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Adversarial EXEmples

https://doi.org/10.1145/3473039
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-50127-7_11openalex-snapshot
Deep Learning for Classification of Malware System Call Sequences
inferredcites →https://doi.org/10.1007/978-3-642-40994-3_25openalex-snapshot
Evasion Attacks against Machine Learning at Test Time
inferredcites →https://doi.org/10.1109/ijcnn.2015.7280815openalex-snapshot
DeepSign: Deep learning for automatic malware signature generation and classification
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/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of 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.2019.00015openalex-snapshot
Exploring Adversarial Examples in Malware Detection
inferredcites →https://doi.org/10.1109/spw.2019.00017openalex-snapshot
Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification
inferredcites →https://doi.org/10.1109/tifs.2021.3082330openalex-snapshot
Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware
inferredcites →https://doi.org/10.1109/tkde.2013.57openalex-snapshot
Security Evaluation of Pattern Classifiers under Attack
inferredcites →https://doi.org/10.1109/trustcom/bigdatase.2019.00040openalex-snapshot
AIMED: Evolving Malware with Genetic Programming to Evade Detection
inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1145/2046684.2046692openalex-snapshot
Adversarial machine learning
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3128572.3140448openalex-snapshot
ZOO
inferredcites →https://doi.org/10.1145/3180445.3180449openalex-snapshot
Adversarially Robust Malware Detection Using Monotonic Classification
inferredcites →https://doi.org/10.1145/3316415openalex-snapshot
From Hack to Elaborate Technique—A Survey on Binary Rewriting
inferredcites →https://doi.org/10.14722/ndss.2020.24310openalex-snapshot
When Malware is Packin' Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features
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/10.48550/arxiv.1312.6199openalex-snapshot
Intriguing properties of neural networks
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
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.1802.04528openalex-snapshot
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
inferredcites →https://doi.org/10.5555/2627435.2638566openalex-snapshot
Natural evolution strategies

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-87839-9_5openalex-snapshot
Attacks on Visualization-Based Malware Detection: Balancing Effectiveness and Executability
inferred← citeshttps://doi.org/10.1007/978-3-030-87839-9_6openalex-snapshot
A Survey on Common Threats in npm and PyPi Registries
inferred← citeshttps://doi.org/10.1007/978-3-031-09484-2_5openalex-snapshot
Prioritizing Antivirus Alerts on Internal Enterprise Machines
inferred← citeshttps://doi.org/10.1007/978-3-031-16564-1_12openalex-snapshot
XAI to Explore Robustness of Features in Adversarial Training for Cybersecurity
inferred← citeshttps://doi.org/10.1007/978-3-031-19208-1_50openalex-snapshot
Gradient-Based Adversarial Attacks Against Malware Detection by Instruction Replacement
inferred← citeshttps://doi.org/10.1007/978-3-031-23633-4_14openalex-snapshot
Towards a General Model for Intrusion Detection: An Exploratory Study
inferred← citeshttps://doi.org/10.1007/978-3-031-34671-2_24openalex-snapshot
Evaluating the Robustness of Automotive Intrusion Detection Systems Against Evasion Attacks
inferred← citeshttps://doi.org/10.1007/978-3-031-36021-3_6openalex-snapshot
RL-MAGE: Strengthening Malware Detectors Against Smart Adversaries
inferred← citeshttps://doi.org/10.1007/978-3-031-51482-1_3openalex-snapshot
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
inferred← citeshttps://doi.org/10.1007/978-3-031-54129-2_40openalex-snapshot
Towards a Practical Defense Against Adversarial Attacks on Deep Learning-Based Malware Detectors via Randomized Smoothing
inferred← citeshttps://doi.org/10.1007/978-3-031-66245-4_6openalex-snapshot
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges, and Ongoing Research
inferred← citeshttps://doi.org/10.1007/978-3-031-83157-7_10openalex-snapshot
Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks
inferred← citeshttps://doi.org/10.1007/978-3-031-83157-7_9openalex-snapshot
Reducing the Surface for Adversarial Attacks in Malware Detectors
inferred← citeshttps://doi.org/10.1007/978-3-032-36590-3_18openalex-snapshot
Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
inferred← citeshttps://doi.org/10.1007/978-981-95-7394-3_31openalex-snapshot
