On the public citation graph
Adversarial classification
0 signed · 841 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4684-7862-4_22openalex-snapshot
Evolution and the Theory of Games
inferredcites →https://doi.org/10.1016/b978-1-55860-335-6.50027-1openalex-snapshot
Markov games as a framework for multi-agent reinforcement learning
inferredcites →https://doi.org/10.1016/s0004-3702(97)00043-xopenalex-snapshot
Wrappers for feature subset selection
inferredcites →https://doi.org/10.1017/cbo9780511806292openalex-snapshot
Evolution and the Theory of Games
inferredcites →https://doi.org/10.1023/a:1007413511361openalex-snapshot
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
inferredcites →https://doi.org/10.1023/a:1007601015854openalex-snapshot
Robust Classification for Imprecise Environments
inferredcites →https://doi.org/10.1023/a:1022948414856openalex-snapshot
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
inferredcites →https://doi.org/10.1109/5254.769882openalex-snapshot
Adaptive image analysis for aerial surveillance
inferredcites →https://doi.org/10.1145/267658.267666openalex-snapshot
A scalable comparison-shopping agent for the World-Wide Web
inferredcites →https://doi.org/10.1145/347090.347102openalex-snapshot
Ongoing management and application of discovered knowledge in a large regulatory organization
inferredcites →https://doi.org/10.1145/502512.502529openalex-snapshot
Mining time-changing data streams
inferredcites →https://doi.org/10.1145/775047.775102openalex-snapshot
Learning nonstationary models of normal network traffic for detecting novel attacks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.1327852openalex-extracted
Adversarial Classification When Even Good Types Can Fake
inferred← citeshttps://doi.org/10.2139/ssrn.4608639openalex-extracted
Adversarial Examples for Captcha Generation Adversarial Machine Learning for Social Good
inferred← citeshttps://doi.org/10.2139/ssrn.4640844openalex-extracted
A Topological Data Analysis Approach for Detecting Data Poisoning Attacks Against Machine Learning Based Network Intrusion Detection Systems
inferred← citeshttps://doi.org/10.2139/ssrn.4651812openalex-extracted
A Topological Data Analysis Approach for Detecting Data Poisoning Attacks Against Machine Learning Based Network Intrusion Detection Systems
inferred← citeshttps://doi.org/10.2139/ssrn.4663447openalex-extracted
Answering Clean Tech Questions with Large Language Models
inferred← citeshttps://doi.org/10.2139/ssrn.916373openalex-extracted
Economics of User Segmentation, Profiling, and Detection in Security
inferred← citeshttps://doi.org/10.5281/zenodo.4543470openalex-extracted
Adversarial Machine Learning: Perspectives from Adversarial Risk Analysis
inferred← citeshttps://doi.org/10.1002/9781118445112.stat08332openalex-snapshot
Adversarial Machine Learning
inferred← citeshttps://doi.org/10.1002/9781119723950.ch13openalex-snapshot
A Game Theoretic Perspective on Adversarial Machine Learning and Related Cybersecurity Applications
inferred← citeshttps://doi.org/10.1002/9781119723950.ch7openalex-snapshot
Minimax Detection (MAD) for Computer Security: A Dynamic Program Characterization
inferred← citeshttps://doi.org/10.1002/9781119815075.ch10openalex-snapshot
Machine Behaviour (Originally Published 2019 by Springer Nature)
inferred← citeshttps://doi.org/10.1002/9781119901808.ch2openalex-snapshot
Deep Neural Network Attacks and Defense: The Case of Image Classification
inferred← citeshttps://doi.org/10.1002/sec.613openalex-snapshot
Semantic aware attribution analysis of remote exploits
inferred← citeshttps://doi.org/10.1002/wics.1511openalex-snapshot
Adversarial machine learning for cybersecurity and computer vision: Current developments and challenges
inferred← citeshttps://doi.org/10.1002/widm.1245openalex-snapshot
A dynamic‐adversarial mining approach to the security of machine learning
inferred← citeshttps://doi.org/10.1002/widm.1259openalex-snapshot
A survey of game theoretic approach for adversarial machine learning
inferred← citeshttps://doi.org/10.1002/widm.1337openalex-snapshot
Behavioral fingerprinting of Internet‐of‐Things devices
inferred← citeshttps://doi.org/10.1007/11564096_14openalex-snapshot
Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam
inferred← citeshttps://doi.org/10.1007/11856214_5openalex-snapshot
Paragraph: Thwarting Signature Learning by Training Maliciously
inferred← citeshttps://doi.org/10.1007/978-0-387-30164-8_828openalex-snapshot
Text Mining for Spam Filtering
inferred← citeshttps://doi.org/10.1007/978-0-387-79420-4_7openalex-snapshot
Security Data Mining: A Survey Introducing Tamper-Resistance
inferred← citeshttps://doi.org/10.1007/978-0-387-88735-7_2openalex-snapshot
Misleading Learners: Co-opting Your Spam Filter
inferred← citeshttps://doi.org/10.1007/978-1-4614-6170-8_294openalex-snapshot
Spam Detection, E-mail/Social Network
inferred← citeshttps://doi.org/10.1007/978-1-4614-7163-9_294-1openalex-snapshot
Spam Detection, E-mail/Social Network
inferred← citeshttps://doi.org/10.1007/978-1-4899-7502-7_982-1openalex-snapshot
Adversarial Learning on Malware
inferred← citeshttps://doi.org/10.1007/978-1-4939-7131-2_294openalex-snapshot
Spam Detection: E-mail/Social Network
inferred← citeshttps://doi.org/10.1007/978-3-030-00374-6_6openalex-snapshot
Identifying the Machine Learning Family from Black-Box Models
inferred← citeshttps://doi.org/10.1007/978-3-030-03251-7_17openalex-snapshot
Too Big to FAIL: What You Need to Know Before Attacking a Machine Learning System
inferred← citeshttps://doi.org/10.1007/978-3-030-03359-0_8openalex-snapshot
A New and Secure Intrusion Detecting System for Detection of Anomalies Within the Big Data
inferred← citeshttps://doi.org/10.1007/978-3-030-12942-2_11openalex-snapshot
An Evaluation of OCR Systems Against Adversarial Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-22479-0_5openalex-snapshot
Adversarial Sampling Attacks Against Phishing Detection
inferred← citeshttps://doi.org/10.1007/978-3-030-29726-8_18openalex-snapshot
Backdoor Attacks in Neural Networks – A Systematic Evaluation on Multiple Traffic Sign Datasets
inferred← citeshttps://doi.org/10.1007/978-3-030-32150-5_109openalex-snapshot
A Review on Security Attacks and Protective Strategies of Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-34637-9_6openalex-snapshot
Security Comparison of Machine Learning Models Facing Different Attack Targets
inferred← citeshttps://doi.org/10.1007/978-3-030-41110-7openalex-snapshot
The Cloud-to-Thing Continuum: Opportunities and Challenges in Cloud, Fog and Edge Computing
inferred← citeshttps://doi.org/10.1007/978-3-030-41110-7_6openalex-snapshot
Security of Distributed Intelligence in Edge Computing: Threats and Countermeasures
inferred← citeshttps://doi.org/10.1007/978-3-030-55692-1_2openalex-snapshot
Security of Distributed Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-59621-7_3openalex-snapshot
FraudFox: Adaptable Fraud Detection in the Real World
inferred← citeshttps://doi.org/10.1007/978-3-030-61609-0_1openalex-snapshot
On the Security Relevance of Initial Weights in Deep Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-030-61725-7_32openalex-snapshot
Resilient Machine Learning (rML) Ensemble Against Adversarial Machine Learning Attacks
inferred← citeshttps://doi.org/10.1007/978-3-030-64793-3_25openalex-snapshot
Attacking Machine Learning Models for Social Good
inferred← citeshttps://doi.org/10.1007/978-3-030-65154-1_6openalex-snapshot
Calibrated Multi-probabilistic Prediction as a Defense Against Adversarial Attacks
inferred← citeshttps://doi.org/10.1007/978-3-030-67661-2_13openalex-snapshot
Quality Guarantees for Autoencoders via Unsupervised Adversarial Attacks
inferred← citeshttps://doi.org/10.1007/978-3-030-71522-9_1824openalex-snapshot
Data Poisoning Attacks in Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-79150-6_21openalex-snapshot
Robustness Testing of AI Systems: A Case Study for Traffic Sign Recognition
inferred← citeshttps://doi.org/10.1007/978-3-030-86362-3_8openalex-snapshot
Model Extraction and Adversarial Attacks on Neural Networks Using Switching Power Information
inferred← citeshttps://doi.org/10.1007/978-3-030-89912-7_1openalex-snapshot
Evaluation of Supervised Machine Learning Algorithms for Multi-class Intrusion Detection Systems
inferred← citeshttps://doi.org/10.1007/978-3-030-90370-1_5openalex-snapshot
Scalable Optimal Classifiers for Adversarial Settings Under Uncertainty
inferred← citeshttps://doi.org/10.1007/978-3-030-90370-1_9openalex-snapshot
When Should You Defend Your Classifier?
inferred← citeshttps://doi.org/10.1007/978-3-030-92121-7_19openalex-snapshot
Performance Evaluation of Adversarial Attacks on Whole-Graph Embedding Models
inferred← citeshttps://doi.org/10.1007/978-3-030-96756-7_9openalex-snapshot
Adversarial Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-98795-4_12openalex-snapshot
Adversarial Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-99772-4_3openalex-snapshot
Adversarial Attack Surfaces
inferred← citeshttps://doi.org/10.1007/978-3-030-99772-4_4openalex-snapshot
Game Theoretical Adversarial Deep Learning
inferred← citeshttps://doi.org/10.1007/978-3-031-01580-9openalex-snapshot
Adversarial Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-031-01942-5_20openalex-snapshot
Adversarial Attacks on Machine Learning Systems
inferred← citeshttps://doi.org/10.1007/978-3-031-06427-2_55openalex-snapshot
Pruning in the Face of Adversaries
inferred← citeshttps://doi.org/10.1007/978-3-031-15030-2_6openalex-snapshot
Offensive Machine Learning Methods and the Cyber Kill Chain
inferred← citeshttps://doi.org/10.1007/978-3-031-15030-2_7openalex-snapshot
Defensive Machine Learning Methods and the Cyber Defence Chain
inferred← citeshttps://doi.org/10.1007/978-3-031-20862-1_35openalex-snapshot
Managing Dataset Shift by Adversarial Validation for Credit Scoring
inferred← citeshttps://doi.org/10.1007/978-3-031-21280-2_21openalex-snapshot
Preventing Adversarial Attacks Against Deep Learning-Based Intrusion Detection System
inferred← citeshttps://doi.org/10.1007/978-3-031-21280-2_26openalex-snapshot
Practical Backdoor Attack Against Speaker Recognition System
inferred← citeshttps://doi.org/10.1007/978-3-031-33383-5_25openalex-snapshot
Targeted Attacks on Time Series Forecasting
inferred← citeshttps://doi.org/10.1007/978-3-031-46674-8_38openalex-snapshot
A Visual Interpretation-Based Self-improved Classification System Using Virtual Adversarial Training
inferred← citeshttps://doi.org/10.1007/978-3-031-49099-6_14openalex-snapshot
Security and Privacy in Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-031-51023-6_2openalex-snapshot
Generating Invariance-Based Adversarial Examples: Bringing Humans Back into the Loop
inferred← citeshttps://doi.org/10.1007/978-3-031-53510-9_8openalex-snapshot
Adversarial Machine Learning for Wireless Localization
inferred← citeshttps://doi.org/10.1007/978-3-031-54827-7_20openalex-snapshot
Adversarial Evasion on LLMs
inferred← citeshttps://doi.org/10.1007/978-3-031-74835-6_13openalex-snapshot
Towards a Game-Theoretic Understanding of Explanation-Based Membership Inference Attacks
inferred← citeshttps://doi.org/10.1007/978-3-031-78110-0_5openalex-snapshot
Fair Latent Representation Learning with Adaptive Reweighing
inferred← citeshttps://doi.org/10.1007/978-3-031-88574-7_31openalex-snapshot
Resilient Machine Learning (rML) Against Adversarial Machine Learning Attacks on Industrial Control Systems
inferred← citeshttps://doi.org/10.1007/978-3-032-08064-6_14openalex-snapshot
A Continuous Strategy Space Adversarial Classification Game
inferred← citeshttps://doi.org/10.1007/978-3-032-08067-7_4openalex-snapshot
Theoretical Bounds on the Adversarial Robustness of Reduced-Rank Regression
inferred← citeshttps://doi.org/10.1007/978-3-032-11153-1_1openalex-snapshot
Fundamentals of Quantum Machine Learning and Robustness
inferred← citeshttps://doi.org/10.1007/978-3-032-19099-4_31openalex-snapshot
Towards an Adversarial Model for Fraud Detection Systems
inferred← citeshttps://doi.org/10.1007/978-3-319-02300-7_4openalex-snapshot
Security Evaluation of Support Vector Machines in Adversarial Environments
inferred← citeshttps://doi.org/10.1007/978-3-319-20248-8_15openalex-snapshot
One-and-a-Half-Class Multiple Classifier Systems for Secure Learning Against Evasion Attacks at Test Time
inferred← citeshttps://doi.org/10.1007/978-3-319-23528-8_17openalex-snapshot
Discovering Opinion Spammer Groups by Network Footprints
inferred← citeshttps://doi.org/10.1007/978-3-319-41187-3_12openalex-snapshot
Planning Cyberspace Deception
inferred← citeshttps://doi.org/10.1007/978-3-319-49055-7_29openalex-snapshot
On Security and Sparsity of Linear Classifiers for Adversarial Settings
inferred← citeshttps://doi.org/10.1007/978-3-319-57529-2_18openalex-snapshot
A Domain-Agnostic Approach to Spam-URL Detection via Redirects
inferred← citeshttps://doi.org/10.1007/978-3-319-63004-5_7openalex-snapshot
SecMD: Make Machine Learning More Secure Against Adversarial Malware Attacks
inferred← citeshttps://doi.org/10.1007/978-3-319-73848-2_49openalex-snapshot
Concept Uncertainty in Adversarial Statistical Decision Theory
inferred← citeshttps://doi.org/10.1007/978-3-319-93040-4_44openalex-snapshot
Randomizing SVM Against Adversarial Attacks Under Uncertainty
inferred← citeshttps://doi.org/10.1007/978-3-540-48716-6_35openalex-snapshot
Systems for Strategic Learning
inferred← citeshttps://doi.org/10.1007/978-3-540-89689-0_54openalex-snapshot
Adversarial Pattern Classification Using Multiple Classifiers and Randomisation
inferred← citeshttps://doi.org/10.1007/978-3-642-02326-2_14openalex-snapshot
Multiple Classifier Systems for Adversarial Classification Tasks
inferred← citeshttps://doi.org/10.1007/978-3-642-02326-2_45openalex-snapshot
Ensemble Based Data Fusion for Gene Function Prediction
inferred← citeshttps://doi.org/10.1007/978-3-642-03348-3_17openalex-snapshot
Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach
inferred← citeshttps://doi.org/10.1007/978-3-642-03999-7_2openalex-snapshot
Evade Hard Multiple Classifier Systems
inferred← citeshttps://doi.org/10.1007/978-3-642-12127-2_8openalex-snapshot
Multiple Classifier Systems under Attack
inferred← citeshttps://doi.org/10.1007/978-3-642-17197-0_4openalex-snapshot
gPath: A Game-Theoretic Path Selection Algorithm to Protect Tor’s Anonymity
inferred← citeshttps://doi.org/10.1007/978-3-642-19896-0_8openalex-snapshot
Classifier Evasion: Models and Open Problems
inferred← citeshttps://doi.org/10.1007/978-3-642-21557-5_37openalex-snapshot
