Awesome Anomaly Detection

Key papers

• Vuldeepecker: A Deep Learning-based System For Vulnerability Detection (2018)
  Zhen Li, Deqing Zou, Shouhuai Xu, et al.
  21.76
• A Comparison Of Static, Dynamic, And Hybrid Analysis For Malware Detection (2022)
  Anusha Damodaran, Fabio di Troia, Visaggio Aaron Corrado, et al.
  18.98
• Neural Trojans (2017)
  Yuntao Liu, Yang Xie, Ankur Srivastava
  17.78
• Ddosnet: A Deep-learning Model For Detecting Network Attacks (2020)
  Mahmoud Said Elsayed, Nhien-An Le-Khac, Soumyabrata Dev, et al.
  17.77
• Adversarial Malware Binaries: Evading Deep Learning For Malware Detection In Executables (2018)
  Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, et al.
  17.71
• 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
• Automated Poisoning Attacks And Defenses In Malware Detection Systems: An Adversarial Machine Learning Approach (2017)
  Sen Chen, Minhui Xue, Lingling Fan, et al.
  17.14
• Var-cnn: A Data-efficient Website Fingerprinting Attack Based On Deep Learning (2018)
  Sanjit Bhat, David Lu, Albert Kwon, et al.
  17.12
• Adversarial Machine Learning In Network Intrusion Detection Systems (2020)
  Elie Alhajjar, Paul Maxwell, Nathaniel D. Bastian
  17.04
• Diversevul: A New Vulnerable Source Code Dataset For Deep Learning Based Vulnerability Detection (2023)
  Yizheng Chen, Zhoujie Ding, Lamya Alowain, et al.
  16.95
• Collective Anomaly Detection Based On Long Short Term Memory Recurrent Neural Network (2017)
  Loic Bontemps, van Loi Cao, James McDermott, et al.
  16.90
• IDSGAN: Generative Adversarial Networks For Attack Generation Against Intrusion Detection (2018)
  Zilong Lin, Yong Shi, Zhi Xue
  16.79
• Yes, Machine Learning Can Be More Secure! A Case Study On Android Malware Detection (2017)
  Ambra Demontis, Marco Melis, Battista Biggio, et al.
  16.73
• Deepsweep: An Evaluation Framework For Mitigating DNN Backdoor Attacks Using Data Augmentation (2020)
  Han Qiu, Yi Zeng, Shangwei Guo, et al.
  16.51
• Supervised Feature Selection Techniques In Network Intrusion Detection: A Critical Review (2021)
  Mario di Mauro, Giovanni Galatro, Giancarlo Fortino, et al.
  16.39
• 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
• Robust Watermarking Of Neural Network With Exponential Weighting (2019)
  Ryota Namba, Jun Sakuma
  15.10
• Ransomware Classification And Detection With Machine Learning Algorithms (2022)
  Mohammad Masum, Md Jobair Hossain Faruk, Hossain Shahriar, et al.
  15.06
• FLAD: Adaptive Federated Learning For Ddos Attack Detection (2022)
  Roberto Doriguzzi-Corin, Domenico Siracusa
  15.03
• The Cross-evaluation Of Machine Learning-based Network Intrusion Detection Systems (2022)
  Giovanni Apruzzese, Luca Pajola, Mauro Conti
  14.93
• Machine Learning For Anomaly Detection And Categorization In Multi-cloud Environments (2018)
  Tara Salman, Deval Bhamare, Aiman Erbad, et al.
  14.55
• On The Effectiveness Of System Api-related Information For Android Ransomware Detection (2018)
  Michele Scalas, Davide Maiorca, Francesco Mercaldo, et al.
  14.55
• A Multi-view Context-aware Approach To Android Malware Detection And Malicious Code Localization (2017)
  Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, et al.
  14.35
• Automated Machine Learning For Deep Learning Based Malware Detection (2023)
  Austin Brown, Maanak Gupta, Mahmoud Abdelsalam
  14.19
• Detecting Malicious Powershell Commands Using Deep Neural Networks (2018)
  Danny Hendler, Shay Kels, Amir Rubin
  14.11
• Towards Adversarial Malware Detection: Lessons Learned From Pdf-based Attacks (2018)
  Davide Maiorca, Battista Biggio, Giorgio Giacinto
  14.11
• Bayesian Hyperparameter Optimization For Deep Neural Network-based Network Intrusion Detection (2022)
  Mohammad Masum, Hossain Shahriar, Hisham Haddad, et al.
  14.11
• Data Augmentation Based Malware Detection Using Convolutional Neural Networks (2020)
  Ferhat Ozgur Catak, Javed Ahmed, Kevser Sahinbas, et al.