Explainable CAN Intrusion Detection via Feature Extraction
inferred← citeshttps://doi.org/10.1007/978-981-95-9846-5_26openalex-snapshot
Adversarial Assembly: Leveraging LLMs to Evade CNN-Based Windows Malware Detection
inferred← citeshttps://doi.org/10.1007/978-981-96-9935-3_15openalex-snapshot
Robust Adversarial Patterns to Defeat Deep Learning Malware Detection Systems
inferred← citeshttps://doi.org/10.1007/s00521-023-08590-1openalex-snapshot
Towards robust CNN-based malware classifiers using adversarial examples generated based on two saliency similarities
inferred← citeshttps://doi.org/10.1007/s10207-023-00715-wopenalex-snapshot
Evading malware classifiers using RL agent with action-mask
inferred← citeshttps://doi.org/10.1007/s10207-026-01310-5openalex-snapshot
An advanced framework for malware detection using adversarial learning
inferred← citeshttps://doi.org/10.1007/s10586-025-05549-wopenalex-snapshot
A feature-learning-enabled malware analysis for enhanced IoT-centric cybersecurity
inferred← citeshttps://doi.org/10.1007/s10618-025-01127-1openalex-snapshot
OLIVANDER: a counterfactual-based method to generate adversarial Windows PE malware
inferred← citeshttps://doi.org/10.1007/s10994-026-07067-1openalex-snapshot
Progressive Induction-Aware Optimization for LLMs Safety Under Multi-turn Jailbreaks
inferred← citeshttps://doi.org/10.1007/s11416-023-00498-7openalex-snapshot
Mal2GCN: a robust malware detection approach using deep graph convolutional networks with non-negative weights
inferred← citeshttps://doi.org/10.1007/s11416-024-00519-zopenalex-snapshot
A comparison of adversarial malware generators
inferred← citeshttps://doi.org/10.1007/s44443-026-00984-0openalex-snapshot
AFA-MalGAN: adaptive adversarial sample generation for high-dimensional malware features via constrained manifold optimization
inferred← citeshttps://doi.org/10.1016/j.aej.2024.04.024openalex-snapshot
Enhancing reinforcement learning based adversarial malware generation to evade static detection
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.2026.115186openalex-snapshot
OSEAF: A robust one-shot environment-aware framework for malware detection evasion
inferred← citeshttps://doi.org/10.1016/j.cose.2022.102901openalex-snapshot
Practical Evaluation of Poisoning Attacks on Online Anomaly Detectors in Industrial Control Systems
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103103openalex-snapshot
AMGmal: Adaptive mask-guided adversarial attack against malware detection with minimal perturbation
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103134openalex-snapshot
Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103192openalex-snapshot
Stealing and evading malware classifiers and antivirus at low false positive conditions
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103279openalex-snapshot
GAMBD: Generating adversarial malware against MalConv
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103409openalex-snapshot
XMal: A lightweight memory-based explainable obfuscated-malware detector
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103459openalex-snapshot
StratDef: Strategic defense against adversarial attacks in ML-based malware detection
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103595openalex-snapshot
A survey of strategy-driven evasion methods for PE malware: Transformation, concealment, and attack
inferred← citeshttps://doi.org/10.1016/j.cose.2024.103841openalex-snapshot
Defend against adversarial attacks in malware detection through attack space management
inferred← citeshttps://doi.org/10.1016/j.cose.2024.104031openalex-snapshot
BD-MDLC: Behavior description-based enhanced malware detection for windows environment using longformer classifier
inferred← citeshttps://doi.org/10.1016/j.cose.2024.104264openalex-snapshot
SLIFER: Investigating performance and robustness of malware detection pipelines
inferred← citeshttps://doi.org/10.1016/j.cose.2024.104280openalex-snapshot
Practical clean-label backdoor attack against static malware detection
inferred← citeshttps://doi.org/10.1016/j.cose.2025.104466openalex-snapshot
Updating Windows malware detectors: Balancing robustness and regression against adversarial EXEmples
inferred← citeshttps://doi.org/10.1016/j.cose.2025.104495openalex-snapshot
Assessing the impact of packing on static machine learning-based malware detection and classification systems
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.117957openalex-snapshot
Fusing feature engineering and deep learning: A case study for malware classification
