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Adversarial Attacks on Deep Learning-Based Network Intrusion Detection Systems: A Taxonomy and Review

https://doi.org/10.2139/ssrn.4863302
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Gray-Box Adversarial Training
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Analyzing the Footprint of Classifiers in Adversarial Denial of Service Contexts
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Zeek-Osquery: Host-Network Correlation for Advanced Monitoring and Intrusion Detection
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Deep Neural Network Based Malicious Network Activity Detection Under Adversarial Machine Learning Attacks
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Advances in Password Recovery Using Generative Deep Learning Techniques
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Implementation-Oriented Feature Selection in UNSW-NB15 Intrusion Detection Dataset
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IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection
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Performance Analysis of NSL-KDD Dataset Using Classification Algorithms with Different Feature Selection Algorithms and Supervised Filter Discretization
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Statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection
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Deep Learning Models for Intrusion Detection in Wi-Fi Networks: A Literature Survey
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A Detailed Analysis of the CIDDS-001 and CICIDS-2017 Datasets
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Detect & Reject for Transferability of Black-Box Adversarial Attacks Against Network Intrusion Detection Systems
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Object-Aware Transfer-Based Black-Box Adversarial Attack on Object Detector
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Evaluation of adversarial machine learning tools for securing AI systems
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Classification performance improvement by enhancing the detection accuracy of DDOS attacks over flash crowd using CROSS GAN (XGAN)
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Secure and optimized intrusion detection scheme using LSTM-MAC principles for underwater wireless sensor networks
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The robust deep learning–based schemes for intrusion detection in Internet of Things environments
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Automatically synthesizing DoS attack traces using generative adversarial networks
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Intrusion Detection Systems: A State-of-the-Art Taxonomy and Survey
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Numerical Linear Algebra with Applications
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Semantics aware adversarial malware examples generation for black-box attacks
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Honey-block: Edge assisted ensemble learning model for intrusion detection and prevention using defense mechanism in IoT
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Toward a reliable anomaly-based intrusion detection in real-world environments
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Anomaly based network intrusion detection for IoT attacks using deep learning technique
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Your culture is in your password: An analysis of a demographically-diverse password dataset
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POBA-GA: Perturbation optimized black-box adversarial attacks via genetic algorithm
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Cybersecurity for autonomous vehicles: Review of attacks and defense
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Hardening machine learning denial of service (DoS) defences against adversarial attacks in IoT smart home networks
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Synthetic attack data generation model applying generative adversarial network for intrusion detection
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Stealthy and profitable data injection attack on real time electricity market with network model uncertainties
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Adversarial machine learning in Network Intrusion Detection Systems
inferredcites →https://doi.org/10.1016/j.eswa.2023.122198openalex-extracted
HDA-IDS: A Hybrid DoS Attacks Intrusion Detection System for IoT by using semi-supervised CL-GAN
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Internet of Things (IoT): A vision, architectural elements, and future directions
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Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset
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Managing a pool of rules for credit card fraud detection by a Game Theory based approach
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Defending network intrusion detection systems against adversarial evasion attacks
inferredcites →https://doi.org/10.1016/j.future.2021.06.047openalex-extracted
Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
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FGMD: A robust detector against adversarial attacks in the IoT network
inferredcites →https://doi.org/10.1016/j.ijin.2023.08.001openalex-extracted
Three-stage data generation algorithm for multiclass network intrusion detection with highly imbalanced dataset
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Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues
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Adversarial attacks on machine learning cybersecurity defences in Industrial Control Systems
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AB-FGSM: AdaBelief optimizer and FGSM-based approach to generate adversarial examples
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The security of machine learning in an adversarial setting: A survey
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A GAN-based security strategy for WSN networks based on honeypot algorithm
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attackGAN: Adversarial Attack against Black-box IDS using Generative Adversarial Networks
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An Efficient Deep Learning Approach To IoT Intrusion Detection
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Network intrusion detection
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Deep Learning-Based Intrusion Detection With Adversaries
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Deep Learning Approach for Intelligent Intrusion Detection System
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A Simple Recurrent Unit Model Based Intrusion Detection System With DCGAN
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Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review
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TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems
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Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset
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Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset
inferredcites →https://doi.org/10.1109/access.2022.3162827openalex-extracted
Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge
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A Secure Decentralized Event-Triggered Cooperative Localization in Multi-Robot Systems Under Cyber Attack
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Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network
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A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach
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The Big Four - What We Did Wrong in Advanced Persistent Threat Detection?
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Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives
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Enhancing Suricata intrusion detection system for cyber security in SCADA networks
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A Deep Reinforcement Learning Approach for Anomaly Network Intrusion Detection System
inferredcites →https://doi.org/10.1109/cns.2015.7346850openalex-extracted
Crowd-based detection of routing anomalies on the internet
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A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
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Network Intrusion Detection for IoT Security Based on Learning Techniques
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Deep Learning in Mobile and Wireless Networking: A Survey
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Machine Learning in IoT Security: Current Solutions and Future Challenges
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Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS
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High Performance WGAN-GP based Multiple-category Network Anomaly Classification System
inferredcites →https://doi.org/10.1109/csr51186.2021.9527967openalex-extracted
Detecting Adversarial DDoS Attacks in Software- Defined Networking Using Deep Learning Techniques and Adversarial Training
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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00108openalex-extracted
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00664openalex-extracted
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00891openalex-extracted
Improving the Transferability of Adversarial Samples with Adversarial Transformations
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01488openalex-extracted
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity
inferredcites →https://doi.org/10.1109/cybersa52016.2021.9478199openalex-extracted
Feature Vulnerability and Robustness Assessment against Adversarial Machine Learning Attacks
inferredcites →https://doi.org/10.1109/dasc.2009.94openalex-extracted
Intrusion Detection in the Cloud
inferredcites →https://doi.org/10.1109/dasc50938.2020.9256597openalex-extracted
Adversarial Attacks on AI based Intrusion Detection System for Heterogeneous Wireless Communications Networks
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Examining the Robustness of Learning-Based DDoS Detection in Software Defined Networks
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Anomaly Detection using Generative Adversarial Networks on Firewall Log Message Data
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-extracted
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/fg52635.2021.9667076openalex-extracted
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition
inferredcites →https://doi.org/10.1109/globecom38437.2019.9014102openalex-extracted
Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things
inferredcites →https://doi.org/10.1109/globecom42002.2020.9322377openalex-extracted
Enhancing Robustness of Malware Detection using Synthetically-Adversarial Samples
inferredcites →https://doi.org/10.1109/globecom42002.2020.9348244openalex-extracted
Generative Adversarial Network and Auto Encoder based Anomaly Detection in Distributed IoT Networks
inferredcites →https://doi.org/10.1109/ibigdelft.2018.8625291openalex-extracted
Evaluation of Security Information and Event Management Systems for Custom Security Visualization Generation
inferredcites →https://doi.org/10.1109/icbda55095.2022.9760353openalex-extracted
Defense against Adversarial Attacks Using Image Label and Pixel Guided Sparse Denoiser
inferredcites →https://doi.org/10.1109/icc40277.2020.9149117openalex-extracted
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization
inferredcites →https://doi.org/10.1109/icccn52240.2021.9522215openalex-extracted
Def-IDS: An Ensemble Defense Mechanism Against Adversarial Attacks for Deep Learning-based Network Intrusion Detection
inferredcites →https://doi.org/10.1109/iccv48922.2021.00741openalex-extracted
AdvDrop: Adversarial Attack to DNNs by Dropping Information
inferredcites →https://doi.org/10.1109/icpr48806.2021.9412832openalex-extracted
A Weak Coupling of Semi-Supervised Learning with Generative Adversarial Networks for Malware Classification
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Detecting Adversarial Examples for Network Intrusion Detection System with GAN
inferredcites →https://doi.org/10.1109/icspc51351.2021.9451786openalex-extracted
Design and Development of IOT Testbed with DDoS Attack for Cyber Security Research
inferredcites →https://doi.org/10.1109/icstcc.2017.8107046openalex-extracted
An efficient method in pre-processing phase of mining suspicious web crawlers
inferredcites →https://doi.org/10.1109/ictai50040.2020.00110openalex-extracted
Adversarial Attack against LSTM-based DDoS Intrusion Detection System
inferredcites →https://doi.org/10.1109/ictc46691.2019.8940025openalex-extracted
Opcode Sequence Amplifier using Sequence Generative Adversarial Networks
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9207627openalex-extracted
An Adversarial Attack Detection Paradigm With Swarm Optimization
inferredcites →https://doi.org/10.1109/ijcnn52387.2021.9534119openalex-extracted
Digital Watermark Perturbation for Adversarial Examples to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/ijcnn52387.2021.9534190openalex-extracted
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
inferredcites →https://doi.org/10.1109/ijcnn52387.2021.9534316openalex-extracted
Adaptive Adversarial Training for Meta Reinforcement Learning
inferredcites →https://doi.org/10.1109/isie.2019.8781458openalex-extracted
A Review of Artificial Intelligence Based Intrusion Detection for Software-Defined Wireless Sensor Networks
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Polymorphic Adversarial DDoS attack on IDS using GAN
inferredcites →https://doi.org/10.1109/isncc49221.2020.9297344openalex-extracted
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs
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FLOWGAN:Unbalanced Network Encrypted Traffic Identification Method Based on GAN
inferredcites →https://doi.org/10.1109/isqed57927.2023.10129311openalex-extracted
SpotOn: A Gradient-based Targeted Data Poisoning Attack on Deep Neural Networks
inferredcites →https://doi.org/10.1109/issc49989.2020.9180198openalex-extracted
Dynamic Countermeasure Knowledge for Intrusion Response Systems
inferredcites →https://doi.org/10.1109/itme53901.2021.00041openalex-extracted
Malware Identification Method Based on Image Analysis
inferredcites →https://doi.org/10.1109/itnec48623.2020.9085163openalex-extracted
ADGAN: A Scalable GAN-based Architecture for Image Anomaly Detection
inferredcites →https://doi.org/10.1109/iwcmc.2019.8766353openalex-extracted
Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems
inferredcites →https://doi.org/10.1109/jiot.2020.3048038openalex-extracted
Adversarial Attacks Against Network Intrusion Detection in IoT Systems
inferredcites →https://doi.org/10.1109/jiot.2021.3112159openalex-extracted
Intelligent Intrusion Detection for Internet of Things Security: A Deep Convolutional Generative Adversarial Network-Enabled Approach
inferredcites →https://doi.org/10.1109/jiot.2022.3211346openalex-extracted
An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks
inferredcites →https://doi.org/10.1109/jiot.2023.3236314openalex-extracted
Over-the-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting
inferredcites →https://doi.org/10.1109/lwc.2022.3207348openalex-extracted
Multiuser Adversarial Attack on Deep Learning for OFDM Detection
inferredcites →https://doi.org/10.1109/milcom.2018.8599759openalex-extracted
Adversarial Examples Against the Deep Learning Based Network Intrusion Detection Systems
inferredcites →https://doi.org/10.1109/mpel.2017.2719201openalex-extracted
Securing the Power Grid: Protecting Smart Grids and Connected Power Systems from Cyberattacks
inferredcites →https://doi.org/10.1109/netsoft51509.2021.9492526openalex-extracted
Mitigating Evasion Attacks on Machine Learning based NIDS Systems in SDN
inferredcites →https://doi.org/10.1109/nfv-sdn50289.2020.9289869openalex-extracted
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems
inferredcites →https://doi.org/10.1109/pst.2018.8514157openalex-extracted
GIDS: GAN based Intrusion Detection System for In-Vehicle Network
inferredcites →https://doi.org/10.1109/smc53992.2023.10393964openalex-extracted
Black-Box Targeted Adversarial Attack Based on Multi-Population Genetic Algorithm
inferredcites →https://doi.org/10.1109/sp.2017.41openalex-extracted
Membership Inference Attacks Against Machine Learning Models
inferredcites →https://doi.org/10.1109/sp.2019.00023openalex-extracted
DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model
inferredcites →https://doi.org/10.1109/sp40000.2020.00045openalex-extracted
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
inferredcites →https://doi.org/10.1109/spw59333.2023.00023openalex-extracted
