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Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cpe.4023openalex-snapshot
Bypassing system calls–based intrusion detection systems
inferredcites →https://doi.org/10.1007/978-3-030-00470-5_23openalex-snapshot
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
inferredcites →https://doi.org/10.1007/978-3-030-22479-0_5openalex-snapshot
Adversarial Sampling Attacks Against Phishing Detection
inferredcites →https://doi.org/10.1007/978-3-031-05981-0_7openalex-snapshot
IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection
inferredcites →https://doi.org/10.1007/978-3-319-08509-8_11openalex-snapshot
Phoenix: DGA-Based Botnet Tracking and Intelligence
inferredcites →https://doi.org/10.1007/978-3-319-28166-7_24openalex-snapshot
Replacement Attacks: Automatically Impeding Behavior-Based Malware Specifications
inferredcites →https://doi.org/10.1007/978-3-319-59050-9_12openalex-snapshot
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
inferredcites →https://doi.org/10.1007/978-3-319-63387-9_5openalex-snapshot
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
inferredcites →https://doi.org/10.1007/978-3-319-66399-9_4openalex-snapshot
Adversarial Examples for Malware Detection
inferredcites →https://doi.org/10.1007/978-981-13-1059-1_17openalex-snapshot
Adversarial Attacks on SDN-Based Deep Learning IDS System
inferredcites →https://doi.org/10.1007/978-981-19-8991-9_29openalex-snapshot
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
inferredcites →https://doi.org/10.1007/s10579-008-9076-6openalex-snapshot
IEMOCAP: interactive emotional dyadic motion capture database
inferredcites →https://doi.org/10.1007/s10994-010-5188-5openalex-snapshot
The security of machine learning
inferredcites →https://doi.org/10.1016/j.cose.2017.11.007openalex-snapshot
Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach
inferredcites →https://doi.org/10.1016/j.eswa.2017.09.053openalex-snapshot
Quantifying the resilience of machine learning classifiers used for cyber security
inferredcites →https://doi.org/10.1016/j.future.2019.05.041openalex-snapshot
Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset
inferredcites →https://doi.org/10.1016/j.ins.2020.02.075openalex-snapshot
A feature-vector generative adversarial network for evading PDF malware classifiers
inferredcites →https://doi.org/10.1016/j.neucom.2018.02.007openalex-snapshot
Data driven exploratory attacks on black box classifiers in adversarial domains
inferredcites →https://doi.org/10.1016/j.patcog.2018.07.023openalex-snapshot
Wild patterns: Ten years after the rise of adversarial machine learning
inferredcites →https://doi.org/10.1109/78.650093openalex-snapshot
Bidirectional recurrent neural networks
inferredcites →https://doi.org/10.1109/access.2018.2807385openalex-snapshot
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
inferredcites →https://doi.org/10.1109/access.2018.2854599openalex-snapshot
Deep Learning-Based Intrusion Detection With Adversaries
inferredcites →https://doi.org/10.1109/access.2019.2909068openalex-snapshot
BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
inferredcites →https://doi.org/10.1109/access.2020.3020964openalex-snapshot
MaskDGA: An Evasion Attack Against DGA Classifiers and Adversarial Defenses
inferredcites →https://doi.org/10.1109/bigdata.2018.8622547openalex-snapshot
Phishing URL Detection with Oversampling based on Text Generative Adversarial Networks
inferredcites →https://doi.org/10.1109/btas46853.2019.9186006openalex-snapshot
Defending Against Adversarial Iris Examples UsingWavelet Decomposition
inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-snapshot
A detailed analysis of the KDD CUP 99 data set
inferredcites →https://doi.org/10.1109/cvpr.2014.244openalex-snapshot
Deep Learning Face Representation from Predicting 10,000 Classes
inferredcites →https://doi.org/10.1109/cvpr.2016.282openalex-snapshot
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00175openalex-snapshot
Robust Physical-World Attacks on Deep Learning Visual Classification
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00808openalex-snapshot
Global Texture Enhancement for Fake Face Detection in the Wild
inferredcites →https://doi.org/10.1109/dac.2018.8465773openalex-snapshot
Reverse Engineering Convolutional Neural Networks Through Side-channel Information Leaks
inferredcites →https://doi.org/10.1109/ecrime.2017.7945048openalex-snapshot
Classifying phishing URLs using recurrent neural networks
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/eurosp51992.2021.00024openalex-snapshot
Sponge Examples: Energy-Latency Attacks on Neural Networks
inferredcites →https://doi.org/10.1109/globecom38437.2019.9014337openalex-snapshot
Analyzing Adversarial Attacks against Deep Learning for Intrusion Detection in IoT Networks
inferredcites →https://doi.org/10.1109/icassp.2018.8462693openalex-snapshot
Fooling End-To-End Speaker Verification With Adversarial Examples
inferredcites →https://doi.org/10.1109/icb45273.2019.8987389openalex-snapshot
Adversarial Examples to Fool Iris Recognition Systems
inferredcites →https://doi.org/10.1109/icdcs.2019.00130openalex-snapshot
Adversarial Learning Attacks on Graph-based IoT Malware Detection Systems
inferredcites →https://doi.org/10.1109/icdm.2018.00088openalex-snapshot