Bagging Classifiers for Fighting Poisoning Attacks in Adversarial Classification Tasks
inferred← citeshttps://doi.org/10.1007/978-3-642-27739-9_1824-1openalex-snapshot
Data Poisoning Attacks in Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-642-30217-6_18openalex-snapshot
Evasion Attack of Multi-class Linear Classifiers
inferred← citeshttps://doi.org/10.1007/978-3-642-31909-9_11openalex-snapshot
SA3: Automatic Semantic Aware Attribution Analysis of Remote Exploits
inferred← citeshttps://doi.org/10.1007/978-3-642-34266-0_5openalex-snapshot
Computing the Nash Equilibria of Intruder Classification Games
inferred← citeshttps://doi.org/10.1007/978-3-642-35101-3_55openalex-snapshot
An Efficient Adversarial Learning Strategy for Constructing Robust Classification Boundaries
inferred← citeshttps://doi.org/10.1007/978-3-642-40994-3_25openalex-snapshot
Evasion Attacks against Machine Learning at Test Time
inferred← citeshttps://doi.org/10.1007/978-3-642-41822-8_1openalex-snapshot
Pattern Recognition Systems under Attack
inferred← citeshttps://doi.org/10.1007/978-981-13-2384-3_32openalex-snapshot
Intrusion Detection in SCADA System: A Survey
inferred← citeshttps://doi.org/10.1007/978-981-16-2765-1_47openalex-snapshot
Detecting Adversarial Examples for Time Series Classification and Its Performance Evaluation
inferred← citeshttps://doi.org/10.1007/978-981-19-7867-8_18openalex-snapshot
A Black-Box Attack on Optical Character Recognition Systems
inferred← citeshttps://doi.org/10.1007/978-981-96-0573-6_9openalex-snapshot
On Adversarial Training with Incorrect Labels
inferred← citeshttps://doi.org/10.1007/978-981-96-4506-0_15openalex-snapshot
A Comparative Testing of Object Detection Model Based on Adversarial Examples
inferred← citeshttps://doi.org/10.1007/978-981-96-9033-6_6openalex-snapshot
On Security of Data-Driven FDC Systems: Attack, Defense, and Verification
inferred← citeshttps://doi.org/10.1007/978-981-96-9101-2_16openalex-snapshot
Understanding the Robustness of Machine-Unlearning Models
inferred← citeshttps://doi.org/10.1007/s00521-020-04914-7openalex-snapshot
A semi-self-taught network intrusion detection system
inferred← citeshttps://doi.org/10.1007/s10207-011-0132-7openalex-snapshot
SAS: semantics aware signature generation for polymorphic worm detection
inferred← citeshttps://doi.org/10.1007/s10257-007-0065-xopenalex-snapshot
Systems for strategic learning
inferred← citeshttps://doi.org/10.1007/s10458-025-09725-5openalex-snapshot
Strategic classification for non-uniform preferences using penalty labels and randomisation
inferred← citeshttps://doi.org/10.1007/s10462-011-9285-zopenalex-snapshot
Genetic optimized artificial immune system in spam detection: a review and a model
inferred← citeshttps://doi.org/10.1007/s10462-022-10195-4openalex-snapshot
A review of spam email detection: analysis of spammer strategies and the dataset shift problem
inferred← citeshttps://doi.org/10.1007/s10473-022-0613-yopenalex-snapshot
Achieving optimal adversarial accuracy for adversarial deep learning using Stackelberg games
inferred← citeshttps://doi.org/10.1007/s10479-008-0485-0openalex-snapshot
Induction over Strategic Agents: a genetic algorithm solution
inferred← citeshttps://doi.org/10.1007/s10479-010-0833-8openalex-snapshot
Endogenizing the sticks and carrots: modeling possible perverse effects of counterterrorism measures
inferred← citeshttps://doi.org/10.1007/s10489-020-02086-4openalex-snapshot
Label flipping attacks against Naive Bayes on spam filtering systems
inferred← citeshttps://doi.org/10.1007/s10579-009-9113-0openalex-snapshot
Filtering artificial texts with statistical machine learning techniques
