Key papers

• Anomal-e: A Self-supervised Network Intrusion Detection System Based On Graph Neural Networks (2022)
  Evan Caville, Wai Weng Lo, Siamak Layeghy, et al.
  17.37
• Support Vector Machines Under Adversarial Label Contamination (2022)
  Huang Xiao, Battista Biggio, Blaine Nelson, et al.
  16.53
• Adversary Resistant Deep Neural Networks With An Application To Malware Detection (2016)
  Qinglong Wang, Wenbo Guo, Kaixuan Zhang, et al.
  15.37
• Learning The PE Header, Malware Detection With Minimal Domain Knowledge (2017)
  Edward Raff, Jared Sylvester, Charles Nicholas
  15.13
• Enhancing Threat Detection Using Artificial Intelligence in Modern Cybersecurity Systems Using SPSS Statistics (2026)
  Rajendar Dommeti
  13.37
• Using Kernel SHAP XAI Method To Optimize The Network Anomaly Detection Model (2023)
  Khushnaseeb Roshan, Aasim Zafar
  12.74
• Enhancing 6G-IoT Network Security: A Trustworthy and Responsible AI-Driven Stacked-Hybrid Model for Attack Detection (2026)
  Anshika Sharma et al.
  11.41
• Ast-based Deep Learning For Detecting Malicious Powershell (2018)
  Gili Rusak, Abdullah Al-Dujaili, Una-May O'Reilly
  10.97
• Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review (2025)
  Saeid Jamshidi et al.
  10.90
• Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection (2021)
  Shucheng Li, Fengyuan Xu, Runchuan Wang, et al.
  10.61
• Reverse Engineering Imperceptible Backdoor Attacks On Deep Neural Networks For Detection And Training Set Cleansing (2020)
  Zhen Xiang, David J. Miller, George Kesidis
  10.07
• Efficient Toxic Content Detection By Bootstrapping And Distilling Large Language Models (2023)
  Jiang Zhang, Qiong Wu, Yiming Xu, et al.
  9.76
• Adversarial Robustness Of Neural-statistical Features In Detection Of Generative Transformers (2022)
  Evan Crothers, Nathalie Japkowicz, Herna Viktor, et al.
  9.59
• Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models (2025)
  Ahmed Bensaoud and Jugal Kalita
  8.98
• Anomaly-based intrusion detection on benchmark datasets for network security: a comprehensive evaluation (2026)
  L. Kumar et al.
  8.75
• Robust Neural Malware Detection Models For Emulation Sequence Learning (2018)
  Rakshit Agrawal, Jack W. Stokes, Mady Marinescu, et al.
  7.50
• CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network For Network Intrusion Detection (2025)
  Md Abrar Jahin, Shahriar Soudeep, Fahmid Al Farid, et al.
  7.46
• Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing (2026)
  M. Masari et al.
  7.20
• A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles (2025)
  Md Ashraf Uddin et al.
  7.12
• Network Attack Traffic Detection With Hybrid Quantum-Enhanced Convolution Neural Network (2025)
  Zihao Wang et al.
  6.75
• A Cyber Threat Intelligence Sharing Scheme Based On Federated Learning For Network Intrusion Detection (2021)
  Mohanad Sarhan, Siamak Layeghy, Nour Moustafa, et al.
  6.34
• CodeGraph- Malware Detection via Control Flow Graph Embeddings and Graph Neural Networks (2026)
  Chandrashekhar Medicherla et al.
  6.10
• Attention-enhanced BiLSTM-ANN framework with CNN-based feature selection for advanced threat detection (2026)
  Mohammed Tayebi et al.
  6.10
• Explainable Machine Learning for Malware Detection: A SHAP-Based LightGBM Framework (2026)
  Abdullah Al Siam et al.
  6.10
• AI‐Driven Threat Detection: Synergizing Edge Honeypots and IDS in SDN‐Enabled Edge Computing (2026)
  Afef Slimani et al.
  6.10
• STS-AT: A Structured Tensor Flow Adversarial Training Framework for Robust Intrusion Detection (2026)
  Juntong Zhu et al.
  6.10
• Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger (2026)
  Frederick T. Sheldon et al.
  6.10
• A Sample-Based, Multistage Machine Learning Pipeline for Scalable IoT Threat Detection (2026)
  Marcelo V. C. Aragão et al.
