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  1. Proceedings of the 2015 ACM International Workshop on International Workshop on Security and Privacy Analytics (IWSPA '15)
  2. Deep Learning of Behaviors for Security
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Deep Learning of Behaviors for Security
Predicting Cyber Security Incidents Using Feature-Based Characterization of Network-Level Malicious Activities
Photo Forensics: There is More to a Picture Than Meets the Eye
Visualizing Traffic Causality for Analyzing Network Anomalies
Panel: Essential Data Analytics Knowledge forCyber-security Professionals and Students
Application-Specific Traffic Anomaly Detection Using Universal Background Model
Using Density Estimation to Detect Computer Intrusions
HRS: A Hybrid Framework for Malware Detection
Keystroke Active Authentications Based on Most Frequently Used Words
Towards Better Semi-Supervised Classification of Malicious Software

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Deep Learning of Behaviors for Security

Content Provider ACM Digital Library
Author Cybenko, George
Abstract Deep learning has generated much research and commercialization interest recently. In a way, it is the third incarnation of neural networks as pattern classifiers, using insightful algorithms and architectures that act as unsupervised auto-encoders which learn hierarchies of features in a dataset. After a short review of that work, we will discuss computational approaches for deep learning of behaviors as opposed to just static patterns. Our approach is based on structured non-negative matrix factorizations of matrices that encode observation frequencies of behaviors. Example security applications and covert channel detection and coding will be presented.
Starting Page 1
Ending Page 1
Page Count 1
File Format PDF
ISBN 9781450333412
DOI 10.1145/2713579.2713592
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-03-04
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Behaviors. Machines learning Security
Content Type Text
Resource Type Article
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