Adversarially Learned Anomaly Detection
inferredcites →https://doi.org/10.1109/ijcnn.2005.1556215openalex-snapshot
Framewise phoneme classification with bidirectional LSTM networks
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9206780openalex-snapshot
Black Box Attacks on Explainable Artificial Intelligence(XAI) methods in Cyber Security
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9207168openalex-snapshot
Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability
inferredcites →https://doi.org/10.1109/indin.2018.8472060openalex-snapshot
Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems
inferredcites →https://doi.org/10.1109/inista.2018.8466271openalex-snapshot
Intrusion detection systems vulnerability on adversarial examples
inferredcites →https://doi.org/10.1109/lsp.2019.2906826openalex-snapshot
Adversarial Deep Learning in EEG Biometrics
inferredcites →https://doi.org/10.1109/malware.2015.7413680openalex-snapshot
Deep neural network based malware detection using two dimensional binary program features
inferredcites →https://doi.org/10.1109/milcom.2016.7795300openalex-snapshot
Crafting adversarial input sequences for recurrent neural networks
inferredcites →https://doi.org/10.1109/milcom.2018.8599759openalex-snapshot
Adversarial Examples Against the Deep Learning Based Network Intrusion Detection Systems
inferredcites →https://doi.org/10.1109/milcom.2018.8599855openalex-snapshot
Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Detection Models
inferredcites →https://doi.org/10.1109/sp.2014.20openalex-snapshot
Practical Evasion of a Learning-Based Classifier: A Case Study
inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/sp.2018.00038openalex-snapshot
Stealing Hyperparameters in Machine Learning
inferredcites →https://doi.org/10.1109/sp.2019.00031openalex-snapshot
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
inferredcites →https://doi.org/10.1109/sp40000.2020.00073openalex-snapshot
Intriguing Properties of Adversarial ML Attacks in the Problem Space
inferredcites →https://doi.org/10.1109/spw.2018.00016openalex-snapshot
Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
inferredcites →https://doi.org/10.1109/spw.2018.00027openalex-snapshot
Security Risks in Deep Learning Implementations
inferredcites →https://doi.org/10.1109/spw.2019.00015openalex-snapshot
Exploring Adversarial Examples in Malware Detection
inferredcites →https://doi.org/10.1109/ssci50451.2021.9660011openalex-snapshot
Rallying Adversarial Techniques against Deep Learning for Network Security
inferredcites →https://doi.org/10.1109/tifs.2019.2894031openalex-snapshot
Characterizing and Evaluating Adversarial Examples for Offline Handwritten Signature Verification
inferredcites →https://doi.org/10.1109/tkde.2013.57openalex-snapshot
Security Evaluation of Pattern Classifiers under Attack
inferredcites →https://doi.org/10.1109/tnet.2012.2184552openalex-snapshot
Detecting Algorithmically Generated Domain-Flux Attacks With DNS Traffic Analysis
inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2018.00079openalex-snapshot
A Malicious Attack on the Machine Learning Policy of a Robotic System
inferredcites →https://doi.org/10.1145/1879141.1879148openalex-snapshot
Detecting algorithmically generated malicious domain names
inferredcites →https://doi.org/10.1145/2699026.2699115openalex-snapshot
On the Character of Phishing URLs
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3092566openalex-snapshot
Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques
inferredcites →https://doi.org/10.1145/3128572.3140444openalex-snapshot
Adversarial Examples Are Not Easily Detected
inferredcites →https://doi.org/10.1145/3128572.3140451openalex-snapshot
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
inferredcites →https://doi.org/10.1145/3133956.3133978openalex-snapshot
Evading Classifiers by Morphing in the Dark
inferredcites →https://doi.org/10.1145/3243734.3264418openalex-snapshot
Wild patterns: Ten years after the rise of adversarial machine learning half-day tutorial
inferredcites →https://doi.org/10.1145/3264888.3264896openalex-snapshot
Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1145/3270101.3270111openalex-snapshot
Stochastic Substitute Training
inferredcites →https://doi.org/10.1145/3302504.3311814openalex-snapshot
Gray-box adversarial testing for control systems with machine learning components
inferredcites →https://doi.org/10.1145/3308897.3308959openalex-snapshot
Robust URL Classification With Generative Adversarial Networks
inferredcites →https://doi.org/10.1145/3320269.3384733openalex-snapshot
SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems
inferredcites →https://doi.org/10.1145/3339252.3339266openalex-snapshot
Black Box Attacks on Deep Anomaly Detectors
inferredcites →https://doi.org/10.1145/3341105.3373859openalex-snapshot
MANIS: evading malware detection system on graph structure
inferredcites →https://doi.org/10.1145/3375708.3380315openalex-snapshot
Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks
inferredcites →https://doi.org/10.1145/3427228.3427230openalex-snapshot
Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers
inferredcites →https://doi.org/10.1145/3433210.3437513openalex-snapshot
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems
inferredcites →https://doi.org/10.1201/9781351251389-8openalex-snapshot
Adversarial Examples in the Physical World
inferredcites →https://doi.org/10.14722/ndss.2014.23247openalex-snapshot
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
inferredcites →https://doi.org/10.14722/ndss.2016.23115openalex-snapshot
Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers
inferredcites →https://doi.org/10.14722/ndss.2018.23191openalex-snapshot
Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations
inferredcites →https://doi.org/10.14722/ndss.2018.23198openalex-snapshot
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
inferredcites →https://doi.org/10.14722/ndss.2018.23204openalex-snapshot
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
inferredcites →https://doi.org/10.14722/ndss.2018.23291openalex-snapshot
Trojaning Attack on Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v31i1.10804openalex-snapshot
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
inferredcites →https://doi.org/10.1609/aaai.v32i1.11302openalex-snapshot
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
inferredcites →https://doi.org/10.18653/v1/d18-1316openalex-snapshot
Generating Natural Language Adversarial Examples
inferredcites →https://doi.org/10.23919/eusipco.2018.8553214openalex-snapshot
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
inferredcites →https://doi.org/10.24963/ijcai.2018/524openalex-snapshot
Adversarial Regression for Detecting Attacks in Cyber-Physical Systems
inferredcites →https://doi.org/10.3115/v1/w14-4012openalex-snapshot
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
inferredcites →https://doi.org/10.3156/jsoft.29.5_177_2openalex-snapshot
GAN（Generative Adversarial Nets）
inferredcites →https://doi.org/10.3233/jcs-2010-0410openalex-snapshot
Automatic analysis of malware behavior using machine learning
inferredcites →https://doi.org/10.3390/app9050909openalex-snapshot
Review of Artificial Intelligence Adversarial Attack and Defense Technologies
inferredcites →https://doi.org/10.3390/bdcc3030043openalex-snapshot
RazorNet: Adversarial Training and Noise Training on a Deep Neural Network Fooled by a Shallow Neural Network
inferredcites →https://doi.org/10.3390/info10040122openalex-snapshot
A Survey of Deep Learning Methods for Cyber Security
inferredcites →https://doi.org/10.3390/s19040974openalex-snapshot
Adversarial Samples on Android Malware Detection Systems for IoT Systems
inferredcites →https://doi.org/10.48550/arxiv.1508.03096openalex-snapshot
Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features
inferredcites →https://doi.org/10.48550/arxiv.1605.07277openalex-snapshot
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
inferredcites →https://doi.org/10.48550/arxiv.1606.04435openalex-snapshot
Adversarial Perturbations Against Deep Neural Networks for Malware Classification
inferredcites →https://doi.org/10.48550/arxiv.1611.02770openalex-snapshot
Delving into Transferable Adversarial Examples and Black-box Attacks
inferredcites →https://doi.org/10.48550/arxiv.1701.04739openalex-snapshot
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1705.08131openalex-snapshot
Black-Box Attacks against RNN based Malware Detection Algorithms
inferredcites →https://doi.org/10.48550/arxiv.1709.06397openalex-snapshot
A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systems
inferredcites →https://doi.org/10.48550/arxiv.1711.03280openalex-snapshot
Crafting Adversarial Examples For Speech Paralinguistics Applications
inferredcites →https://doi.org/10.48550/arxiv.1712.05526openalex-snapshot
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
inferredcites →https://doi.org/10.48550/arxiv.1712.05919openalex-snapshot
Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Classification Models
inferredcites →https://doi.org/10.48550/arxiv.1712.09196openalex-snapshot
The Robust Manifold Defense: Adversarial Training using Generative Models
inferredcites →https://doi.org/10.48550/arxiv.1801.02384openalex-snapshot
Attacking Speaker Recognition With Deep Generative Models
inferredcites →https://doi.org/10.48550/arxiv.1801.08917openalex-snapshot
Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1802.00420openalex-snapshot
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1802.04528openalex-snapshot
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1804.04637openalex-snapshot
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
inferredcites →https://doi.org/10.48550/arxiv.1812.00151openalex-snapshot
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
inferredcites →https://doi.org/10.48550/arxiv.1812.11720openalex-snapshot
Stealing Neural Networks via Timing Side Channels
inferredcites →https://doi.org/10.48550/arxiv.1901.09963openalex-snapshot
Defense Methods Against Adversarial Examples for Recurrent Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1902.08909openalex-snapshot
MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses
inferredcites →https://doi.org/10.48550/arxiv.1905.02175openalex-snapshot
Adversarial Examples Are Not Bugs, They Are Features
inferredcites →https://doi.org/10.48550/arxiv.1907.01216openalex-snapshot
Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA
inferredcites →https://doi.org/10.5220/0006639801080116openalex-snapshot
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