  6.10
• Geometry-Aware Multi-view Malware Detection Using Gromov–Wasserstein Fusion (2026)
  Vijay H. Kalmani et al.
  6.10
• Boosting Vulnerability Detection Of Llms Via Curriculum Preference Optimization With Synthetic Reasoning Data (2025)
  Xin-Cheng Wen, Yijun Yang, Cuiyun Gao, et al.
  6.05
• Enhancing JavaScript Malware Detection through Weighted Behavioral DFAs (2025)
  Pedro Pereira et al.
  6.02
• DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks (2025)
  Jing Chen et al.
  5.97
• Evaluating LLaMA 3.2 for Software Vulnerability Detection (2025)
  Jos\'e Gon\c{c}alves et al.
  5.91
• Sparse Autoencoders as Plug-and-Play Firewalls for Adversarial Attack Detection in VLMs (2026)
  Hao Wang et al.
  5.90
• MLRan: A Behavioural Dataset for Ransomware Analysis and Detection (2025)
  Faithful Chiagoziem Onwuegbuche et al.
  5.89
• Comparative Analysis Of Imbalanced Malware Byteplot Image Classification Using Transfer Learning (2023)
  Jayasudha M, Ayesha Shaik, Gaurav Pendharkar, et al.
  5.84
• Beyond the QBER Threshold: A Temporal QBER Based Machine Learning Framework for Multi Attack Detection in BB84 QKD (2026)
  Isha et al.
  5.54
• When Modalities Fail to Tango: Conformal Backdoor Detection in Multimodal Contrastive Learning (2026)
  Yiming Chen et al.
  5.54
• FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks (2026)
  An Khanh Bui et al.
  5.54
• Checked-In Secret Detection: Strings Are All You Need (2026)
  Zhengdong Huang et al.
  5.54
• Generative AI and Federated Learning for Intrusion Detection Systems: A Survey (2026)
  Jiefei Liu et al.
  5.49
• Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytes (2026)
  Derek Everett et al.
  5.49
• Event Burst Trigger: An Availability Backdoor Attack on Event-Based SNN Object Detection (2026)
  Jaesun Baek et al.
  5.49
• Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning (2026)
  Xingzhi Qian et al.
  5.49
• Leveraging Interpretable Tsetlin Machine for PDF Malware Detection (2026)
  Rahul Jaiswal et al.
  5.49
• ToolGuardian: Declarative Security for AI Agent-Tool Interactions (2026)
  Arun Ravindran et al.
  5.49
• Ethereum NFT Smart Contracts: Knowledge-Guided Vulnerability Detection with LLM and Code Slicing (2026)
  Deyu Yang et al.
  5.49
• Not In My Git Yard: Catching Backdoors at Commit and Release Time (2026)
  Dimitri Kokkonis (LSL) et al.
  5.49
• MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair (2026)
  Xuanze Chen et al.
  5.49
• Bending the Curve: Operational Cyber Epidemiology for Ransomware (2026)
  Stephen V Flowerday et al.
  5.49
• DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors (2026)
  Christian Scano et al.
  5.43
• A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows (2025)
  Alberto Miguel-Diez et al.
  5.42
• MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection (2025)
  Kayua Oleques Paim and Angelo Gaspar Diniz Nogueira and Diego Kreutz and Weverton Cordeiro and Rodrigo Brandao Mansilha
  5.26
• VISION: Robust And Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation (2025)
  David Egea, Barproda Halder, Sanghamitra Dutta
  4.98
• CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection (2025)
  Richard A. Dubniczky et al.
  4.81
• SNR-Adaptive Optimal Threshold Design for Energy Detection in Dynamic Spectrum Access (2026)
  Sushila Dhaka et al.
  4.75
• Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence (2026)
  Jose Luis Vela Alonso et al.
  4.75
• Data-driven mitigation of catastrophic forgetting in dynamic physical layer attack detection (2026)
  Aleksandra Knapi\'nska et al.
  4.75
• Detecting Adversarial Evasion Attacks Against Autoencoder-Based Network Intrusion Detection Systems (2026)
  Niklas Bunzel et al.
  4.75
• When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems (2026)
  Haowen Xu et al.
  4.75