation URLs from Resource URLs

Presentation URLs from Resource URLs. Last updated Sep. 22, 2008. UI Client’s Problem. Clients obtain URLs to JTS data resources Directly Embedded as links in other data resources Clients know how to work with and present some data resources

389 views • 18 slides

URLDoc: Learning to Detect Malicious URLs using Online Logistic Regression

URLDoc: Learning to Detect Malicious URLs using Online Logistic Regression. Presented by : Mohammed Nazim Feroz 11/26/2013. Motivation. Web services drive new opportunities for people to interact, they also create new opportunities for criminals

850 views • 25 slides

Group Session: Malvertising : How To Detect and Deal With Malicious Ads

Group Session: Malvertising : How To Detect and Deal With Malicious Ads. Mike Nolet admonsters Ad Ops 360 July 17 th 2008. What are “Malvertisements”?. Malware Ads, or “Malvertisements”, when displayed attempt to install spyware or adware or otherwise hijack a user’s browsing session

220 views • 9 slides

Malicious Web Servers

Malicious Web Servers. Christian Seifert. IMT551 31 st October 2007. What are we dealing with?. Honeypot.

453 views • 33 slides

Learning to Detect A Salient Object

Learning to Detect A Salient Object. Reporter: 鄭綱 (3/2). Outline. Introduction CRF Formulation of Static Salient Object Strong Contrast Center-Surround Histogram Color Spatial-Distribution Learning & Inference for the Model Formulation of Dynamic Salient Object Results Conclusion

491 views • 30 slides

Semalt: Extracting URLs From Web Pages With Beautiful Soup

Semalt, semalt SEO, Semalt SEO Tips, Semalt Agency, Semalt SEO Agency, Semalt SEO services, web design, web development, site promotion, analytics, SMM, Digital marketing

53 views • 2 slides

Web Servers and URLs

Web Servers and URLs. And Domain Names. WWW vs. Internet. World Wide Web. Internet. Existed before the WWW Refers mostly to physical network, i.e., hardware, computers, etc. Network of Network concept Unique features TCP/IP protocol Packet Switching.

298 views • 25 slides

How to Remove the Date from WordPress URLs Quickly Beyond 2020?

WordPress has an SEO friendly URL structure, where numerous link formats can enable the exclusive WordPress customization options. It includes the ones that add dates to the WordPress post URLS. Non-readable link structure for page and post URLs are used by default, where post ID is added in the URL. Read More: https://bit.ly/2G1nlQL

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Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs

Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs

Presentation Transcript

Learning to Detect Phishing Emails

A Hybrid Model to Detect Malicious Executables

From PLATO to Web Based Learning

UNDERSTANDING BLACKLISTS

Q&amp;A for “Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs”

Suspicious

Evolving from Web 1.0 to Web 2.0 and beyond

Learning to Detect A Salient Object

An ANN approach to identify malicious URLs

Suspicious

Malicious URLs

Learning to Detect and Classify Malicious Executables in the Wild

Presentation URLs from Resource URLs

URLDoc: Learning to Detect Malicious URLs using Online Logistic Regression

Group Session: Malvertising : How To Detect and Deal With Malicious Ads

Malicious Web Servers

Learning to Detect A Salient Object

Semalt: Extracting URLs From Web Pages With Beautiful Soup

Web Servers and URLs

How to Remove the Date from WordPress URLs Quickly Beyond 2020?

Q&A for “Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs”