Malware Detection by Eating a Whole EXE
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Abstract (arXiv)
In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community. Building a neural network for such a problem presents a number of interesting challenges that have not occurred in tasks such as image processing or NLP. In particular, we note that detection from raw bytes presents a sequence problem with over two million time steps and a problem where batch normalization appear to hinder the learning process. We present our initial work in building a solution to tackle this problem, which has linear complexity dependence on the sequence length, and allows for interpretable sub-regions of the binary to be identified. In doing so we will discuss the many challenges in building a neural network to process data at this scale, and the methods we used to work around them.
Key facts. Cited by 114 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Oct 2017.
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Citing papers per year: 2021: 20; 2022: 20; 2023: 17; 2024: 19; 2025: 15; 2026: 23.
114 papers.
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- 2022I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defencescited by 75
- 2021PalmTree: Learning an Assembly Language Model for Instruction Embeddingcited by 49
- 2021Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examplescited by 23
- 2024Beyond Language Models: Byte Models are Digital World Simulatorscited by 21
- 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
- 2023RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletioncited by 17
- 2024CLAP: Learning Transferable Binary Code Representations with Natural Language Supervisioncited by 17
- 2022Out of Distribution Data Detection Using Dropout Bayesian Neural Networkscited by 15
- 2023Recasting Self-Attention with Holographic Reduced Representationscited by 14
- 2021EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detectioncited by 13
- 2023Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performancecited by 12
- 2021MalNet: A Large-Scale Image Database of Malicious Softwarecited by 11
38 more
- 2021Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraintscited by 11
- 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 10
- 2021Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretabilitycited by 9
- 2021Morphence: Moving Target Defense Against Adversarial Examplescited by 9
- 2022Toward More Generalized Malicious URL Detection Modelscited by 9
- 2022Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representationscited by 9
- 2024RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanationcited by 7
- 2021Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learningcited by 6
- 2021Explanation-Guided Diagnosis of Machine Learning Evasion Attackscited by 6
- 2022Transformers for End-to-End InfoSec Tasks: A Feasibility Studycited by 6
- 2024Is Function Similarity Over-Engineered? Building a Benchmarkcited by 6
- 2025Recent Advances in Malware Detection: Graph Learning and Explainabilitycited by 6
- 2023PAD: Towards Principled Adversarial Malware Detection Against Evasion Attackscited by 5
- 2023Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothingcited by 5
- 2023A Comparison of Adversarial Learning Techniques for Malware Detectioncited by 5
- 2024Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challengescited by 5
- 2024Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systemscited by 5
- 2021Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approachcited by 4
- 2023Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!cited by 4
- 2024A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothingcited by 4
- 2024SLIFER: Investigating Performance and Robustness of Malware Detection Pipelinescited by 4
- 2026OrgForge-IT: A Verifiable Synthetic Benchmark for LLM-Based Insider Threat Detectioncited by 4
- 2021Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weightscited by 3
- 2021A Comprehensive Study on Learning-Based PE Malware Family Classification Methodscited by 3
- 2023DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustnesscited by 3
- 2023Creating Valid Adversarial Examples of Malwarecited by 3
- 2023Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutationscited by 3
- 2024How to Train your Antivirus: RL-based Hardening through the Problem-Spacecited by 3
- 2025Semantic Preprocessing for LLM-based Malware Analysiscited by 3
- 2021Evading Malware Classifiers via Monte Carlo Mutant Feature Discoverycited by 2
- 2021Data Augmentation for Opcode Sequence Based Malware Detectioncited by 2
- 2021MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learningcited by 2
- 2022Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detectioncited by 2
- 2022Simulation of Attacker Defender Interaction in a Noisy Security Gamecited by 2
- 2022Machine Learning for Detecting Malware in PE Filescited by 2
- 2023Combining Generators of Adversarial Malware Examples to Increase Evasion Ratecited by 2
- 2023Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysiscited by 2
- 2023A Survey on Cross-Architectural IoT Malware Threat Huntingcited by 2
50 most cited of 114 All 114 (JSON)
- 2026Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection21 Sep 2026
- 2026Fast And Accurate Text Content File Type Identification18 Sep 2026
- 2026Delphi Scanner: efficient and interpretable static malware detection via API sequence modeling17 Sep 2026
- 2026ALIBI: Adversarial Legitimacy Injection in Binary Input against LLM Malware Analyzers17 Sep 2026
- 2026Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Context3 Aug 2026
- 2026Latent Stability Analysis of Malware Representations Under Feature-Space Perturbations27 Jul 2026
- 2026EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability27 Jul 2026
- 2026Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks16 Jul 2026
- 2026A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution29 Jun 2026
- 2026Cybersecurity is the True Frontier for Generative AI Success or Failure27 Jun 2026
- 2026MalTree: Tracing Malware Evolution from Embeddings at Scale4 Jun 2026
- 2026Large Byte Model: Teaching Language Models About Compiled Code1 Jun 2026
38 more
- 2026Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation25 May 2026
- 2026Quantifiable Uncertainty: A Stochastic Consensus Multi-Agent RAG Framework for Robust Malware Detection8 May 2026
- 2026LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution7 May 2026
- 2026Trident: Improving Malware Detection with LLMs and Behavioral Features30 Apr 2026
- 2026Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations24 Apr 2026
- 2026Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks22 Apr 2026
- 2026Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs4 Apr 2026
- 2026Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Models2 Apr 2026
- 2026Machine Learning Transferability for Malware Detection27 Mar 2026
- 2026OrgForge-IT: A Verifiable Synthetic Benchmark for LLM-Based Insider Threat Detection23 Mar 2026
- 2026Many Hands Make Light Work: An LLM-based Multi-Agent System for Detecting Malicious PyPI Packages17 Jan 2026
- 2025Binary-30K: A Heterogeneous Dataset for Deep Learning in Binary Analysis and Malware Detection27 Nov 2025
- 2025Efficient Adversarial Malware Defense via Trust-Based Raw Override and Confidence-Adaptive Bit-Depth Reduction16 Nov 2025
- 2025Binary BPE: A Family of Cross-Platform Tokenizers for Binary Analysis14 Nov 2025
- 2025DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis30 Sep 2025
- 2025Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity27 Sep 2025
- 2025BEACON: Behavioral Malware Classification with Large Language Model Embeddings and Deep Learning18 Sep 2025
- 2025Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection13 Aug 2025
- 2025Certifiably robust malware detectors by design10 Aug 2025
- 2025Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset22 Jul 2025
- 2025Efficient Malware Detection with Optimized Learning on High-Dimensional Features18 Jun 2025
- 2025Semantic Preprocessing for LLM-based Malware Analysis13 Jun 2025
- 2025Empirical Quantification of Spurious Correlations in Malware Detection11 Jun 2025
- 2025On the Security Risks of ML-based Malware Detection Systems: A Survey16 May 2025
- 2025Bridging the PLC Binary Analysis Gap: A Cross-Compiler Dataset and Neural Framework for Industrial Control Systems27 Feb 2025
- 2025Recent Advances in Malware Detection: Graph Learning and Explainability14 Feb 2025
- 2024Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems31 Oct 2024
- 2024Is Function Similarity Over-Engineered? Building a Benchmark30 Oct 2024
- 2024Model X-Ray: Detection of Hidden Malware in AI Model Weights using Few Shot Learning28 Sep 2024
- 2024Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges9 Sep 2024
- 2024On the Robustness of Malware Detectors to Adversarial Samples5 Aug 2024
- 2024Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response4 Aug 2024
- 2024On the Abuse and Detection of Polyglot Files1 Jul 2024
- 2024A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection23 May 2024
- 2024SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines23 May 2024
- 2024EGAN: Evolutional GAN for Ransomware Evasion20 May 2024
- 2024RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation5 May 2024
- 2024Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing1 May 2024
50 newest of 114 All 114 (JSON)
- 2022I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defencescited by 75
- 2021PalmTree: Learning an Assembly Language Model for Instruction Embeddingcited by 49
- 2021Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examplescited by 23
- 2024Beyond Language Models: Byte Models are Digital World Simulatorscited by 21
- 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
- 2023RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletioncited by 17
- 2024CLAP: Learning Transferable Binary Code Representations with Natural Language Supervisioncited by 17
- 2022Out of Distribution Data Detection Using Dropout Bayesian Neural Networkscited by 15
- 2023Recasting Self-Attention with Holographic Reduced Representationscited by 14
- 2021EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detectioncited by 13
- 2023Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performancecited by 12
- 2021MalNet: A Large-Scale Image Database of Malicious Softwarecited by 11
12 most cited of 111 All 111 (JSON)
- 2026Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection21 Sep 2026
- 2026Fast And Accurate Text Content File Type Identification18 Sep 2026
- 2026Delphi Scanner: efficient and interpretable static malware detection via API sequence modeling17 Sep 2026
- 2026ALIBI: Adversarial Legitimacy Injection in Binary Input against LLM Malware Analyzers17 Sep 2026
- 2026Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Context3 Aug 2026
- 2026Latent Stability Analysis of Malware Representations Under Feature-Space Perturbations27 Jul 2026
- 2026EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability27 Jul 2026
- 2026Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks16 Jul 2026
- 2026Cybersecurity is the True Frontier for Generative AI Success or Failure27 Jun 2026
- 2026MalTree: Tracing Malware Evolution from Embeddings at Scale4 Jun 2026
- 2026Large Byte Model: Teaching Language Models About Compiled Code1 Jun 2026
- 2026Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation25 May 2026
12 newest of 111 All 111 (JSON)