inferredcites →https://doi.org/10.60882/cispa.24613341openalex-snapshot
Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Evaluation of neural networks defenses and attacks using NDCG and reciprocal rank metrics
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Deceiving supervised machine learning models via adversarial data poisoning attacks: a case study with USB keyboards
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Adversarial attack detection framework based on optimized weighted conditional stepwise adversarial network
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USB-GATE: USB-based GAN-augmented transformer reinforced defense framework for adversarial keystroke injection attacks
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Constrained optimization based adversarial example generation for transfer attacks in network intrusion detection systems
inferred← citeshttps://doi.org/10.1007/s12046-022-01980-6openalex-snapshot
Defense against adversarial malware using robust classifier: DAM-ROC
inferred← citeshttps://doi.org/10.1007/s12145-024-01369-5openalex-snapshot
Virtual monitoring method for computer network security in earth observation communication using multi ant colony random walk classification algorithm
inferred← citeshttps://doi.org/10.1007/s12243-023-00953-yopenalex-snapshot
Towards adversarial realism and robust learning for IoT intrusion detection and classification
inferred← citeshttps://doi.org/10.1007/s12243-024-01047-zopenalex-snapshot
Reliable feature selection for adversarially robust cyber-attack detection
inferred← citeshttps://doi.org/10.1007/s12525-025-00802-xopenalex-snapshot
An iterative five-phase process model to successfully implement AI for cybersecurity in a corporate environment
inferred← citeshttps://doi.org/10.1007/s13748-026-00448-6openalex-snapshot
Phishing in the age of distributed intelligence: taxonomies, detection strategies, and the emerging role of federated learning
inferred← citeshttps://doi.org/10.1007/s40031-026-01337-1openalex-snapshot
Adversarial Machine Learning Framework for Robust Banking Security: The Automated Cyber Threat Detection and Prevention (ACTP) Tool
inferred← citeshttps://doi.org/10.1007/s40745-022-00444-2openalex-snapshot
Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects
inferred← citeshttps://doi.org/10.1007/s41060-024-00655-1openalex-snapshot
Rethinking security: the resilience of shallow ML models
inferred← citeshttps://doi.org/10.1007/s41060-026-01183-wopenalex-snapshot
Common sense testing and sanity checks in machine learning
inferred← citeshttps://doi.org/10.1007/s42979-025-04332-3openalex-snapshot
Secure Data Retrieval and User Behavior Analysis in Social Media Using Blockchain Aware Privacy Preservation
inferred← citeshttps://doi.org/10.1007/s42979-026-05257-1openalex-snapshot
A Comprehensive Review on Adversarial Attacks and Detection Techniques in Deep Learning Models for Image Analysis
inferred← citeshttps://doi.org/10.1007/s43681-024-00427-4openalex-snapshot
Artificial intelligence (AI) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive AI
inferred← citeshttps://doi.org/10.1007/s43681-024-00588-2openalex-snapshot
AI versus AI for democracy: exploring the potential of adversarial machine learning to enhance privacy and deliberative decision-making in elections
inferred← citeshttps://doi.org/10.1007/s44196-024-00686-3openalex-snapshot
A Model for Estimating Resiliency of AI-Based Classifiers Defending Against Cyber Attacks
inferred← citeshttps://doi.org/10.1016/j.artint.2026.104606openalex-snapshot
Posterior-conditioned multi-agent reinforcement learning for adaptive cyber defense
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.112933openalex-snapshot
Automatic optimization for generating adversarial malware based on prioritized evolutionary computing
inferred← citeshttps://doi.org/10.1016/j.asoc.2026.114930openalex-snapshot
FRAME: Comprehensive risk assessment framework for adversarial machine learning threats
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Adversarial vulnerability of deep neural network-based gait event detection: A comparative study using accelerometer-based data
inferred← citeshttps://doi.org/10.1016/j.comcom.2022.06.012openalex-snapshot
Machine learning security attacks and defense approaches for emerging cyber physical applications: A comprehensive survey
inferred← citeshttps://doi.org/10.1016/j.comcom.2025.108265openalex-snapshot
Autonomous cyber defense for AIoT using Graph Attention Network-Enhanced reinforcement learning
inferred← citeshttps://doi.org/10.1016/j.comnet.2025.111331openalex-snapshot
Cyberattacks and defenses for Autonomous Navigation Systems: A systematic literature review
inferred← citeshttps://doi.org/10.1016/j.comnet.2025.111524openalex-snapshot
Towards effective black-box attacks on DoH tunnel detection systems
inferred← citeshttps://doi.org/10.1016/j.comnet.2025.111755openalex-snapshot
Adversarial attacks to latent representations of distributed neural networks in split computing
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Cyber risk quantification for adversarial machine learning attacks
inferred← citeshttps://doi.org/10.1016/j.cose.2022.102830openalex-snapshot
Robust stacking ensemble model for darknet traffic classification under adversarial settings
