Related references
Note: Only part of the references are listed.Adversarial Attacks on Deep-learning Models in Natural Language Processing: A Survey
Wei Emma Zhang et al.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2020)
NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion
Aritran Piplai et al.
2020 IEEE 6TH INT CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY) / 6TH IEEE INT CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING, (HPSC) / 5TH IEEE INT CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS) (2020)
Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs
Chaoyun Zhang et al.
IEEE ACCESS (2020)
Adversarial Examples: Attacks and Defenses for Deep Learning
Xiaoyong Yu et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)
One Pixel Attack for Fooling Deep Neural Networks
Jiawei Su et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)
Intrusion Detection Using Big Data and Deep Learning Techniques
Osama Faker et al.
PROCEEDINGS OF THE 2019 ANNUAL ACM SOUTHEAST CONFERENCE (ACMSE 2019) (2019)
Black Box Attacks on Deep Anomaly Detectors
Aditya Kuppa et al.
14TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY (ARES 2019) (2019)
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Kenneth T. Co et al.
PROCEEDINGS OF THE 2019 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'19) (2019)
Deep Learning Approach for Intelligent Intrusion Detection System
R. Vinayakumar et al.
IEEE ACCESS (2019)
Network Intrusion Detection: Based on Deep Hierarchical Network and original Flow Data
Yong Zhang et al.
IEEE ACCESS (2019)
PCCN: Parallel Cross Convolutional Neural Network for Abnormal Network Traffic Flows Detection in Multi-Class Imbalanced Network Traffic Flows
Yong Zhang et al.
IEEE ACCESS (2019)
Deep Learning in Mobile and Wireless Networking: A Survey
Chaoyun Zhang et al.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2019)
TR-IDS: Anomaly-Based Intrusion Detection through Text-Convolutional Neural Network and Random Forest
Erxue Min et al.
SECURITY AND COMMUNICATION NETWORKS (2018)
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Naveed Akhtar et al.
IEEE ACCESS (2018)
Deep Learning-Based Intrusion Detection with Adversaries
Zheng Wang
IEEE ACCESS (2018)
Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks
Chaoyun Zhang et al.
PROCEEDINGS OF THE 2018 THE NINETEENTH INTERNATIONAL SYMPOSIUM ON MOBILE AD HOC NETWORKING AND COMPUTING (MOBIHOC '18) (2018)
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
Chuanlong Yin et al.
IEEE ACCESS (2017)
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz et al.
COMPUTER AIDED VERIFICATION, CAV 2017, PT I (2017)
TensorLayer: A Versatile Library for Efficient Deep Learning Development
Hao Dong et al.
PROCEEDINGS OF THE 2017 ACM MULTIMEDIA CONFERENCE (MM'17) (2017)
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini et al.
2017 IEEE SYMPOSIUM ON SECURITY AND PRIVACY (SP) (2017)
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak et al.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2016)
Learning visual similarity for product design with convolutional neural networks
Sean Bell et al.
ACM TRANSACTIONS ON GRAPHICS (2015)
Share Paper