inferred← citeshttps://doi.org/10.1007/s10618-010-0197-3openalex-snapshot
Classifier evaluation and attribute selection against active adversaries
inferred← citeshttps://doi.org/10.1007/s10618-016-0459-9openalex-snapshot
Adversarial classification using signaling games with an application to phishing detection
inferred← citeshttps://doi.org/10.1007/s10664-021-10064-8openalex-snapshot
Omni: automated ensemble with unexpected models against adversarial evasion attack
inferred← citeshttps://doi.org/10.1007/s10710-020-09389-yopenalex-snapshot
Adversarial genetic programming for cyber security: a rising application domain where GP matters
inferred← citeshttps://doi.org/10.1007/s10723-012-9232-5openalex-snapshot
Data Placement in P2P Data Grids Considering the Availability, Security, Access Performance and Load Balancing
inferred← citeshttps://doi.org/10.1007/s10799-010-0064-3openalex-snapshot
Data mining with agent gaming
inferred← citeshttps://doi.org/10.1007/s10994-009-5124-8openalex-snapshot
Learning to classify with missing and corrupted features
inferred← citeshttps://doi.org/10.1007/s10994-010-5188-5openalex-snapshot
The security of machine learning
inferred← citeshttps://doi.org/10.1007/s10994-010-5199-2openalex-snapshot
Mining adversarial patterns via regularized loss minimization
inferred← citeshttps://doi.org/10.1007/s10994-010-5207-6openalex-snapshot
Machine learning in adversarial environments
inferred← citeshttps://doi.org/10.1007/s10994-017-5663-3openalex-snapshot
Analysis of classifiers’ robustness to adversarial perturbations
inferred← citeshttps://doi.org/10.1007/s10994-021-05994-9openalex-snapshot
Metrics and methods for robustness evaluation of neural networks with generative models
inferred← citeshttps://doi.org/10.1007/s10994-021-06110-7openalex-snapshot
Troubleshooting image segmentation models with human-in-the-loop
inferred← citeshttps://doi.org/10.1007/s10994-022-06216-6openalex-snapshot
On the robustness of randomized classifiers to adversarial examples
inferred← citeshttps://doi.org/10.1007/s10994-022-06224-6openalex-snapshot
Adversarial vulnerability bounds for Gaussian process classification
inferred← citeshttps://doi.org/10.1007/s11023-021-09580-9openalex-snapshot
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples
inferred← citeshttps://doi.org/10.1007/s11042-024-18475-7openalex-snapshot
Trans-IFFT-FGSM: a novel fast gradient sign method for adversarial attacks
inferred← citeshttps://doi.org/10.1007/s11081-021-09652-wopenalex-snapshot
An empirical study of derivative-free-optimization algorithms for targeted black-box attacks in deep neural networks
inferred← citeshttps://doi.org/10.1007/s11128-024-04310-3openalex-snapshot
Adversarial examples detection based on quantum fuzzy convolution neural network
inferred← citeshttps://doi.org/10.1007/s11263-019-01213-0openalex-snapshot
Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks
inferred← citeshttps://doi.org/10.1007/s11277-021-08284-8openalex-snapshot
Security Threats and Defensive Approaches in Machine Learning System Under Big Data Environment
inferred← citeshttps://doi.org/10.1007/s11432-020-3067-8openalex-snapshot
Learning from crowds with robust support vector machines
inferred← citeshttps://doi.org/10.1007/s11633-019-1211-xopenalex-snapshot
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
inferred← citeshttps://doi.org/10.1007/s12243-022-00910-1openalex-snapshot
Investigating the practicality of adversarial evasion attacks on network intrusion detection
inferred← citeshttps://doi.org/10.1007/s12530-010-9013-yopenalex-snapshot
Adversarial learning: the impact of statistical sample selection techniques on neural ensembles
