{"total":31,"rows":[{"id":"2106.15764","title":"The Threat of Offensive AI to Organizations","created":"2021-06-30","cited_by_count":38,"area":null},{"id":"2110.09610","title":"A Survey on Machine Learning Techniques for Source Code Analysis","created":"2021-10-18","cited_by_count":24,"area":null},{"id":"2206.09707","title":"The Role of Machine Learning in Cybersecurity","created":"2022-06-20","cited_by_count":22,"area":null},{"id":"2103.05292","title":"Deep Learning for Android Malware Defenses: a Systematic Literature Review","created":"2021-03-09","cited_by_count":18,"area":null},{"id":"2112.12310","title":"Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art","created":"2021-12-23","cited_by_count":18,"area":null},{"id":"2202.03277","title":"On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks","created":"2022-02-07","cited_by_count":15,"area":null},{"id":"2305.00550","title":"SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection","created":"2023-04-30","cited_by_count":13,"area":null},{"id":"2203.12980","title":"MERLIN -- Malware Evasion with Reinforcement LearnINg","created":"2022-03-24","cited_by_count":11,"area":null},{"id":"2208.12248","title":"Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations","created":"2022-08-20","cited_by_count":9,"area":null},{"id":"2207.02108","title":"AI-based Malware and Ransomware Detection Models","created":"2022-07-05","cited_by_count":7,"area":null},{"id":"2405.04095","title":"Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification","created":"2024-05-07","cited_by_count":7,"area":null},{"id":"2112.13236","title":"An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification","created":"2021-12-25","cited_by_count":5,"area":null},{"id":"2306.06228","title":"AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora","created":"2023-06-09","cited_by_count":5,"area":null},{"id":"2309.09807","title":"Efficient Concept Drift Handling for Batch Android Malware Detection Models","created":"2023-09-18","cited_by_count":5,"area":null},{"id":"2309.12421","title":"Change Management using Generative Modeling on Digital Twins","created":"2023-09-21","cited_by_count":4,"area":null},{"id":"2110.15552","title":"A Comprehensive Study on Learning-Based PE Malware Family Classification Methods","created":"2021-10-29","cited_by_count":3,"area":null},{"id":"2306.13587","title":"Creating Valid Adversarial Examples of Malware","created":"2023-06-23","cited_by_count":3,"area":null},{"id":"2405.05906","title":"Deep Multi-Task Learning for Malware Image Classification","created":"2024-05-09","cited_by_count":3,"area":null},{"id":"2405.16476","title":"KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation","created":"2024-05-26","cited_by_count":3,"area":null},{"id":"2502.11173","title":"Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems","created":"2025-02-16","cited_by_count":3,"area":null},{"id":"2306.07989","title":"A Survey on Cross-Architectural IoT Malware Threat Hunting","created":"2023-06-09","cited_by_count":2,"area":null},{"id":"2511.13808","title":"Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corpora","created":"2025-11-17","cited_by_count":2,"area":null},{"id":"2206.13889","title":"Parallel Instance Filtering for Malware Detection","created":"2022-06-28","cited_by_count":1,"area":null},{"id":"2112.02639","title":"Using Static and Dynamic Malware features to perform Malware Ascription","created":"2021-12-05","cited_by_count":0,"area":null},{"id":"2204.05632","title":"Malware Analysis with Symbolic Execution and Graph Kernel","created":"2022-04-12","cited_by_count":0,"area":null},{"id":"2206.06256","title":"On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques","created":"2022-06-13","cited_by_count":0,"area":null},{"id":"2210.08273","title":"Classification of Web Phishing Kits for early detection by platform providers","created":"2022-10-15","cited_by_count":0,"area":null},{"id":"2211.10048","title":"Clustering based opcode graph generation for malware variant detection","created":"2022-11-18","cited_by_count":0,"area":null},{"id":"2306.00284","title":"Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection","created":"2023-06-01","cited_by_count":0,"area":null},{"id":"2504.00041","title":"Imbalanced malware classification: an approach based on dynamic classifier selection","created":"2025-03-30","cited_by_count":0,"area":null},{"id":"2510.02391","title":"LLM-Generated Samples for Android Malware Detection","created":"2025-09-30","cited_by_count":0,"area":null}],"next_cursor":null}