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103433openalex-snapshot
SoK: Realistic adversarial attacks and defenses for intelligent network intrusion detection
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103488openalex-snapshot
A Survey of PPG's Application in Authentication
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FLAD: Adaptive Federated Learning for DDoS attack detection
inferred← citeshttps://doi.org/10.1016/j.cose.2024.103853openalex-snapshot
Black-box adversarial transferability: An empirical study in cybersecurity perspective
inferred← citeshttps://doi.org/10.1016/j.cose.2024.103928openalex-snapshot
A sequential deep learning framework for a robust and resilient network intrusion detection system
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Defense strategies for Adversarial Machine Learning: A survey
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Security through the eyes of AI: How visualization is shaping malware detection
inferred← citeshttps://doi.org/10.1016/j.cosrev.2026.100963openalex-snapshot
Beyond vulnerabilities: A comprehensive survey of adversarial attacks across domains
inferred← citeshttps://doi.org/10.1016/j.esr.2024.101600openalex-snapshot
Integrating artificial intelligence in energy transition: A comprehensive review
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122223openalex-snapshot
Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.123567openalex-snapshot
Boosting robustness of network intrusion detection systems: A novel two phase defense strategy against untargeted white-box optimization adversarial attack
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.125646openalex-snapshot
On the local convergence of ADAM-DPGAN with simultaneous and alternating gradient decent training methods
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.133447openalex-snapshot
Adversarial machine learning defense in image based CAPTCHA systems
inferred← citeshttps://doi.org/10.1016/j.future.2021.12.013openalex-snapshot
Ensemble dynamic behavior detection method for adversarial malware
inferred← citeshttps://doi.org/10.1016/j.future.2023.10.011openalex-snapshot
Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification
inferred← citeshttps://doi.org/10.1016/j.future.2026.108857openalex-snapshot
Design and implementation of MLOps-based healthcare pipelines using the Cybersecurity Framework
inferred← citeshttps://doi.org/10.1016/j.icte.2024.05.007openalex-snapshot
Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects
inferred← citeshttps://doi.org/10.1016/j.icte.2025.06.010openalex-snapshot
Data-driven integrated sensing and communication: Recent advances, challenges, and future prospects
inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102303openalex-snapshot
Adversarial attacks and defenses in explainable artificial intelligence: A survey
inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102922openalex-snapshot
AI-driven fusion with cybersecurity: Exploring current trends, advanced techniques, future directions, and policy implications for evolving paradigms– A comprehensive review
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A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
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A survey of integrated multi-layer security for continuous-process industrial control systems: Insights from a steel manufacturing sector
inferred← citeshttps://doi.org/10.1016/j.jiixd.2024.02.001openalex-snapshot
A survey on membership inference attacks and defenses in machine learning
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Dual defense: Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks
inferred← citeshttps://doi.org/10.1016/j.jisa.2023.103472openalex-snapshot
Investigating on the robustness of flow-based intrusion detection system against adversarial samples using Generative Adversarial Networks
inferred← citeshttps://doi.org/10.1016/j.jisa.2023.103508openalex-snapshot
ELAMD: An ensemble learning framework for adversarial malware defense
inferred← citeshttps://doi.org/10.1016/j.jisa.2025.104215openalex-snapshot
Adversarial feature generation for ML-based intrusion detection in the petrochemical industry
inferred← citeshttps://doi.org/10.1016/j.jisa.2025.104269openalex-snapshot
Vulnerabilities in Machine Learning for cybersecurity: Current trends and future research directions
inferred← citeshttps://doi.org/10.1016/j.jnca.2023.103761openalex-snapshot
NLP methods in host-based intrusion detection systems: A systematic review and future directions
inferred← citeshttps://doi.org/10.1016/j.jnca.2024.104090openalex-snapshot
Adversarial machine learning threat analysis and remediation in Open Radio Access Network (O-RAN)
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113019openalex-snapshot
Social bot detection using variational generative adversarial networks with hidden Markov models in Twitter network
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.134490openalex-snapshot
Concept drift handling in security applications: A survey across unimodal to multimodal data
inferred← citeshttps://doi.org/10.1016/j.nlp.2024.100076openalex-snapshot
Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Position Paper
inferred← citeshttps://doi.org/10.1016/j.procs.2025.01.065openalex-snapshot