inferred← citeshttps://doi.org/10.1007/s12650-024-01029-2openalex-snapshot
Adversarial attacks on machine learning-aided visualizations
inferred← citeshttps://doi.org/10.1007/s13042-010-0007-7openalex-snapshot
Multiple classifier systems for robust classifier design in adversarial environments
inferred← citeshttps://doi.org/10.1007/s13042-016-0629-5openalex-snapshot
Data sanitization against adversarial label contamination based on data complexity
inferred← citeshttps://doi.org/10.1007/s13042-020-01159-7openalex-snapshot
Causative label flip attack detection with data complexity measures
inferred← citeshttps://doi.org/10.1007/s13042-021-01276-xopenalex-snapshot
Improving crowd labeling using Stackelberg models
inferred← citeshttps://doi.org/10.1007/s13042-021-01393-7openalex-snapshot
Do gradient-based explanations tell anything about adversarial robustness to android malware?
inferred← citeshttps://doi.org/10.1007/s13042-025-02702-0openalex-snapshot
Detection of adversarial examples through chaos quantification in time-series analysis
inferred← citeshttps://doi.org/10.1007/s13369-023-08315-5openalex-snapshot
A Robust SNMP-MIB Intrusion Detection System Against Adversarial Attacks
inferred← citeshttps://doi.org/10.1007/s13437-025-00362-zopenalex-snapshot
Visualisation of cyber vulnerabilities in maritime human-autonomy teaming technology
inferred← citeshttps://doi.org/10.1007/s42519-021-00171-6openalex-snapshot
Theoretical Investigation of Generalization Bounds for Adversarial Learning of Deep Neural Networks
inferred← citeshttps://doi.org/10.1007/s42979-022-01321-8openalex-snapshot
A Comparative Study on the Impact of Adversarial Machine Learning Attacks on Contemporary Intrusion Detection Datasets
inferred← citeshttps://doi.org/10.1007/s42979-023-02556-9openalex-snapshot
Data Poisoning Attacks and Mitigation Strategies on Federated Support Vector Machines
inferred← citeshttps://doi.org/10.1007/s44244-023-00005-3openalex-snapshot
On the robustness of vision transformers for in-flight monocular depth estimation
inferred← citeshttps://doi.org/10.1016/j.apenergy.2023.121405openalex-snapshot
Data security of machine learning applied in low-carbon smart grid: A formal model for the physics-constrained robustness
inferred← citeshttps://doi.org/10.1016/j.artint.2012.03.008openalex-snapshot
Algorithms for strategyproof classification
inferred← citeshttps://doi.org/10.1016/j.asoc.2021.107311openalex-snapshot
Robustness analysis of classical and fuzzy decision trees under adversarial evasion attack
inferred← citeshttps://doi.org/10.1016/j.asoc.2023.110173openalex-snapshot
A gradient-based approach for adversarial attack on deep learning-based network intrusion detection systems
inferred← citeshttps://doi.org/10.1016/j.autcon.2022.104298openalex-snapshot
Trustworthy AI and robotics: Implications for the AEC industry
inferred← citeshttps://doi.org/10.1016/j.cam.2024.116201openalex-snapshot
Difference of Convex programming in adversarial SVM
inferred← citeshttps://doi.org/10.1016/j.chb.2012.01.002openalex-snapshot
Identifying the signs of fraudulent accounts using data mining techniques
inferred← citeshttps://doi.org/10.1016/j.comcom.2023.09.030openalex-snapshot
Untargeted white-box adversarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2019.106669openalex-snapshot
Hidden representations in deep neural networks: Part 1. Classification problems
inferred← citeshttps://doi.org/10.1016/j.cor.2023.106478openalex-snapshot
Manipulating hidden-Markov-model inferences by corrupting batch data
inferred← citeshttps://doi.org/10.1016/j.cose.2011.05.004openalex-snapshot