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.2025.129812openalex-snapshot
GraphShield: Advanced dynamic graph-based malware detection using graph neural 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.2026.104632openalex-snapshot
On the effectiveness of memory analysis in revealing maliciousness in android applications
inferred← citeshttps://doi.org/10.1016/j.jisa.2026.104659openalex-snapshot
Auditing VirusTotal consensus thresholds in malware benchmarks: A reproducible security-measurement study on EMBER2024
inferred← citeshttps://doi.org/10.1016/j.neucom.2022.04.072openalex-snapshot
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview
inferred← citeshttps://doi.org/10.1038/s41598-025-88130-0openalex-snapshot
Semantic lossless encoded image representation for malware classification
inferred← citeshttps://doi.org/10.1063/5.0237002openalex-snapshot
A comprehensive survey on robust Malware detection model learning from adversarial attacks
inferred← citeshttps://doi.org/10.1088/2631-8695/ad612dopenalex-snapshot
Software installation threat detection based on attention mechanism and improved convolutional neural network in IOT platform
inferred← citeshttps://doi.org/10.1109/access.2024.3392391openalex-snapshot
Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized Smoothing
inferred← citeshttps://doi.org/10.1109/access.2024.3513713openalex-snapshot
An Adversarial Attack on ML-Based IoT Malware Detection Using Binary Diversification Techniques
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00243openalex-snapshot
When Does Wasm Malware Detection Fail? A Systematic Analysis of Their Robustness to Evasion
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020475openalex-snapshot
Evaluating Anti-Virus Effectiveness in Linux
inferred← citeshttps://doi.org/10.1109/cns56114.2022.9947226openalex-snapshot
Transferability of Adversarial Examples in Machine Learning-based Malware Detection
inferred← citeshttps://doi.org/10.1109/cns56114.2022.9947227openalex-snapshot
MultiEvasion: Evasion Attacks Against Multiple Malware Detectors
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.2023.3319492openalex-snapshot
Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey
inferred← citeshttps://doi.org/10.1109/csp58884.2023.00018openalex-snapshot
A Multi-Strategy Adversarial Attack Method for Deep Learning Based Malware Detectors
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/dsc54232.2022.9888884openalex-snapshot
A Co-evolutionary Algorithm-Based Malware Adversarial Sample Generation Method
inferred← citeshttps://doi.org/10.1109/dsn58367.2023.00027openalex-snapshot
MalAder: Decision-Based Black-Box Attack Against API Sequence Based Malware Detectors
inferred← citeshttps://doi.org/10.1109/eurospw67616.2025.00008openalex-snapshot
Demystifying the Role of Rule-Based Detection in AI Systems for Windows Malware Detection
inferred← citeshttps://doi.org/10.1109/eurospw72509.2026.00038openalex-snapshot
Adversarial Malware Can Be Both Evasive and Deceiving: a Gradient-based Attack Against Prediction and Explainability in Windows PE Malware Detection
inferred← citeshttps://doi.org/10.1109/fit57066.2022.00054openalex-snapshot
Defeating Modern Day Anti-Viruses for Defense Evaluation
inferred← citeshttps://doi.org/10.1109/hpcc-dss-smartcity-dependsys57074.2022.00113openalex-snapshot
Black-Box Adversarial Windows Malware Generation via United Puppet-based Dropper and Genetic Algorithm
inferred← citeshttps://doi.org/10.1109/ic2e365635.2025.11167374openalex-snapshot
Adversarial Machine Learning in Cybersecurity: Defending Against Evasive Threats in Distributed Systems
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11461429openalex-snapshot
LLM-Guided Hierarchical Reinforcement Learning for Black-Box Adversarial Attacks Against Malware Detectors
inferred← citeshttps://doi.org/10.1109/icccn61486.2024.10637577openalex-snapshot
Explainability Guided Adversarial Evasion Attacks on Malware Detectors
inferred← citeshttps://doi.org/10.1109/icetes68504.2026.11519032openalex-snapshot
Adversarially Trained Deep Models for Robust Malware and Cyber-Threat Detection
inferred← citeshttps://doi.org/10.1109/icmla58977.2023.00258openalex-snapshot
EvilELF: Evasion Attacks on Deep-Learning Malware Detection over ELF Files
inferred← citeshttps://doi.org/10.1109/icsccc69031.2026.11600354openalex-snapshot
Robust Malware Detection against Adversarial Malwares
inferred← citeshttps://doi.org/10.1109/imsa70415.2026.11700294openalex-snapshot
Enhancing Evasive Malware Detection Using Supervised Contrastive Learning and Online Adversarial Training
