{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/malware-detection/papers/2","list_of":"/task/malware-detection","task":"Malware Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":5,"rows_per_page":100,"rows":[101,200],"of":431,"counts":{"archive_papers_tagged":431,"with_a_code_link":108,"where_syntology_ran_a_sample":11,"not_listed_spam_title":0,"listed":431,"listed_where_code_ran":11,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":8,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":8,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/malware-detection","prev":"/task/malware-detection","next":"/task/malware-detection/papers/3","papers":[{"url":"/paper/robust-neural-malware-detection-models-for","slug":"robust-neural-malware-detection-models-for","title":"Robust Neural Malware Detection Models for Emulation Sequence Learning","date":"2018-06-28","arxiv_id":"1806.10741","repositories_listed":1,"syntology":null},{"url":"/paper/arhuaco-deep-learning-and-isolation-based","slug":"arhuaco-deep-learning-and-isolation-based","title":"Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing","date":"2018-01-12","arxiv_id":"1801.04179","repositories_listed":1,"syntology":null},{"url":"/paper/improving-malware-detection-accuracy-by","slug":"improving-malware-detection-accuracy-by","title":"Improving Malware Detection Accuracy by Extracting Icon Information","date":"2017-12-10","arxiv_id":"1712.03483","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-neural-network-for-1","slug":"convolutional-neural-network-for-1","title":"Convolutional Neural Network for Classification of Malware Assembly Code","date":"2017-10-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-framework-for-validating-models-of-evasion","slug":"a-framework-for-validating-models-of-evasion","title":"Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features","date":"2017-08-28","arxiv_id":"1708.08327","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-framework-for-validating-models-of-evasion#ran","syntology_url":"https://syntology.ai/paper/1708.08327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.08327"}},"official":{"repos":["shinington/Robust-PDF-Classifier-with-Conserved-Features"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-network-based-graph-embedding-for","slug":"neural-network-based-graph-embedding-for","title":"Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection","date":"2017-08-22","arxiv_id":"1708.06525","repositories_listed":1,"syntology":null},{"url":"/paper/evasion-attacks-against-machine-learning-at","slug":"evasion-attacks-against-machine-learning-at","title":"Evasion Attacks against Machine Learning at Test Time","date":"2017-08-21","arxiv_id":"1708.06131","repositories_listed":1,"syntology":null},{"url":"/paper/black-box-attacks-against-rnn-based-malware","slug":"black-box-attacks-against-rnn-based-malware","title":"Black-Box Attacks against RNN based Malware Detection Algorithms","date":"2017-05-23","arxiv_id":"1705.08131","repositories_listed":1,"syntology":null},{"url":null,"slug":"efficient-malware-detection-with-optimized","title":"Efficient Malware Detection with Optimized Learning on High-Dimensional Features","date":"2025-06-18","arxiv_id":"2506.17309","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-quantification-of-spurious","title":"Empirical Quantification of Spurious Correlations in Malware Detection","date":"2025-06-11","arxiv_id":"2506.09662","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08383","title":"Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest","date":"2025-06-10","arxiv_id":"2506.08383","repositories_listed":0,"syntology":null},{"url":null,"slug":"system-calls-for-malware-detection-and","title":"System Calls for Malware Detection and Classification: Methodologies and Applications","date":"2025-06-02","arxiv_id":"2506.01412","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-malware-classification-of-windows-pe","title":"Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion","date":"2025-05-30","arxiv_id":"2505.24231","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-novelty-towards-generating-antigens","title":"Adapting Novelty towards Generating Antigens for Antivirus systems","date":"2025-05-24","arxiv_id":"2505.18520","repositories_listed":0,"syntology":null},{"url":null,"slug":"madcat-combating-malware-detection-under","title":"MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation","date":"2025-05-24","arxiv_id":"2505.18734","repositories_listed":0,"syntology":null},{"url":null,"slug":"malware-families-discovery-via-open-set","title":"Malware families discovery via Open-Set Recognition on Android manifest permissions","date":"2025-05-19","arxiv_id":"2505.12750","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-security-risks-of-ml-based-malware","title":"On the Security Risks of ML-based Malware Detection Systems: A Survey","date":"2025-05-16","arxiv_id":"2505.10903","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysing-safety-risks-in-llms-fine-tuned","title":"Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data","date":"2025-05-15","arxiv_id":"2505.09974","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-explanations-via-subgraph-matching-for","title":"Dual Explanations via Subgraph Matching for Malware Detection","date":"2025-04-29","arxiv_id":"2504.20904","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-approaches-to-malware-detection-a","title":"Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques","date":"2025-04-24","arxiv_id":"2504.17930","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-consistency-of-gnn-explanations-for","title":"On the Consistency of GNN Explanations for Malware Detection","date":"2025-04-22","arxiv_id":"2504.16316","repositories_listed":0,"syntology":null},{"url":null,"slug":"opcode-based-malware-classification-using","title":"OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques","date":"2025-04-18","arxiv_id":"2504.13408","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-model-llm-for-software","title":"Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering","date":"2025-04-07","arxiv_id":"2504.07137","repositories_listed":0,"syntology":null},{"url":null,"slug":"malware-detection-in-docker-containers-an","title":"Malware Detection in Docker Containers: An Image is Worth a Thousand Logs","date":"2025-04-04","arxiv_id":"2504.03238","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-vae-derived-latent-spaces-for","title":"Leveraging VAE-Derived Latent Spaces for Enhanced Malware Detection with Machine Learning Classifiers","date":"2025-03-24","arxiv_id":"2503.20803","repositories_listed":0,"syntology":null},{"url":null,"slug":"bertdetect-a-neural-topic-modelling-approach","title":"BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection","date":"2025-03-23","arxiv_id":"2503.18043","repositories_listed":0,"syntology":null},{"url":null,"slug":"trust-under-siege-label-spoofing-attacks","title":"Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection","date":"2025-03-14","arxiv_id":"2503.11841","repositories_listed":0,"syntology":null},{"url":null,"slug":"malware-detection-at-the-edge-with","title":"Malware Detection at the Edge with Lightweight LLMs: A Performance Evaluation","date":"2025-03-06","arxiv_id":"2503.04302","repositories_listed":0,"syntology":null},{"url":null,"slug":"lamd-context-driven-android-malware-detection","title":"LAMD: Context-driven Android Malware Detection and Classification with LLMs","date":"2025-02-18","arxiv_id":"2502.13055","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-advances-in-malware-detection-graph","title":"Recent Advances in Malware Detection: Graph Learning and Explainability","date":"2025-02-14","arxiv_id":"2502.10556","repositories_listed":0,"syntology":null},{"url":null,"slug":"roma-robust-malware-attribution-via-byte","title":"RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization","date":"2025-02-11","arxiv_id":"2502.07492","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-temporal-invariance-in-android","title":"Learning Temporal Invariance in Android Malware Detectors","date":"2025-02-07","arxiv_id":"2502.05098","repositories_listed":0,"syntology":null},{"url":null,"slug":"defending-against-adversarial-malware-attacks","title":"Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems","date":"2025-01-23","arxiv_id":"2501.13782","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-explainable-ai-for-effective","title":"Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic","date":"2025-01-09","arxiv_id":"2501.05387","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-vulnerability-to-malware-using","title":"Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines","date":"2025-01-05","arxiv_id":"2501.02493","repositories_listed":0,"syntology":null},{"url":null,"slug":"malware-classification-using-a-hybrid-hidden","title":"Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network","date":"2024-12-25","arxiv_id":"2412.18932","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-survey-on-adversarial-examples","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","date":"2024-12-16","arxiv_id":"2412.12217","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-malware-classification-using-qr","title":"Image-Based Malware Classification Using QR and Aztec Codes","date":"2024-12-11","arxiv_id":"2412.08514","repositories_listed":0,"syntology":null},{"url":null,"slug":"applications-of-positive-unlabeled-pu-and","title":"Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity","date":"2024-12-09","arxiv_id":"2412.06203","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-malware-detection-through","title":"Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques","date":"2024-12-04","arxiv_id":"2412.03634","repositories_listed":0,"syntology":null},{"url":null,"slug":"living-off-the-analyst-harvesting-features","title":"Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection","date":"2024-11-27","arxiv_id":"2411.18516","repositories_listed":0,"syntology":null},{"url":null,"slug":"xai-and-android-malware-models","title":"XAI and Android Malware Models","date":"2024-11-25","arxiv_id":"2411.16817","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-cost-of-model-serving-frameworks-an","title":"On the Cost of Model-Serving Frameworks: An Experimental Evaluation","date":"2024-11-15","arxiv_id":"2411.10337","repositories_listed":0,"syntology":null},{"url":null,"slug":"score-syntactic-code-representations-for","title":"SCORE: Syntactic Code Representations for Static Script Malware Detection","date":"2024-11-12","arxiv_id":"2411.08182","repositories_listed":0,"syntology":null},{"url":null,"slug":"unmasking-the-shadows-pinpoint-the","title":"Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM","date":"2024-11-08","arxiv_id":"2411.05982","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-impact-of-packing-on-machine","title":"Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems","date":"2024-10-31","arxiv_id":"2410.24017","repositories_listed":0,"syntology":null},{"url":null,"slug":"metamorphic-malware-evolution-the-potential","title":"Metamorphic Malware Evolution: The Potential and Peril of Large Language Models","date":"2024-10-31","arxiv_id":"2410.23894","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-under-strategic-adversary","title":"Classification under strategic adversary manipulation using pessimistic bilevel optimisation","date":"2024-10-26","arxiv_id":"2410.20284","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-reinforcement-learning-model-for-post","title":"A Novel Reinforcement Learning Model for Post-Incident Malware Investigations","date":"2024-10-19","arxiv_id":"2410.15028","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-xiot-malware-analysis-a","title":"Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges","date":"2024-10-14","arxiv_id":"2410.13894","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-approach-to-malicious-code-detection","title":"A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion","date":"2024-10-12","arxiv_id":"2410.09401","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-android-malware-with-a-fraction-of","title":"Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach","date":"2024-09-28","arxiv_id":"2409.19234","repositories_listed":0,"syntology":null},{"url":null,"slug":"revolutionizing-payload-inspection-a-self","title":"Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples","date":"2024-09-26","arxiv_id":"2409.18219","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-visualized-malware-detection-framework-with","title":"A Visualized Malware Detection Framework with CNN and Conditional GAN","date":"2024-09-22","arxiv_id":"2409.14439","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-novel-malicious-packet-recognition-a","title":"Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach","date":"2024-09-17","arxiv_id":"2409.11254","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-static-feature-based-android","title":"Revisiting Static Feature-Based Android Malware Detection","date":"2024-09-11","arxiv_id":"2409.07397","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-malware-analysis-concepts","title":"Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges","date":"2024-09-09","arxiv_id":"2409.13723","repositories_listed":0,"syntology":null},{"url":null,"slug":"android-malware-detection-based-on-rgb-images","title":"Android Malware Detection Based on RGB Images and Multi-feature Fusion","date":"2024-08-29","arxiv_id":"2408.16555","repositories_listed":0,"syntology":null},{"url":null,"slug":"obfuscated-memory-malware-detection","title":"Obfuscated Memory Malware Detection","date":"2024-08-23","arxiv_id":"2408.12866","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-outlines-for-code-literate","title":"Natural Language Outlines for Code: Literate Programming in the LLM Era","date":"2024-08-09","arxiv_id":"2408.04820","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-malware-detection-using-deep","title":"A Survey of Malware Detection Using Deep Learning","date":"2024-07-27","arxiv_id":"2407.19153","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-ai-based-intrusion-detection","title":"Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions","date":"2024-07-21","arxiv_id":"2408.03335","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-the-application-of-generative","title":"A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes","date":"2024-07-11","arxiv_id":"2407.08839","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-new-obfuscated-malware-variants-a","title":"Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach","date":"2024-07-07","arxiv_id":"2407.07918","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-abuse-and-detection-of-polyglot-files","title":"On the Abuse and Detection of Polyglot Files","date":"2024-07-01","arxiv_id":"2407.01529","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-representation-learning-with-3","title":"Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks","date":"2024-06-07","arxiv_id":"2406.04733","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-formulation-for-zeroth-order","title":"A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection","date":"2024-05-23","arxiv_id":"2405.14519","repositories_listed":0,"syntology":null},{"url":null,"slug":"slifer-investigating-performance-and","title":"SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines","date":"2024-05-23","arxiv_id":"2405.14478","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-ai-and-large-language-models-for","title":"Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities","date":"2024-05-21","arxiv_id":"2405.12750","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-in-pre-trained-large","title":"Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls","date":"2024-05-15","arxiv_id":"2405.09318","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-task-learning-for-malware-image","title":"Deep Multi-Task Learning for Malware Image Classification","date":"2024-05-09","arxiv_id":"2405.05906","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-lstm-and-gan-for-modern-malware","title":"Leveraging LSTM and GAN for Modern Malware Detection","date":"2024-05-07","arxiv_id":"2405.04373","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-android-malware-from-neural","title":"Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid","date":"2024-05-06","arxiv_id":"2405.03620","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-clustering-of-known-and-emerging","title":"Online Clustering of Known and Emerging Malware Families","date":"2024-05-06","arxiv_id":"2405.03298","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-malware-detection-with-tailored","title":"Explainable