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
• Collective Anomaly Detection Based On Long Short Term Memory Recurrent Neural Network (2017)
  Loic Bontemps, van Loi Cao, James McDermott, et al.
  16.90
• Machine Generated Text: A Comprehensive Survey Of Threat Models And Detection Methods (2022)
  Evan Crothers, Nathalie Japkowicz, Herna Viktor
  15.98
• Bayesian Optimization With Machine Learning Algorithms Towards Anomaly Detection (2020)
  Mohammadnoor Injadat, Fadi Salo, Ali Bou Nassif, et al.
  15.67
• 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
• An Empirical Study Of Deep Learning Models For Vulnerability Detection (2022)
  Benjamin Steenhoek, Md Mahbubur Rahman, Richard Jiles, et al.
  15.13
• Charbot: A Simple And Effective Method For Evading DGA Classifiers (2019)
  Jonathan Peck, Claire Nie, Raaghavi Sivaguru, et al.
  12.99
• Bypassing Backdoor Detection Algorithms In Deep Learning (2019)
  Te Juin Lester Tan, Reza Shokri
  12.81
• Using Kernel SHAP XAI Method To Optimize The Network Anomaly Detection Model (2023)
  Khushnaseeb Roshan, Aasim Zafar
  12.74
• An Adversarial Attack Analysis On Malicious Advertisement URL Detection Framework (2022)
  Ehsan Nowroozi, Abhishek, Mohammadreza Mohammadi, et al.
  12.33
• Unsupervised Anomaly Detectors To Detect Intrusions In The Current Threat Landscape (2020)
  Tommaso Zoppi, Andrea Ceccarelli, Tommaso Capecchi, et al.
  12.25
• Explaining Black-box Android Malware Detection (2018)
  Marco Melis, Davide Maiorca, Battista Biggio, et al.
  12.10
• DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach (2020)
  Abdallah Moubayed, Mohammadnoor Injadat, Abdallah Shami, et al.
  11.58
• Ast-based Deep Learning For Detecting Malicious Powershell (2018)
  Gili Rusak, Abdullah Al-Dujaili, Una-May O'Reilly
  10.97
• Dos And Ddos Mitigation Using Variational Autoencoders (2021)
  Eirik Molde Bårli, Anis Yazidi, Enrique Herrera Viedma, et al.
  10.85
• Deep Learning For Malicious Flow Detection (2018)
  Yun-Chun Chen, Yu-Jhe Li, Aragorn Tseng, et al.
  10.74
• Self-supervised Incremental Deep Graph Learning For Ethereum Phishing Scam Detection (2021)
  Shucheng Li, Fengyuan Xu, Runchuan Wang, et al.
  10.61
• Universal Detection Of Backdoor Attacks Via Density-based Clustering And Centroids Analysis (2023)
  Wei Guo, Benedetta Tondi, Mauro Barni
  10.35
• Admoe: Anomaly Detection With Mixture-of-experts From Noisy Labels (2022)
  Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, et al.
  10.35
• Backdoor Cleansing With Unlabeled Data (2022)
  Lu Pang, Tao Sun, Haibin Ling, et al.
  10.21
• Treant: Training Evasion-aware Decision Trees (2019)
  Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei, et al.
  10.07
• 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
• Enhancing 6G-IoT Network Security: A Trustworthy and Responsible AI-Driven Stacked-Hybrid Model for Attack Detection (2026)
  Anshika Sharma et al.
  10.04
• Detection Of Lddos Attacks Based On TCP Connection Parameters (2019)
  Michael Siracusano, Stavros Shiaeles, Bogdan Ghita
  9.76
• 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
• Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review (2025)
  Saeid Jamshidi et al.
  9.48
• CLEANN: Accelerated Trojan Shield For Embedded Neural Networks (2020)
  Mojan Javaheripi, Mohammad Samragh, Gregory Fields, et al.
  9.41
• Malprotect: Stateful Defense Against Adversarial Query Attacks In Ml-based Malware Detection (2023)
  Aqib Rashid, Jose Such
  9.23
• A Robust Adversary Detection-deactivation Method For Metaverse-oriented Collaborative Deep Learning (2023)
  Pengfei Li, Zhibo Zhang, Ameena S. Al-Sumaiti, et al.
