A Hybrid Model to Detect Malicious Executables

Jan 13, 2013

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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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A Hybrid Model to Detect Malicious Executables

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

350 views • 22 slides

Pimp My PE: Parsing Malicious and Malformed Executables

Pimp My PE: Parsing Malicious and Malformed Executables. Virus Bulletin 2007. Authors. Sunbelt Software, Tampa FL Anti-Malware SDK team: Casey Sheehan, lead developer Nick Hnatiw, developer / researcher Tom Robinson, developer / researcher Nick Suan, developer / researcher. Purpose.

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A Dynamic Mixture Model to Detect Student Motivation and Proficiency

. Agenda. Problem StatementProposed ModelResultsConclusions and Future Work. Problem Statement. BackgroundDevelop a machine learning component for a geometry tutoring system used by high school students (SAT, MCAS)Focus on estimating the state" of a student, which is then used for selecting

374 views • 26 slides

Data Mining for Security Applications: Detecting Malicious Executables

Mr. Mehedy M. Masud (PhD Student) Prof. Latifur Khan Prof. Bhavani Thuraisingham Department of Computer Science The University of Texas at Dallas. Data Mining for Security Applications: Detecting Malicious Executables. Outline and Acknowledgement. Vision for Assured Information Sharing

632 views • 49 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?

219 views • 11 slides

TRANSITIONING TO A HYBRID MODEL

Kimberly Jansma , UCLA Laurence Denie , UCLA Margaret Ann Kassen , Catholic University of America. ACTFL 2011. TRANSITIONING TO A HYBRID MODEL. A REVOLUTION IN MEDIA REQUIRES RETHINKING HOW WE TEACH . The traditional classroom teaching model is under intense pressure to change. RadioLab.

467 views • 30 slides

Hybrid School Model

Hybrid School Model. Modern Schools for Modern Students. Market: Hybrid Learning Definition. Hybrid Model Sweet Spot. Market: Content vs Course. Need 90 – 100 hrs of instruction time. vs. A Course. Overview: Key Benefits. Traditional Classroom. Hybrid Rotation. Paperbased.

889 views • 9 slides

Detecting Malicious Executables

Mr. Mehedy Masud (PhD Student) Prof. Latifur Khan Prof. Bhavani Thuraisingham Department of Computer Science The University of Texas at Dallas Lecture#7. Detecting Malicious Executables. Introduction: Detecting Malicious Executables. What are malicious executables? Harm computer systems

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Shareable Executables

Shareable Executables. Often multiple programs share some code E.g., widely used libraries Do we need to load a different copy for each process? Not if all they’re doing is executing the code OS can load one copy and make it available to all processes that need it

265 views • 18 slides

A Hybrid Model of Distance Learning

A Hybrid Model of Distance Learning. Adam Kieffer & Julia Tabbut Hubbs Center for Lifelong Learning St. Paul ABE Summer Institute August 17, 2012. Hubbs Center (Midway/Frogtown neighborhood, St. Paul). Distance Learning Office. SPPS ABE/Hubbs Center Snapshot - 2011/12.

456 views • 29 slides

Towards a Hybrid Model

Towards a Hybrid Model. Provide a structure with building blocks Provide a programming and evaluation environment Invite researchers to evaluate and improve their algorithms Check performance improvements. Saliency and Visual Attention. Temporal Visual Attention. PVS. Image

272 views • 13 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

435 views • 25 slides

Hybrid equivalent model

Hybrid equivalent model. 5.5 The hybrid equivalent model Approximate model CE and CB 5.6 Hybrid π model High frequencies => Chapter 9 5.25 Complete hybrid equivalent model, including R S and R L Equations to determine: Ai, Av, Zi and Zo. 5.5 The hybrid equivalent model.

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Detect a Leak

Detect a Leak is a reputed company that is providing defect-free water leak detection services in Sydney. Our professionals lead effective leak detection in industrial as well as domestic buildings. We are also instrumental in finding shower leaks.

298 views • 6 slides

Decays in a Hybrid Baryon Model

Decays in a Hybrid Baryon Model. K. Khosonthongkee † , N. Supanam and Y. Yan. School of Physics, Suranaree University of Technology, Nakhon Ratchasima, Thailand. 18 th International IUPAP Conference on Few-Body Problems in Physics (FB18). Santos, São Paulo, Brazil August 24, 2006. Outline.

325 views • 21 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

823 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

208 views • 9 slides

Freeze-Out in a Hybrid Model

Freeze-Out in a Hybrid Model. Freeze-out Workshop, 6.5.09 Goethe-Universität Frankfurt Hannah Petersen. Outline. Short overview of the hybrid model Two freeze-out prescriptions Details about the implementation T and m B distributions Influence of transition criterion Multiplicities

465 views • 25 slides

A Dynamic Mixture Model to Detect Student Motivation and Proficiency

A Dynamic Mixture Model to Detect Student Motivation and Proficiency. Jeff Johns Autonomous Learning Laboratory. Beverly Woolf Center for Knowledge Communication. AAAI 7/20/2006. Agenda. Problem Statement Proposed Model Results Conclusions and Future Work. Problem Statement.

391 views • 26 slides

A Hybrid Model of Distance Learning

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A Hybrid Model to Detect Malicious Executables

A Hybrid Model to Detect Malicious Executables

Presentation Transcript

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

Pimp My PE: Parsing Malicious and Malformed Executables

A Dynamic Mixture Model to Detect Student Motivation and Proficiency

Data Mining for Security Applications: Detecting Malicious Executables

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

TRANSITIONING TO A HYBRID MODEL

Hybrid School Model

Detecting Malicious Executables

Shareable Executables

A Hybrid Model of Distance Learning

Towards a Hybrid Model

Learning to Detect and Classify Malicious Executables in the Wild

Hybrid equivalent model

Detect a Leak

Decays in a Hybrid Baryon Model

URLDoc: Learning to Detect Malicious URLs using Online Logistic Regression

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

Freeze-Out in a Hybrid Model

A Dynamic Mixture Model to Detect Student Motivation and Proficiency

A Hybrid Model of Distance Learning

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