Malware Detection with Tailored Logic Explained Networks","date":"2024-05-05","arxiv_id":"2405.03009","repositories_listed":0,"syntology":null},{"url":null,"slug":"certified-adversarial-robustness-of-machine","title":"Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing","date":"2024-05-01","arxiv_id":"2405.00392","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-windows-malware","title":"Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research","date":"2024-04-29","arxiv_id":"2404.18541","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-real-time-hardware-based","title":"Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation","date":"2024-04-19","arxiv_id":"2404.13125","repositories_listed":0,"syntology":null},{"url":null,"slug":"counteracting-concept-drift-by-learning-with","title":"Counteracting Concept Drift by Learning with Future Malware Predictions","date":"2024-04-14","arxiv_id":"2404.09352","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-of-lightweight-malware-detection","title":"Optimization of Lightweight Malware Detection Models For AIoT Devices","date":"2024-04-06","arxiv_id":"2404.04567","repositories_listed":0,"syntology":null},{"url":null,"slug":"obfuscated-malware-detection-investigating","title":"Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis","date":"2024-04-03","arxiv_id":"2404.02372","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-malware-detection-for-embedded","title":"Generative AI-Based Effective Malware Detection for Embedded Computing Systems","date":"2024-04-02","arxiv_id":"2404.02344","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-transformer-based-framework-for-payload","title":"A Transformer-Based Framework for Payload Malware Detection and Classification","date":"2024-03-27","arxiv_id":"2403.18223","repositories_listed":0,"syntology":null},{"url":null,"slug":"holographic-global-convolutional-networks-for","title":"Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection","date":"2024-03-23","arxiv_id":"2403.17978","repositories_listed":0,"syntology":null},{"url":null,"slug":"shifting-the-lens-detecting-malware-in-npm","title":"Leveraging Large Language Models to Detect npm Malicious Packages","date":"2024-03-18","arxiv_id":"2403.12196","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-evaluation-of-mal-api-2019","title":"Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection","date":"2024-03-04","arxiv_id":"2403.02232","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-android-malware-detection-through","title":"Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks","date":"2024-03-01","arxiv_id":"2403.00890","repositories_listed":0,"syntology":null},{"url":null,"slug":"use-of-multi-cnns-for-section-analysis-in","title":"Use of Multi-CNNs for Section Analysis in Static Malware Detection","date":"2024-02-06","arxiv_id":"2402.04102","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-anomaly-detection-via","title":"Weakly Supervised Anomaly Detection via Knowledge-Data Alignment","date":"2024-02-06","arxiv_id":"2402.03785","repositories_listed":0,"syntology":null},{"url":null,"slug":"unraveling-the-key-of-machine-learning","title":"Unraveling the Key of Machine Learning Solutions for Android Malware Detection","date":"2024-02-05","arxiv_id":"2402.02953","repositories_listed":0,"syntology":null},{"url":null,"slug":"evading-deep-learning-based-malware-detectors","title":"Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach","date":"2024-02-04","arxiv_id":"2402.02600","repositories_listed":0,"syntology":null},{"url":null,"slug":"actdroid-an-active-learning-framework-for","title":"ActDroid: An active learning framework for Android malware detection","date":"2024-01-30","arxiv_id":"2401.16982","repositories_listed":0,"syntology":null},{"url":null,"slug":"morph-towards-automated-concept-drift","title":"MORPH: Towards Automated Concept Drift Adaptation for Malware Detection","date":"2024-01-23","arxiv_id":"2401.12790","repositories_listed":0,"syntology":null},{"url":null,"slug":"malware-detection-in-iot-systems-using","title":"Malware Detection in IOT Systems Using Machine Learning Techniques","date":"2023-12-29","arxiv_id":"2312.17683","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-effect-sizes-in-malware-detection-make","title":"Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!","date":"2023-12-25","arxiv_id":"2312.15813","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-malicious-signatures-in-software","title":"Discovering Malicious Signatures in Software from Structural Interactions","date":"2023-12-19","arxiv_id":"2312.12667","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-an-in-depth-detection-of-malware","title":"Towards an in-depth detection of malware using distributed QCNN","date":"2023-12-19","arxiv_id":"2312.12161","repositories_listed":0,"syntology":null},{"url":null,"slug":"android-malware-detection-with-unbiased","title":"Android Malware Detection with Unbiased Confidence Guarantees","date":"2023-12-17","arxiv_id":"2312.11559","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-malware-classification-survey-on","title":"A Malware Classification Survey on Adversarial Attacks and Defences","date":"2023-12-15","arxiv_id":"2312.09636","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-high-dimensional-text-classifiers","title":"Explaining high-dimensional text classifiers","date":"2023-11-22","arxiv_id":"2311.13454","repositories_listed":0,"syntology":null}],"record_sha256":"9f44059c5b62962dcda94e2095880c5cbb423b5e7a68c569bccc5fac2eb2cac7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}