  14.02
• A Few-shot Meta-learning Based Siamese Neural Network Using Entropy Features For Ransomware Classification (2021)
  Jinting Zhu, Julian Jang-Jaccard, Amardeep Singh, et al.
  14.02
• Htmlphish: Enabling Phishing Web Page Detection By Applying Deep Learning Techniques On HTML Analysis (2019)
  Chidimma Opara, Bo Wei, Yingke Chen
  13.93
• High Accuracy Phishing Detection Based On Convolutional Neural Networks (2020)
  Suleiman Y. Yerima, Mohammed K. Alzaylaee
  13.84
• Portable, Data-driven Malware Detection Using Language Processing And Machine Learning Techniques On Behavioral Analysis Reports (2018)
  Elmouatez Billah Karbab, Mourad Debbabi
  13.70
• On Generating Network Traffic Datasets With Synthetic Attacks For Intrusion Detection (2019)
  Carlos Garcia Cordero, Emmanouil Vasilomanolakis, Aidmar Wainakh, et al.
  13.65
• Self-supervised Vision Transformers For Malware Detection (2022)
  Sachith Seneviratne, Ridwan Shariffdeen, Sanka Rasnayaka, et al.
  13.60
• Security Vulnerability Detection Using Deep Learning Natural Language Processing (2021)
  Noah Ziems, Shaoen Wu
  13.55
• Black-box Attacks On Sequential Recommenders Via Data-free Model Extraction (2021)
  Zhenrui Yue, Zhankui He, Huimin Zeng, et al.
  13.17
• HAWK: Rapid Android Malware Detection Through Heterogeneous Graph Attention Networks (2021)
  Yiming Hei, Renyu Yang, Hao Peng, et al.
  13.11
• Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection Technique For Crypto-ransomware Early Detection (2018)
  Bander Ali Saleh Al-Rimy, Mohd Aizaini Maarof, Syed Zainudeen Mohd Shaid
  12.99
• 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
• Machine-learning Techniques For Detecting Attacks In SDN (2019)
  Mahmoud Said Elsayed, Nhien-An Le-Khac, Soumyabrata Dev, et al.
  12.68
• Antiphishstack: Lstm-based Stacked Generalization Model For Optimized Phishing URL Detection (2024)
  Saba Aslam, Hafsa Aslam, Arslan Manzoor, et al.
  12.61
• DI-NIDS: Domain Invariant Network Intrusion Detection System (2022)
  Siamak Layeghy, Mahsa Baktashmotlagh, Marius Portmann
  12.61
• A Novel Malware Detection System Based On Machine Learning And Binary Visualization (2019)
  Irina Baptista, Stavros Shiaeles, Nicholas Kolokotronis
  12.40
• An Adversarial Attack Analysis On Malicious Advertisement URL Detection Framework (2022)
  Ehsan Nowroozi, Abhishek, Mohammadreza Mohammadi, et al.
  12.33
• Leveraging Support Vector Machine For Opcode Density Based Detection Of Crypto-ransomware (2018)
  James Baldwin, Ali Dehghantanha
  12.25
• Fast & Furious: Modelling Malware Detection As Evolving Data Streams (2022)
  Fabrício Ceschin, Marcus Botacin, Heitor Murilo Gomes, et al.
  12.25
• Unsupervised Anomaly Detectors To Detect Intrusions In The Current Threat Landscape (2020)
  Tommaso Zoppi, Andrea Ceccarelli, Tommaso Capecchi, et al.
  12.25
• R-htdetector: Robust Hardware-trojan Detection Based On Adversarial Training (2022)
  Kento Hasegawa, Seira Hidano, Kohei Nozawa, et al.
  12.10
• Big Data Analysis And Distributed Deep Learning For Next-generation Intrusion Detection System Optimization (2022)
  Khloud Al Jallad, Mohamad Aljnidi, Mohammad Said Desouki
  12.10
• Explaining Black-box Android Malware Detection (2018)
  Marco Melis, Davide Maiorca, Battista Biggio, et al.
  12.10
• Adasyn-random Forest Based Intrusion Detection Model (2021)
  Zhewei Chen, Wenwen Yu, Linyue Zhou
  12.02
• Explainable AI For Android Malware Detection: Towards Understanding Why The Models Perform So Well? (2022)
  Yue Liu, Chakkrit Tantithamthavorn, Li Li, et al.
  12.02
• Adaptative Perturbation Patterns: Realistic Adversarial Learning For Robust Intrusion Detection (2022)
  João Vitorino, Nuno Oliveira, Isabel Praça
  12.02
• Query-efficient Black-box Attack Against Sequence-based Malware Classifiers (2018)
  Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, et al.
  11.93