inferred← citeshttps://doi.org/10.1109/jetcas.2024.3491497openalex-snapshot
Systematical Evasion from Learning-based Microarchitectural Attack Detection Tools
inferred← citeshttps://doi.org/10.1109/lcn58197.2023.10223320openalex-snapshot
EGAN: Evolutional GAN for Ransomware Evasion
inferred← citeshttps://doi.org/10.1109/miucc66482.2025.11196809openalex-snapshot
Beyond Accuracy: Robustness and Explainability in Adversarial Malware Detection
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/smartiot70864.2026.00030openalex-snapshot
Robust Adversarial Malware Detection through Two-Phase Zone Perturbation in Windows-based Smart IoT
inferred← citeshttps://doi.org/10.1109/sp61157.2025.00022openalex-snapshot
Verifiable Boosted Tree Ensembles
inferred← citeshttps://doi.org/10.1109/tc.2026.3661060openalex-snapshot
MalPDT: Backdoor Attack Against Static Malware Detection With Plug-and-Play Dynamic Triggers
inferred← citeshttps://doi.org/10.1109/tdsc.2023.3265665openalex-snapshot
PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks
inferred← citeshttps://doi.org/10.1109/tdsc.2025.3566708openalex-snapshot
GAME-RL: Generating Adversarial Malware Examples Against API Call Based Detection via Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/tdsc.2026.3689685openalex-snapshot
UAP4MA: Leveraging Multi-Agent Bandits to Generate Universal Adversarial Perturbations for Malware Attribution
inferred← citeshttps://doi.org/10.1109/tic68483.2026.11702961openalex-snapshot
A Comparison of Existing Deep Learning Techniques for Malware Detection with Adversarial Vulnerability
inferred← citeshttps://doi.org/10.1109/tifs.2023.3328431openalex-snapshot
ResNeXt+: Attention Mechanisms Based on ResNeXt for Malware Detection and Classification
inferred← citeshttps://doi.org/10.1109/tifs.2023.3333567openalex-snapshot
MalPatch: Evading DNN-Based Malware Detection With Adversarial Patches
inferred← citeshttps://doi.org/10.1109/tifs.2024.3409083openalex-snapshot
Nebula: Self-Attention for Dynamic Malware Analysis
inferred← citeshttps://doi.org/10.1109/tnse.2025.3627147openalex-snapshot
MalCAFF: A Cross Attention-Based Feature Fusion Framework for Malware Classification
inferred← citeshttps://doi.org/10.1109/tse.2026.3693254openalex-snapshot
MalElves: Reinforcement Learning-Driven Adversarial Example Generation for Evading Cross-Platform ELF Malware Detection
inferred← citeshttps://doi.org/10.1142/s2196888825500095openalex-snapshot
ProDef-MDS: A Proactive Defense Mechanism Protecting Malware Detection Systems from Adversarial Attacks
inferred← citeshttps://doi.org/10.1145/3484491openalex-snapshot
Arms Race in Adversarial Malware Detection: A Survey
inferred← citeshttps://doi.org/10.1145/3494110.3528244openalex-snapshot
Position Paper
inferred← citeshttps://doi.org/10.1145/3555776.3577671openalex-snapshot
Realism versus Performance for Adversarial Examples Against DL-based NIDS
inferred← citeshttps://doi.org/10.1145/3568562.3568636openalex-snapshot
Leveraging Reinforcement Learning and Generative Adversarial Networks to Craft Mutants of Windows Malware against Black-box Malware Detectors
inferred← citeshttps://doi.org/10.1145/3576915.3623100openalex-snapshot
Verifiable Learning for Robust Tree Ensembles
inferred← citeshttps://doi.org/10.1145/3597503.3639141openalex-snapshot
MalwareTotal: Multi-Faceted and Sequence-Aware Bypass Tactics against Static Malware Detection
inferred← citeshttps://doi.org/10.1145/3605764.3623914openalex-snapshot
Certified Robustness of Static Deep Learning-based Malware Detectors against Patch and Append Attacks
inferred← citeshttps://doi.org/10.1145/3610223openalex-snapshot
A Survey of Malware Analysis Using Community Detection Algorithms
inferred← citeshttps://doi.org/10.1145/3615669openalex-snapshot
Efficient Malware Analysis Using Metric Embeddings
inferred← citeshttps://doi.org/10.1145/3627106.3627178openalex-snapshot
PSP-Mal: Evading Malware Detection via Prioritized Experience-based Reinforcement Learning with Shapley Prior
inferred← citeshttps://doi.org/10.1145/3627817openalex-snapshot
It Is All about Data: A Survey on the Effects of Data on Adversarial Robustness
inferred← citeshttps://doi.org/10.1145/3643563openalex-snapshot
AdverSPAM: Adversarial SPam Account Manipulation in Online Social Networks
inferred← citeshttps://doi.org/10.1145/3678007openalex-snapshot
CBAs: Character-level Backdoor Attacks against Chinese Pre-trained Language Models
inferred← citeshttps://doi.org/10.1145/3678890.3678912openalex-snapshot
How to Train your Antivirus: RL-based Hardening through the Problem Space