Research Report: Synthesizing Intrusion Detection System Test Data from Open-Source Attack Signatures
inferredcites →https://doi.org/10.1109/tcsii.2020.3012005openalex-extracted
Side-Channel Gray-Box Attack for DNNs
inferredcites →https://doi.org/10.1109/tcyb.2018.2882908openalex-extracted
Adversarial Examples for Hamming Space Search
inferredcites →https://doi.org/10.1109/tdsc.2022.3148990openalex-extracted
MANDA: On Adversarial Example Detection for Network Intrusion Detection System
inferredcites →https://doi.org/10.1109/tetci.2019.2961157openalex-extracted
Hardening Random Forest Cyber Detectors Against Adversarial Attacks
inferredcites →https://doi.org/10.1109/tifs.2020.3047752openalex-extracted
Adversarial Adaptive Neighborhood With Feature Importance-Aware Convex Interpolation
inferredcites →https://doi.org/10.1109/tifs.2022.3201377openalex-extracted
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack
inferredcites →https://doi.org/10.1109/tip.2020.2975918openalex-extracted
Principal Component Adversarial Example
inferredcites →https://doi.org/10.1109/tip.2021.3050303openalex-extracted
Adversarial Attack Against Deep Saliency Models Powered by Non-Redundant Priors
inferredcites →https://doi.org/10.1109/tkde.2021.3078755openalex-extracted
Adversarial Attack on Large Scale Graph
inferredcites →https://doi.org/10.1109/tkde.2022.3153060openalex-extracted
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
inferredcites →https://doi.org/10.1109/tnnls.2023.3315414openalex-extracted
Gradient Correction for White-Box Adversarial Attacks
inferredcites →https://doi.org/10.1109/tnsm.2022.3164354openalex-extracted
Demystifying the Transferability of Adversarial Attacks in Computer Networks
inferredcites →https://doi.org/10.1109/tnsm.2023.3260039openalex-extracted
Unsupervised GAN-Based Intrusion Detection System Using Temporal Convolutional Networks and Self-Attention
inferredcites →https://doi.org/10.1109/tpami.2020.3033291openalex-extracted
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet
inferredcites →https://doi.org/10.1109/tr.2022.3204349openalex-extracted
Abnormal Traffic Detection: Traffic Feature Extraction and DAE-GAN With Efficient Data Augmentation
inferredcites →https://doi.org/10.1109/tvcg.2018.2864500openalex-extracted
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
inferredcites →https://doi.org/10.1109/twc.2021.3124855openalex-extracted
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
inferredcites →https://doi.org/10.1109/upec.2015.7339864openalex-extracted
Deploying trusted cloud computing for data intensive power system applications
inferredcites →https://doi.org/10.1109/wcnc45663.2020.9120472openalex-extracted
Robust Self-Protection Against Application-Layer (D)DoS Attacks in SDN Environment
inferredcites →https://doi.org/10.1109/wism.2009.109openalex-extracted
Hacking Risk Analysis of Web Trojan in Electric Power System
inferredcites →https://doi.org/10.1117/12.777341openalex-extracted
Usefulness of DARPA dataset for intrusion detection system evaluation
inferredcites →https://doi.org/10.1142/s2196888821500123openalex-extracted
IoT Botnet Detection Using Various One-Class Classifiers
inferredcites →https://doi.org/10.1145/3143361.3143393openalex-extracted
ZipNet-GAN
inferredcites →https://doi.org/10.1145/3339252.3339266openalex-extracted
Black Box Attacks on Deep Anomaly Detectors
inferredcites →https://doi.org/10.1145/3359992.3366642openalex-extracted
Towards Evaluation of NIDSs in Adversarial Setting
inferredcites →https://doi.org/10.1145/3374217openalex-extracted
Adversarial Attacks on Deep-learning Models in Natural Language Processing
inferredcites →https://doi.org/10.1145/3398394openalex-extracted
Adversarial Examples on Object Recognition
inferredcites →https://doi.org/10.1145/3400286.3418252openalex-extracted
Evading a Machine Learning-based Intrusion Detection System through Adversarial Perturbations
inferredcites →https://doi.org/10.1145/3411495.3421359openalex-extracted
Tiki-Taka
inferredcites →https://doi.org/10.1145/3425780openalex-extracted
The Creation and Detection of Deepfakes
inferredcites →https://doi.org/10.1145/3450268.3453533openalex-extracted
Sentinel
inferredcites →https://doi.org/10.1145/3460537.3460543openalex-extracted
Occupation mechanism for eliminating double-spending attacks on trusted transaction blockchain
inferredcites →https://doi.org/10.1145/3461462openalex-extracted
Methods for Host-based Intrusion Detection with Deep Learning
inferredcites →https://doi.org/10.1145/3511808.3557238openalex-extracted
Are Gradients on Graph Structure Reliable in Gray-box Attacks?