Masquerade mimicry attack detection: A randomised approach
inferred← citeshttps://doi.org/10.1016/j.cose.2020.101901openalex-snapshot
Adversarial Detection of Flash Malware: Limitations and Open Issues
inferred← citeshttps://doi.org/10.1016/j.cose.2022.103006openalex-snapshot
The Threat of Offensive AI to Organizations
inferred← citeshttps://doi.org/10.1016/j.cose.2024.103861openalex-snapshot
Diversity supporting robustness: Enhancing adversarial robustness via differentiated ensemble predictions
inferred← citeshttps://doi.org/10.1016/j.cose.2024.103929openalex-snapshot
A topological data analysis approach for detecting data poisoning attacks against machine learning based network intrusion detection systems
inferred← citeshttps://doi.org/10.1016/j.cosrev.2019.100199openalex-snapshot
A taxonomy and survey of attacks against machine learning
inferred← citeshttps://doi.org/10.1016/j.egypro.2011.11.043openalex-snapshot
Security Analysis of Kolmogorov Complexity Based Online Spam Filtering under Adversarial Impact
inferred← citeshttps://doi.org/10.1016/j.ejor.2009.09.028openalex-snapshot
Induction over constrained strategic agents
inferred← citeshttps://doi.org/10.1016/j.ejor.2016.05.052openalex-snapshot
Comparison of imputation methods for discriminant analysis with strategically hidden data
inferred← citeshttps://doi.org/10.1016/j.engappai.2020.104085openalex-snapshot
Adversarial robustness and attacks for multi-view deep models
inferred← citeshttps://doi.org/10.1016/j.eswa.2017.04.055openalex-snapshot
Towards filtering undesired short text messages using an online learning approach with semantic indexing
inferred← citeshttps://doi.org/10.1016/j.eswa.2017.09.053openalex-snapshot
Quantifying the resilience of machine learning classifiers used for cyber security
inferred← citeshttps://doi.org/10.1016/j.eswa.2017.11.032openalex-snapshot
Picking on the family: Disrupting android malware triage by forcing misclassification
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115782openalex-snapshot
Adversarial machine learning in Network Intrusion Detection Systems
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.116815openalex-snapshot
Adversarial attacks and defenses on AI in medical imaging informatics: A survey
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.future.2022.03.003openalex-snapshot
Dynamic defense against byzantine poisoning attacks in federated learning
inferred← citeshttps://doi.org/10.1016/j.icte.2022.03.002openalex-snapshot
Security methods for AI based COVID-19 analysis system : A survey
inferred← citeshttps://doi.org/10.1016/j.ifacsc.2021.100163openalex-snapshot
A game-theoretic approach to adversarial linear Gaussian classification
inferred← citeshttps://doi.org/10.1016/j.ijar.2019.07.003openalex-snapshot
Adversarial classification: An adversarial risk analysis approach
inferred← citeshttps://doi.org/10.1016/j.ijhcs.2009.03.004openalex-snapshot
Interacting meaningfully with machine learning systems: Three experiments
inferred← citeshttps://doi.org/10.1016/j.inffus.2022.09.011openalex-snapshot
Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges
inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102728openalex-snapshot
Adversarial robust image processing in medical digital twin
inferred← citeshttps://doi.org/10.1016/j.ins.2020.02.075openalex-snapshot
A feature-vector generative adversarial network for evading PDF malware classifiers
inferred← citeshttps://doi.org/10.1016/j.ins.2021.05.049openalex-snapshot
Data poisoning against information-theoretic feature selection
showing the first 200 of 825 · page through all 825
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.