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Arms Race in Adversarial Malware Detection: A Survey
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Outgoing claims: the papers this work cites, builds on, or argues with.
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/10.1007/978-3-030-13453-2_1openalex-snapshot
Label Sanitization Against Label Flipping Poisoning Attacks
inferredcites →https://doi.org/10.1007/978-3-319-02300-7_4openalex-snapshot
Security Evaluation of Support Vector Machines in Adversarial Environments
inferredcites →https://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
inferredcites →https://doi.org/10.1007/978-3-319-66399-9_4openalex-snapshot
Adversarial Examples for Malware Detection
inferredcites →https://doi.org/10.1007/978-3-540-89862-7_1openalex-snapshot
BitBlaze: A New Approach to Computer Security via Binary Analysis
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/10.1007/978-981-19-8991-9_29openalex-snapshot
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
inferredcites →https://doi.org/10.1007/s10994-010-5188-5openalex-snapshot
The security of machine learning
inferredcites →https://doi.org/10.1007/s11416-008-0108-yopenalex-snapshot
SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging
inferredcites →https://doi.org/10.1007/s13042-010-0007-7openalex-snapshot
Multiple classifier systems for robust classifier design in adversarial environments
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.cose.2020.101901openalex-snapshot
Adversarial Detection of Flash Malware: Limitations and Open Issues
inferredcites →https://doi.org/10.1016/j.icte.2018.10.006openalex-snapshot
DeepMalNet: Evaluating shallow and deep networks for static PE malware detection
inferredcites →https://doi.org/10.1016/j.ins.2018.04.092openalex-snapshot
Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders
inferredcites →https://doi.org/10.1023/a:1018628609742openalex-snapshot
Least Squares Support Vector Machine Classifiers
inferredcites →https://doi.org/10.1049/cit2.12028openalex-snapshot
A survey on adversarial attacks and defences
inferredcites →https://doi.org/10.1109/access.2018.2805680openalex-snapshot
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View
inferredcites →https://doi.org/10.1109/acsac.2007.21openalex-snapshot
Limits of Static Analysis for Malware Detection
inferredcites →https://doi.org/10.1109/asonam.2018.8508284openalex-snapshot
DroidEye: Fortifying Security of Learning-Based Classifier Against Adversarial Android Malware Attacks
inferredcites →https://doi.org/10.1109/bwcca.2010.85openalex-snapshot
Malware Obfuscation Techniques: A Brief Survey
inferredcites →https://doi.org/10.1109/cmpsac.2004.1342667openalex-snapshot
N-gram-based detection of new malicious code
inferredcites →https://doi.org/10.1109/cns.2014.6997494openalex-snapshot
An evasion and counter-evasion study in malicious websites detection
inferredcites →https://doi.org/10.1109/eisic.2017.21openalex-snapshot
Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/eurosp.2018.00035openalex-snapshot
SoK: Security and Privacy in Machine Learning
inferredcites →https://doi.org/10.1109/i4ct.2015.7219584openalex-snapshot
In memory detection of Windows API call hooking technique
inferredcites →https://doi.org/10.1109/ijcnn.2016.7727508openalex-snapshot
Adaptive and scalable Android malware detection through online learning
inferredcites →https://doi.org/10.1109/ijcnn.2017.7966340openalex-snapshot
Empowering convolutional networks for malware classification and analysis
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.2012.14openalex-snapshot
Prudent Practices for Designing Malware Experiments: Status Quo and Outlook
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/sp40000.2020.00073openalex-snapshot
Intriguing Properties of Adversarial ML Attacks in the Problem Space
inferredcites →https://doi.org/10.1109/spw.2018.00020openalex-snapshot
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
inferredcites →https://doi.org/10.1109/spw.2019.00015openalex-snapshot
Exploring Adversarial Examples in Malware Detection
inferredcites →https://doi.org/10.1109/tcyb.2015.2415032openalex-snapshot
Adversarial Feature Selection Against Evasion Attacks
inferredcites →https://doi.org/10.1109/tdsc.2017.2700270openalex-snapshot
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
inferredcites →https://doi.org/10.1109/tifs.2014.2353996openalex-snapshot
Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection
inferredcites →https://doi.org/10.1109/tifs.2018.2833292openalex-snapshot
Statistical Estimation of Malware Detection Metrics in the Absence of Ground Truth
inferredcites →https://doi.org/10.1109/tifs.2019.2932228openalex-snapshot
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
inferredcites →https://doi.org/10.1109/tifs.2020.3003571openalex-snapshot
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
inferredcites →https://doi.org/10.1109/tkde.2013.57openalex-snapshot
Security Evaluation of Pattern Classifiers under Attack
inferredcites →https://doi.org/10.1109/tnnls.2018.2886017openalex-snapshot
Adversarial Examples: Attacks and Defenses for Deep Learning
inferredcites →https://doi.org/10.1109/tnse.2021.3051354openalex-snapshot
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware
inferredcites →https://doi.org/10.1109/tpami.2005.159openalex-snapshot
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
inferredcites →https://doi.org/10.1109/tpami.2013.50openalex-snapshot
Representation Learning: A Review and New Perspectives
inferredcites →https://doi.org/10.1145/1015330.1015435openalex-snapshot
Feature selection, L 1 vs. L 2 regularization, and rotational invariance
inferredcites →https://doi.org/10.1145/1102351.1102430openalex-snapshot
Predicting good probabilities with supervised learning
inferredcites →https://doi.org/10.1145/1128817.1128824openalex-snapshot
Can machine learning be secure?