Guardians of Reliability, Robustness, and Resilience: Adversarial Maintenance in the Era of Industry 4.0 and 5.0
inferred← citeshttps://doi.org/10.1016/j.procs.2026.05.145openalex-snapshot
Comparative Analysis of Machine Learning Algorithms on Simulated Cyber Attacks Dataset
inferred← citeshttps://doi.org/10.1021/acs.nanolett.6c02614openalex-snapshot
Noise-Resilient Plasmonic Physical Unclonable Functions via Deep Contrastive Learning
inferred← citeshttps://doi.org/10.1038/s41746-023-00965-xopenalex-snapshot
Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
inferred← citeshttps://doi.org/10.1049/cmu2.70047openalex-snapshot
Mobile SDNs: Associating End‐User Commands with Network Flows in Android Devices
inferred← citeshttps://doi.org/10.1057/s41284-024-00435-3openalex-snapshot
Digital security by design
inferred← citeshttps://doi.org/10.1080/00051144.2024.2400640openalex-snapshot
Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training
inferred← citeshttps://doi.org/10.1080/17517575.2022.2098537openalex-snapshot
Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directions
inferred← citeshttps://doi.org/10.1088/1402-4896/ad92adopenalex-snapshot
Design of reconfigurable logic computing circuit for chaotic system
inferred← citeshttps://doi.org/10.1108/idd-01-2024-0003openalex-snapshot
A bibliometric study toward quantitative research assessment of security of machine learning
inferred← citeshttps://doi.org/10.1109/access.2021.3118642openalex-snapshot
Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis
inferred← citeshttps://doi.org/10.1109/access.2022.3179047openalex-snapshot
Hardware-Assisted Machine Learning in Resource-Constrained IoT Environments for Security: Review and Future Prospective
inferred← citeshttps://doi.org/10.1109/access.2022.3188649openalex-snapshot
Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades
inferred← citeshttps://doi.org/10.1109/access.2022.3205023openalex-snapshot
A Survey of Smart Home IoT Device Classification Using Machine Learning-Based Network Traffic Analysis
inferred← citeshttps://doi.org/10.1109/access.2023.3345000openalex-snapshot
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey
inferred← citeshttps://doi.org/10.1109/access.2024.3473531openalex-snapshot
Defending AI Models Against Adversarial Attacks in Smart Grids Using Deep Learning
inferred← citeshttps://doi.org/10.1109/access.2025.3572348openalex-snapshot
A Comprehensive Survey on AI in Counter-Terrorism and Cybersecurity: Challenges and Ethical Dimensions
inferred← citeshttps://doi.org/10.1109/access.2025.3600984openalex-snapshot
Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects
inferred← citeshttps://doi.org/10.1109/access.2026.3662155openalex-snapshot
Evaluating Manifold Learning Techniques for Dimensionality Reduction on Industrial IoT Cybersecurity Data
inferred← citeshttps://doi.org/10.1109/access.2026.3719291openalex-snapshot
A Configuration-Level Comparative Evaluation of Workflow Bundles for Source-Grounded Academic PDF Analysis
inferred← citeshttps://doi.org/10.1109/access.2026.3720554openalex-snapshot
Adversarial Evasion in Machine-Learning-Based Network Intrusion Detection: A Systematic Review, Threat Modeling, and Research Roadmap
inferred← citeshttps://doi.org/10.1109/access.2026.3733985openalex-snapshot
Adversarial Robustness of Deep Learning-Based Intrusion Detection Systems: A Systematic Literature Review of Attacks, Defenses, Datasets, and Open Challenges
inferred← citeshttps://doi.org/10.1109/aic57670.2023.10263830openalex-snapshot
Minimum Selection Feature Importance Guided Attack
inferred← citeshttps://doi.org/10.1109/bigdia63733.2024.10808651openalex-snapshot
A Survey of Recognition Model Attack Algorithms in Communication Countermeasure
inferred← citeshttps://doi.org/10.1109/cars67163.2025.11337828openalex-snapshot
Analyzing the Impact of Adversarial Examples on Explainable Machine Learning
inferred← citeshttps://doi.org/10.1109/ccgridw63211.2024.00022openalex-snapshot
Securing ML-based Android Malware Detectors: A Defensive Feature Selection Approach against Backdoor Attacks
inferred← citeshttps://doi.org/10.1109/cicn67655.2025.11368126openalex-snapshot
A Multi-Layer Security Framework for Adversarial SQL Injection in Machine Learning Systems
inferred← citeshttps://doi.org/10.1109/coins61597.2024.10622294openalex-snapshot
Investigating Vulnerabilities and Potential Security Threats in Current Synthetic Data Generation and Usage in the Automotive Domain
inferred← citeshttps://doi.org/10.1109/comst.2022.3225137openalex-snapshot
A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security
inferred← citeshttps://doi.org/10.1109/comst.2022.3233793openalex-snapshot
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey
inferred← citeshttps://doi.org/10.1109/comst.2023.3317242openalex-snapshot
Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
inferred← citeshttps://doi.org/10.1109/comst.2023.3344808openalex-snapshot
Evasion Attack and Defense on Machine Learning Models in Cyber-Physical Systems: A Survey
inferred← citeshttps://doi.org/10.1109/cscloud66326.2025.00053openalex-snapshot
Explainable AI-Enabled Intrusion Detection Systems for Computer Networks
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.00750openalex-snapshot
Boundary Unlearning: Rapid Forgetting of Deep Networks via Shifting the Decision Boundary