  9.03
• Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks (2026)
  MD Azizul Hakim et al.
  8.86
• Evaluating Ml-based Anomaly Detection Across Datasets Of Varied Integrity: A Case Study (2024)
  Adrian Pekar, Richard Jozsa
  8.82
• Quarantine: Sparsity Can Uncover The Trojan Attack Trigger For Free (2022)
  Tianlong Chen, Zhenyu Zhang, Yihua Zhang, et al.
  8.35
• Network Intrusion Datasets: A Survey, Limitations, and Recommendations (2025)
  Patrik Goldschmidt et al.
  8.07
• Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models (2025)
  Ahmed Bensaoud and Jugal Kalita
  7.84
• Anomalyllm: Few-shot Anomaly Edge Detection For Dynamic Graphs Using Large Language Models (2024)
  Shuo Liu, di Yao, Lanting Fang, et al.
  7.81
• Large Multimodal Agents For Accurate Phishing Detection With Enhanced Token Optimization And Cost Reduction (2024)
  Fouad Trad, Ali Chehab
  7.81
• Anomaly-based intrusion detection on benchmark datasets for network security: a comprehensive evaluation (2026)
  L. Kumar et al.
  7.78
• From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories (2025)
  Ye Tian et al.
  7.55
• Robust Neural Malware Detection Models For Emulation Sequence Learning (2018)
  Rakshit Agrawal, Jack W. Stokes, Mady Marinescu, et al.
  7.50
• An Explainable Transformer-based Model For Phishing Email Detection: A Large Language Model Approach (2024)
  Mohammad Amaz Uddin, Md Mahiuddin, Iqbal H. Sarker
  7.50
• A systematic review of metaheuristics-based and machine learning-driven intrusion detection systems in IoT (2025)
  Mohammad Shamim Ahsan et al.
  7.36
• MANDERA: Malicious Node Detection in Federated Learning via Ranking (2021)
  Wanchuang Zhu et al.
  7.16
• Topological Safeguard For Evasion Attack Interpreting The Neural Networks' Behavior (2024)
  Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Iñigo Mendialdua, et al.
  6.77
• Bfclass: A Backdoor-free Text Classification Framework (2021)
  Zichao Li, Dheeraj Mekala, Chengyu Dong, et al.
  6.77
• 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.
  6.58
• BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection (2025)
  Shams Tarek et al.
  6.53
• Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing (2026)
  M. Masari et al.
  6.46
• APOLLO: A Gpt-based Tool To Detect Phishing Emails And Generate Explanations That Warn Users (2024)
  Giuseppe Desolda, Francesco Greco, Luca Viganò
  6.34
• Aspis: Robust Detection For Distributed Learning (2021)
  Konstantinos Konstantinidis, Aditya Ramamoorthy
  6.34
• 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
• A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles (2025)
  Md Ashraf Uddin et al.
  6.28
• Large Language Models Powered Malicious Traffic Detection: Architecture, Opportunities And Case Study (2025)
  Xinggong Zhang, Haotian Meng, Qingyang Li, et al.
  6.07
• Network Attack Traffic Detection With Hybrid Quantum-Enhanced Convolution Neural Network (2025)
  Zihao Wang et al.
  5.96
• Leveraging Large Language Models for Cybersecurity: Enhancing SMS Spam Detection with Robust and Context-Aware Text Classification (2025)
  Mohsen Ahmadi et al.
  5.84
• Graph-based Methods Coupled With Specific Distributional Distances For Adversarial Attack Detection (2023)
  Dwight Nwaigwe, Lucrezia Carboni, Martial Mermillod, et al.
  5.84
• Effectiveness Of Moving Target Defenses For Adversarial Attacks In Ml-based Malware Detection (2023)
  Aqib Rashid, Jose Such
  5.84
• Humanizing Machine-generated Content: Evading Ai-text Detection Through Adversarial Attack (2024)
  Ying Zhou, Ben He, Le Sun
  5.84
• CodeGraph- Malware Detection via Control Flow Graph Embeddings and Graph Neural Networks (2026)
  Chandrashekhar Medicherla et al.
  5.52