inferredcites →https://doi.org/10.1145/2089125.2089126openalex-snapshot
A survey on automated dynamic malware-analysis techniques and tools
inferredcites →https://doi.org/10.1145/2381896.2381900openalex-snapshot
Improving malware classification
inferredcites →https://doi.org/10.1145/2420950.2420987openalex-snapshot
Malicious PDF detection using metadata and structural features
inferredcites →https://doi.org/10.1145/2484313.2484327openalex-snapshot
Looking at the bag is not enough to find the bomb
inferredcites →https://doi.org/10.1145/2806416.2806548openalex-snapshot
A Min-Max Optimization Framework For Online Graph Classification
inferredcites →https://doi.org/10.1145/2976749.2978422openalex-snapshot
Statistical Deobfuscation of Android Applications
inferredcites →https://doi.org/10.1145/2996758.2996771openalex-snapshot
Secure Kernel Machines against Evasion Attacks
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3073559openalex-snapshot
A Survey on Malware Detection Using Data Mining Techniques
inferredcites →https://doi.org/10.1145/3097983.3098158openalex-snapshot
Adversary Resistant Deep Neural Networks with an Application to Malware Detection
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/10.1145/3134600.3134642openalex-snapshot
Malware Detection in Adversarial Settings
inferredcites →https://doi.org/10.1145/3180445.3180449openalex-snapshot
Adversarially Robust Malware Detection Using Monotonic Classification
inferredcites →https://doi.org/10.1145/3332184openalex-snapshot
Towards Adversarial Malware Detection
inferredcites →https://doi.org/10.1186/s13635-016-0045-0openalex-snapshot
Hidost: a static machine-learning-based detector of malicious files
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.2016.23078openalex-snapshot
When a Tree Falls: Using Diversity in Ensemble Classifiers to Identify Evasion in Malware Detectors
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.2016.23483openalex-snapshot
Extract Me If You Can: Abusing PDF Parsers in Malware Detectors
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.2018/737openalex-snapshot
Make Evasion Harder: An Intelligent Android Malware Detection System
inferredcites →https://doi.org/10.3115/v1/d14-1179openalex-snapshot
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
inferredcites →https://doi.org/10.3115/v1/d14-1181openalex-snapshot
Convolutional Neural Networks for Sentence Classification
inferredcites →https://doi.org/10.48550/arxiv.1206.6389openalex-snapshot
Poisoning Attacks against Support Vector Machines
inferredcites →https://doi.org/10.48550/arxiv.1505.06378openalex-snapshot
Monotonic Calibrated Interpolated Look-Up Tables
inferredcites →https://doi.org/10.48550/arxiv.1604.02606openalex-snapshot
A General Retraining Framework for Scalable Adversarial Classification
inferredcites →https://doi.org/10.48550/arxiv.1608.08967openalex-snapshot
Robustness of classifiers: from adversarial to random noise
inferredcites →https://doi.org/10.48550/arxiv.1702.06280openalex-snapshot
On the (Statistical) Detection of Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1705.09554openalex-snapshot
Robustness of classifiers to universal perturbations: a geometric perspective
inferredcites →https://doi.org/10.48550/arxiv.1711.00851openalex-snapshot
Provable defenses against adversarial examples via the convex outer adversarial polytope
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.1804.07933openalex-snapshot
Is feature selection secure against training data poisoning?
inferredcites →https://doi.org/10.48550/arxiv.1804.10829openalex-snapshot
Formal Security Analysis of Neural Networks using Symbolic Intervals
inferredcites →https://doi.org/10.48550/arxiv.1804.11285openalex-snapshot
Adversarially Robust Generalization Requires More Data
inferredcites →https://doi.org/10.48550/arxiv.1810.00069openalex-snapshot
Adversarial Attacks and Defences: A Survey
inferredcites →https://doi.org/10.48550/arxiv.1810.01185openalex-snapshot
Adversarial Examples - A Complete Characterisation of the Phenomenon
inferredcites →https://doi.org/10.48550/arxiv.1810.12490openalex-snapshot
SAFE-PDF: Robust Detection of JavaScript PDF Malware Using Abstract Interpretation
inferredcites →https://doi.org/10.48550/arxiv.2008.07125openalex-snapshot
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection
inferredcites →https://doi.org/10.5555/1953048.2078186openalex-snapshot
Natural Language Processing (Almost) from Scratch
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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WebAssembly diversification for malware evasion
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A Survey on Adversarial Attacks for Malware Analysis
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LLM-Guided Hierarchical Reinforcement Learning for Black-Box Adversarial Attacks Against Malware Detectors
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