inferred← citeshttps://doi.org/10.1109/cyberscitech64112.2024.00028openalex-snapshot
Adversarial Evaluation of AI-Based Security Alert Screening Systems
inferred← citeshttps://doi.org/10.1109/dsn-w65791.2025.00062openalex-snapshot
ATTAQ: Adversarial Robustness of Quantum Machine Learning
inferred← citeshttps://doi.org/10.1109/esmarta70636.2026.11652256openalex-snapshot
Behavioral Memory-Artifact Deep Learning for Ransomware Detection with Confidence-Aware Automated Response
inferred← citeshttps://doi.org/10.1109/eurosp57164.2023.00022openalex-snapshot
SoK: Explainable Machine Learning for Computer Security Applications
inferred← citeshttps://doi.org/10.1109/eurospw61312.2024.00067openalex-snapshot
Position: The Explainability Paradox - Challenges for XAI in Malware Detection and Analysis
inferred← citeshttps://doi.org/10.1109/fit67061.2025.11333622openalex-snapshot
Android Malware Detection Against Adversarial Attacks Using Machine Learning
inferred← citeshttps://doi.org/10.1109/gast60528.2024.10520759openalex-snapshot
Detecting Cyber Threats and Enhancing Security in IOT Devices: A Federated Learning Approach
inferred← citeshttps://doi.org/10.1109/ic3i59117.2023.10398021openalex-snapshot
Retracted: BiLSTM Classifier: A New Approach for Detecting Cyber-Attacks in MITRE ATTACK Framework
inferred← citeshttps://doi.org/10.1109/icaiss58487.2023.10250526openalex-snapshot
Securing Logistic Regression Model from Data Poisoning Attack using AES and RSA Encryption Techniques
inferred← citeshttps://doi.org/10.1109/icc45041.2023.10279299openalex-snapshot
Adversarial Attack with Genetic Algorithm against IoT Malware Detectors
inferred← citeshttps://doi.org/10.1109/icccnt61001.2024.10726277openalex-snapshot
Machine Learning Vulnerabilities at the Edge: A Study of Multi-stage Gray-box Attacks*
inferred← citeshttps://doi.org/10.1109/iccsdfai70505.2026.11647913openalex-snapshot
Models and algorithms for protecting intrusion detection systems from attacks on machine learning components
inferred← citeshttps://doi.org/10.1109/iccsdfai70505.2026.11648029openalex-snapshot
Explainable Deep Learning for Behavioral Malware Classification: An Attention-Based Analysis of API Call Sequences
inferred← citeshttps://doi.org/10.1109/iccste65902.2025.11138352openalex-snapshot
CDAR-GNN: A Robust and Transferable Graph Neural Network for Malicious Domain Detection
inferred← citeshttps://doi.org/10.1109/iccv51070.2023.00421openalex-snapshot
AdvDiffuser: Natural Adversarial Example Synthesis with Diffusion Models
inferred← citeshttps://doi.org/10.1109/iccv51070.2023.00526openalex-snapshot
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness
inferred← citeshttps://doi.org/10.1109/icdate58146.2023.10248780openalex-snapshot
Offensive Artificial Intelligence: Current State of the Art and Future Directions
inferred← citeshttps://doi.org/10.1109/icdici62993.2024.10810927openalex-snapshot
Blockchain-Enhanced Adversarial Machine Learning for Fraud Detection and Claims Automation in the Insurance Sector
inferred← citeshttps://doi.org/10.1109/icdmw58026.2022.00080openalex-snapshot
Backdoor Poisoning of Encrypted Traffic Classifiers
inferred← citeshttps://doi.org/10.1109/icdsaai65575.2025.11011601openalex-snapshot
Automating Fake News Detection and Content Moderation Using Machine Learning: A Comprehensive Approach
inferred← citeshttps://doi.org/10.1109/icecie66637.2025.11363799openalex-snapshot
Hybrid Convolutional–Bidirectional LSTM Model for Intrusion Detection in Unmanned Aerial Vehicle Cyber-Physical Networks
inferred← citeshttps://doi.org/10.1109/icetci61221.2024.10594186openalex-snapshot
Adversarial Attacks and Defenses in Deep Learning-Based Computer Vision Systems
inferred← citeshttps://doi.org/10.1109/icmla58977.2023.00050openalex-snapshot
Robust Adversarial Defense by Tensor Factorization
inferred← citeshttps://doi.org/10.1109/icscss64956.2025.11501009openalex-snapshot
Integrating Machine Learning into Cyber Defense: Detection and Mitigation Approaches
inferred← citeshttps://doi.org/10.1109/iits69561.2026.11648229openalex-snapshot
Generating Realistic Adversarial Attack Samples Against Flow-Based AI-Driven IDSs Using Hybrid-Space Transformations
inferred← citeshttps://doi.org/10.1109/ijcnn54540.2023.10191429openalex-snapshot
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks
inferred← citeshttps://doi.org/10.1109/ispacs68724.2025.11383172openalex-snapshot
Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox
inferred← citeshttps://doi.org/10.1109/jbhi.2022.3192648openalex-snapshot
Edge Intelligence: Federated Learning-Based Privacy Protection Framework for Smart Healthcare Systems
inferred← citeshttps://doi.org/10.1109/jiot.2024.3349381openalex-snapshot
Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review
inferred← citeshttps://doi.org/10.1109/jiot.2024.3486659openalex-snapshot
NG-ICPS: Next Generation Industrial-CPS, Security Threats in the Era of Artificial Intelligence, and Open Challenges With Future Research Directions
inferred← citeshttps://doi.org/10.1109/lcn65610.2025.11146360openalex-snapshot
Implementing Practical Problem-Space Adversarial Attacks on Modern Network Intrusion Detection Systems
inferred← citeshttps://doi.org/10.1109/nca53618.2021.9685709openalex-snapshot