inferredcites →https://doi.org/10.1145/3538969.3539000openalex-extracted
Combining Variational Autoencoders and Transformer Language Models for Improved Password Generation
inferredcites →https://doi.org/10.1145/3546096.3546103openalex-extracted
The DARPA SEARCHLIGHT Dataset of Application Network Traffic
inferredcites →https://doi.org/10.1145/3568562.3568660openalex-extracted
Leveraging AI-Driven Realtime Intrusion Detection by Using WGAN and XGBoost
inferredcites →https://doi.org/10.1186/s12880-020-00530-yopenalex-extracted
Universal adversarial attacks on deep neural networks for medical image classification
inferredcites →https://doi.org/10.1186/s40537-020-00379-6openalex-extracted
Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset
inferredcites →https://doi.org/10.1186/s42400-020-00063-5openalex-extracted
On the combination of data augmentation method and gated convolution model for building effective and robust intrusion detection
inferredcites →https://doi.org/10.1186/s42400-021-00103-8openalex-extracted
Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique
inferredcites →https://doi.org/10.14722/ndss.2018.23204openalex-extracted
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
inferredcites →https://doi.org/10.18653/v1/2021.naacl-main.321openalex-extracted
Grey-box Adversarial Attack And Defence For Sentiment Classification
inferredcites →https://doi.org/10.21105/joss.02607openalex-extracted
Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX
inferredcites →https://doi.org/10.23919/cycon.2019.8756865openalex-extracted
Addressing Adversarial Attacks Against Security Systems Based on Machine Learning
inferredcites →https://doi.org/10.32604/cmc.2023.032220openalex-extracted
Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment
inferredcites →https://doi.org/10.3390/app9204221openalex-extracted
AE-CGAN Model based High Performance Network Intrusion Detection System
inferredcites →https://doi.org/10.3390/computers12120245openalex-extracted
Optimizing Intrusion Detection Systems in Three Phases on the CSE-CIC-IDS-2018 Dataset
inferredcites →https://doi.org/10.3390/computers9030058openalex-extracted
An Adversarial Approach for Intrusion Detection Systems Using Jacobian Saliency Map Attacks (JSMA) Algorithm
inferredcites →https://doi.org/10.3390/electronics10141633openalex-extracted
Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security
inferredcites →https://doi.org/10.3390/fi8030029openalex-extracted
Windows Based Data Sets for Evaluation of Robustness of Host Based Intrusion Detection Systems (IDS) to Zero-Day and Stealth Attacks
inferredcites →https://doi.org/10.3390/jcp1020014openalex-extracted
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
inferredcites →https://doi.org/10.3390/s20185305openalex-extracted
ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid
inferredcites →https://doi.org/10.3390/s23031315openalex-extracted
Development of a Machine-Learning Intrusion Detection System and Testing of Its Performance Using a Generative Adversarial Network
inferredcites →https://doi.org/10.3390/sym12040653openalex-extracted
AppCon: Mitigating Evasion Attacks to ML Cyber Detectors
inferredcites →https://doi.org/10.3390/sym13081453openalex-extracted
Network Intrusion Detection Based on an Efficient Neural Architecture Search
inferredcites →https://doi.org/10.4108/eai.10-1-2019.156245openalex-extracted
Improving Network Intrusion Detection Classifiers by Non-payload-Based Exploit-Independent Obfuscations: An Adversarial Approach
inferredcites →https://doi.org/10.4172/2168-9679.1000e101openalex-extracted
Numerical Linear Algebra in Applications
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-extracted
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1610.00768openalex-extracted
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
inferredcites →https://doi.org/10.48550/arxiv.1706.06083openalex-extracted
Towards Deep Learning Models Resistant to Adversarial Attacks
inferredcites →https://doi.org/10.48550/arxiv.1711.02846openalex-extracted
Intriguing Properties of Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1712.04248openalex-extracted
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
inferredcites →https://doi.org/10.48550/arxiv.1803.04311openalex-extracted
Deep Learning in Mobile and Wireless Networking: A Survey
inferredcites →https://doi.org/10.48550/arxiv.1804.08598openalex-extracted
Black-box Adversarial Attacks with Limited Queries and Information
inferredcites →https://doi.org/10.48550/arxiv.1805.08318openalex-extracted
Self-Attention Generative Adversarial Networks
inferredcites →https://doi.org/10.48550/arxiv.1805.09190openalex-extracted
Towards the first adversarially robust neural network model on MNIST
inferredcites →https://doi.org/10.48550/arxiv.1807.04457openalex-extracted
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach
inferredcites →https://doi.org/10.48550/arxiv.1810.00069openalex-extracted
Adversarial Attacks and Defences: A Survey
inferredcites →https://doi.org/10.48550/arxiv.1810.07339openalex-extracted
Security Matters: A Survey on Adversarial Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1810.10939openalex-extracted
Evading classifiers in discrete domains with provable optimality guarantees
inferredcites →https://doi.org/10.48550/arxiv.1901.06796openalex-extracted
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
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