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  1. Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics (IWSPA '17)
  2. Analysis of Causative Attacks against SVMs Learning from Data Streams
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Non-interactive (t, n)-Incidence Counting from Differentially Private Indicator Vectors
Continuous Authentication Using Behavioral Biometrics
Tracing the Arc of Smartphone Application Security
What's in a URL: Fast Feature Extraction and Malicious URL Detection
An Internal/Insider Threat Score for Data Loss Prevention and Detection
Analysis of Causative Attacks against SVMs Learning from Data Streams
Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data
EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning
Model-based Cluster Analysis for Identifying Suspicious Activity Sequences in Software
MCDefender: Toward Effective Cyberbullying Defense in Mobile Online Social Networks
Feature Cultivation in Privileged Information-augmented Detection
Identifying Key Cyber-Physical Terrain

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Analysis of Causative Attacks against SVMs Learning from Data Streams

Content Provider ACM Digital Library
Author Burkard, Cody Lagesse, Brent
Abstract Machine learning algorithms have been proven to be vulnerable to a special type of attack in which an active adversary manipulates the training data of the algorithm in order to reach some desired goal. Although this type of attack has been proven in previous work, it has not been examined in the context of a data stream, and no work has been done to study a targeted version of the attack. Furthermore, current literature does not provide any metrics that allow a system to detect these attack while they are happening. In this work, we examine the targeted version of this attack on a Support Vector Machine(SVM) that is learning from a data stream, and examine the impact that this attack has on current metrics that are used to evaluate a models performance. We then propose a new metric for detecting these attacks, and compare its performance against current metrics.
Starting Page 31
Ending Page 36
Page Count 6
File Format PDF
ISBN 9781450349093
DOI 10.1145/3041008.3041012
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-03-24
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Support vector machines Causative attacks Adversarial machine learning Batch learning
Content Type Text
Resource Type Article
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