On the feasibility of adversarial machine learning in malware and network intrusion detection
inferred← citeshttps://doi.org/10.1109/ricai68060.2025.11384811openalex-snapshot
Threat Modeling and Prediction Based on Cryptographic Situation Awareness
inferred← citeshttps://doi.org/10.1109/rivf68649.2025.11365060openalex-snapshot
ASA-IDS: Attribution-Stealth Adversarial Attacks for Network Intrusion Detection Systems
inferred← citeshttps://doi.org/10.1109/rmkmate59243.2023.10369737openalex-snapshot
Defending Web Applications against Malicious Traffic: Leveraging Machine Learning for Enhanced Security
inferred← citeshttps://doi.org/10.1109/satml59370.2024.00027openalex-snapshot
Evading Black-box Classifiers Without Breaking Eggs
inferred← citeshttps://doi.org/10.1109/smartindustrycon65166.2025.10986131openalex-snapshot
A Technique for Protecting Machine Learning Components of Intrusion Detection Systems from Evasion Attacks
inferred← citeshttps://doi.org/10.1109/southeastcon63549.2026.11476200openalex-snapshot
A Systematic Evaluation of Data Poisoning Severity Under Label Flipping and Gradient Manipulation Based Attacks
inferred← citeshttps://doi.org/10.1109/southeastcon63549.2026.11476573openalex-snapshot
Understanding Stability of Choices: Toward Robust Choice-Based Authentication in Cybersecurity
inferred← citeshttps://doi.org/10.1109/tac.2022.3209959openalex-snapshot
Exploiting Partial Observability for Optimal Deception
inferred← citeshttps://doi.org/10.1109/tai.2025.3641908openalex-snapshot
Contrast Duality of Adversarial Learningin Network Intrusion: A Review
inferred← citeshttps://doi.org/10.1109/tap.2024.3396693openalex-snapshot
PT Symmetry-Enabled Physically Unclonable Functions for Anti-Counterfeiting RF Tags
inferred← citeshttps://doi.org/10.1109/tce.2025.3571352openalex-snapshot
Q-P2FL: Quantum-Enhanced Federated Edge Intelligence for Privacy-Preserving Adversarial Attack Detection on Consumer Edge Devices
inferred← citeshttps://doi.org/10.1109/tce.2026.3662480openalex-snapshot
Adversarial Attacks Resilient Machine Learning-Enabled Access Control Scheme for Consumer Electronics in IoT-Based Applications
inferred← citeshttps://doi.org/10.1109/tcss.2024.3415631openalex-snapshot
Incentive Mechanism Design Toward a Win–Win Situation for Generative Art Trainers and Artists
inferred← citeshttps://doi.org/10.1109/tdsc.2025.3542237openalex-snapshot
Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-Based Malware Detection
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Security Risks of AI/ML With a Focus on Reinforcement Learning: A Review and Perspectives From Grid Applications
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Adversarial XAI Methods in Cybersecurity
inferred← citeshttps://doi.org/10.1109/tifs.2024.3402439openalex-snapshot
RFG-HELAD: A Robust Fine-Grained Network Traffic Anomaly Detection Model Based on Heterogeneous Ensemble Learning
inferred← citeshttps://doi.org/10.1109/tifs.2024.3414339openalex-snapshot
MTDroid: A Moving Target Defense-Based Android Malware Detector Against Evasion Attacks
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DynPen: Automated Penetration Testing in Dynamic Network Scenarios Using Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/tifs.2025.3550070openalex-snapshot
Comparing Different Membership Inference Attacks With a Comprehensive Benchmark
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Securing Facial Bioinformation by Eliminating Adversarial Perturbations
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Differential Privacy Configurations in the Real World: A Comparative Analysis
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Evasion Attacks and Countermeasures in Deep Learning-Based Wi-Fi Gesture Recognition
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LAFIT: Efficient and Reliable Evaluation of Adversarial Defenses With Latent Features
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Analysis of Adversarial Training for Resilient Image Recognition Against Different Attacks
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Security Assurance in Modern IoT Systems
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Interconnected and resilient: A CGE analysis of AI‐driven cyberattacks in global trade
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Evading deep learning-based DGA detectors: current problems and solutions.
inferred← citeshttps://doi.org/10.1145/3469659openalex-snapshot
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
inferred← citeshttps://doi.org/10.1145/3523273openalex-snapshot
Membership Inference Attacks on Machine Learning: A Survey
inferred← citeshttps://doi.org/10.1145/3529757openalex-snapshot
A Survey on Data-driven Software Vulnerability Assessment and Prioritization
inferred← citeshttps://doi.org/10.1145/3547330openalex-snapshot
Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity
inferred← citeshttps://doi.org/10.1145/3589572.3589598openalex-snapshot
SARAF: Searching for Adversarial Robust Activation Functions
inferred← citeshttps://doi.org/10.1145/3597503.3639144openalex-snapshot
Investigating White-Box Attacks for On-Device Models
inferred← citeshttps://doi.org/10.1145/3600160.3605031openalex-snapshot
A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection
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