inferred← citeshttps://doi.org/10.1145/3689932.3694757openalex-snapshot
When Adversarial Perturbations meet Concept Drift: An Exploratory Analysis on ML-NIDS
inferred← citeshttps://doi.org/10.1145/3696410.3714696openalex-snapshot
WasmGuard: Enhancing Web Security through Robust Raw-Binary Detection of WebAssembly Malware
inferred← citeshttps://doi.org/10.1145/3696410.3714814openalex-snapshot
On the Abuse and Detection of Polyglot Files
inferred← citeshttps://doi.org/10.1145/3706573openalex-snapshot
Adversarial Binaries: AI-guided Instrumentation Methods for Malware Detection Evasion
inferred← citeshttps://doi.org/10.1145/3713081.3732928openalex-snapshot
From Large Language Models to Adversarial Malware: How far are we
inferred← citeshttps://doi.org/10.1145/3795101.3805276openalex-snapshot
Co-evolving Adversaries for Online Robust Malware Detection
inferred← citeshttps://doi.org/10.1145/3797132openalex-snapshot
Flash: Query-Efficient Black-Box Static Malware Evasion through Transferable GAN-Guided Modification Sequences
inferred← citeshttps://doi.org/10.1145/3811913openalex-snapshot
LightFAt+: Lightweight Control-flow Attestation via Unsupervised Machine Learning
inferred← citeshttps://doi.org/10.1145/3827613openalex-snapshot
Certified Adversarial Robustness of End-to-End Malware Detectors via (De)Randomized Smoothing
inferred← citeshttps://doi.org/10.1145/3832783.3837492openalex-snapshot
RAVEN: Measuring Detection Instability in Anti-virus Engines under Semantics-Preserving Perturbations
inferred← citeshttps://doi.org/10.1186/s42400-025-00455-5openalex-snapshot
MCPDS: image-based malware classification method using PE metadata alone
inferred← citeshttps://doi.org/10.1186/s42400-026-00600-8openalex-snapshot
Improving robustness and explainability of PE malware classifiers using GAN-Generated Synthetic Adversarial examples
inferred← citeshttps://doi.org/10.1214/26-ba1585openalex-snapshot
Posterior Attraction with Exponential-Family Likelihoods and Their Conjugate Priors
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10839641/v1openalex-snapshot
Adversarially Robust and Semantically Aware Phishing Detection using Calibrated Multimodal Learning
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3100627/v1openalex-snapshot
Dynamic Programming-based Adversarial Windows Payload Generator
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9576397/v1openalex-snapshot
XAI-1DSig: Explainable and Robust 1D Signal-Based IoT Malware Classification
inferred← citeshttps://doi.org/10.2174/0126662558279673240515054741openalex-snapshot
Exploring Deep Learning Approaches for Ransomware Detection: A Comprehensive Survey
inferred← citeshttps://doi.org/10.3233/jcs-210133openalex-snapshot
Certifying machine learning models against evasion attacks by program analysis
inferred← citeshttps://doi.org/10.32604/cmes.2023.046658openalex-snapshot
An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection
inferred← citeshttps://doi.org/10.32604/csse.2023.029642openalex-snapshot
Alpha Fusion Adversarial Attack Analysis Using Deep Learning
inferred← citeshttps://doi.org/10.3390/app13042079openalex-snapshot
SFCWGAN-BiTCN with Sequential Features for Malware Detection
inferred← citeshttps://doi.org/10.3390/electronics12071751openalex-snapshot
MalDBA: Detection for Query-Based Malware Black-Box Adversarial Attacks
inferred← citeshttps://doi.org/10.3390/fi16050168openalex-snapshot
Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection
inferred← citeshttps://doi.org/10.3390/fi18080431openalex-snapshot
C-GUARD: Context-Adaptive Conformal Gating for Improving Robustness Against Evasive Windows PE Malware
inferred← citeshttps://doi.org/10.3390/make5010009openalex-snapshot
On Deceiving Malware Classification with Section Injection
inferred← citeshttps://doi.org/10.36948/ijfmr.2024.v06i06.33563openalex-snapshot
Understanding and Mitigating AI-Powered Cyber-Attacks
inferred← citeshttps://doi.org/10.4018/979-8-3373-9968-3.ch001openalex-snapshot
Artificial Intelligence and Machine Learning for Intelligent Cyber Defense
inferred← citeshttps://doi.org/10.4018/979-8-3693-7540-2.ch007openalex-snapshot
Evolving AI-Based Malware Detection
inferred← citeshttps://doi.org/10.48550/arxiv.2308.09958openalex-snapshot
A Comparison of Adversarial Learning Techniques for Malware Detection
inferred← citeshttps://doi.org/10.7717/peerj-cs.1591openalex-snapshot
Instance attack: an explanation-based vulnerability analysis framework against DNNs for malware detection
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