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Title: Anomalous Example Detection in Deep Learning: A Survey

Journal Article · · IEEE Access
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [1];  [4]
  1. EECS Department, Syracuse University, Syracuse, NY, USA
  2. Lawrence Livermore National Laboratory, Livermore, CA, USA
  3. Computer Science Department, University of Illinois at Urbana--Champaign, Champaign IL, USA
  4. EECS Department, University of California at Berkeley, Berkeley, CA, USA
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Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1836335
Journal Information:
IEEE Access, Journal Name: IEEE Access Vol. 8; ISSN 2169-3536
Publisher:
Institute of Electrical and Electronics EngineersCopyright Statement
Country of Publication:
United States
Language:
English

References (44)

Convolutional neural networks for crowd behaviour analysis: a survey journal March 2018
A comparative evaluation of novelty detection algorithms for discrete sequences journal November 2019
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Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network journal April 2018
Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection journal December 2018
An overview of anomaly detection techniques: Existing solutions and latest technological trends journal August 2007
Sequence classification for credit-card fraud detection journal June 2018
Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders journal September 2018
Survey on Anomaly Detection using Data Mining Techniques journal January 2015
A review of novelty detection journal June 2014
A Survey of Outlier Detection Methodologies journal October 2004
A review of deep learning in the study of materials degradation journal November 2018
A guide to deep learning in healthcare journal January 2019
Deep Learning-Based Intrusion Detection With Adversaries journal January 2018
A Survey on Anomalies Detection Techniques and Measurement Methods conference November 2018
An Intrusion Detection Model Based on Deep Belief Networks conference November 2014
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges journal January 2020
Network Anomaly Detection with Stochastically Improved Autoencoder Based Models conference June 2017
Densely Connected Convolutional Networks conference July 2017
Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search conference June 2019
Finding Anomalies with Generative Adversarial Networks for a Patrolbot conference July 2017
Analyzing Adversarial Attacks against Deep Learning for Intrusion Detection in IoT Networks conference December 2019
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly conference October 2017
Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy conference October 2019
MuVAN: A Multi-view Attention Network for Multivariate Temporal Data conference November 2018
Adversarial Attacks on Deep Neural Networks for Time Series Classification conference July 2019
Collaborative Energy-Efficient Moving in Internet of Things: Genetic Fuzzy Tree Versus Neural Networks journal August 2019
UAV-Enabled Spatial Data Sampling in Large-Scale IoT Systems Using Denoising Autoencoder Neural Network journal April 2019
High-Accuracy Entity State Prediction Method Based on Deep Belief Network Toward IoT Search journal April 2019
Adversarial Examples Against the Deep Learning Based Network Intrusion Detection Systems conference October 2018
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch conference October 2019
Exploring Adversarial Examples in Malware Detection conference May 2019
Anomaly detection: A survey journal July 2009
Detecting adversarial example attacks to deep neural networks conference June 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
  • Carlini, Nicholas; Wagner, David
  • CCS '17: 2017 ACM SIGSAC Conference on Computer and Communications Security, Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security https://doi.org/10.1145/3128572.3140444
conference November 2017
Csi conference November 2017
A Survey on Anomaly detection in Evolving Data journal May 2018
Securing the Deep Fraud Detector in Large-Scale E-Commerce Platform via Adversarial Machine Learning Approach conference January 2019
Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders journal January 2017
A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection journal August 2018
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time journal August 2019
NIC: Detecting Adversarial Samples with Neural Network Invariant Checking conference January 2019
A comprehensive survey of anomaly detection in banking, wireless sensor networks, social networks, and healthcare journal May 2019
Wide Residual Networks conference January 2016

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