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Keyword

API call sequences

Description: Ordered series of application programming interface calls represent program behavior and support behavioral analysis and detection of malware.
OpenAlex ID: keywords/api-call-sequences
MalDozer: Automatic framework for android malware detection using deep learning
2018 · ElMouatez Billah Karbab, Mourad Debbabi, et al. · Digital Investigation · 403
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MaMaDroid
2019 · Lucky Onwuzurike, Enrico Mariconti, et al. · ACM Transactions on Privacy and Security · 334
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A dynamic Windows malware detection and prediction method based on contextual understanding of API call sequence
2020 · Eslam Amer, Ivan Zelinka · Computers & Security · 213
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
2018 · Ishai Rosenberg, Asaf Shabtai, et al. · Lecture notes in computer science · 194
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Using spatio-temporal information in API calls with machine learning algorithms for malware detection
2009 · Faraz Ahmed, Haider Hameed, et al. · 155
Dynamic Malware Analysis with Feature Engineering and Feature Learning
2020 · Zhaoqi Zhang, Panpan Qi, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 152
MalDAE: Detecting and explaining malware based on correlation and fusion of static and dynamic characteristics
2019 · Weijie Han, Jingfeng Xue, et al. · Computers & Security · 151
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Profiling and classifying the behavior of malicious codes
2014 · Mamoun Alazab · Journal of Systems and Software · 147
Classification of Android apps and malware using deep neural networks
2017 · Robin Nix, Jian Zhang · 126
Malware detection in mobile environments based on Autoencoders and API-images
2019 · Gianni D’Angelo, Massimo Ficco, et al. · Journal of Parallel and Distributed Computing · 123
Malware detection using assembly and API call sequences
2010 · Madhu K. Shankarapani, Subbu Ramamoorthy, et al. · Journal of Computer Virology and Hacking Techniques · 116
Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense
2017 · Lingwei Chen, Yanfang Ye, et al. · European Intelligence and Security Informatics Conference · 109
Evaluating shallow and deep networks for ransomware detection and classification
2017 · R. Vinayakumar, K.P. Soman, et al. · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · 107
API-MalDetect: Automated malware detection framework for windows based on API calls and deep learning techniques
2023 · Pascal Maniriho, Abdun Naser Mahmood, et al. · Journal of Network and Computer Applications · 103
Deep learning LSTM based ransomware detection
2017 · Sumith Maniath, Aravind Ashok, et al. · 102
CruParamer: Learning on Parameter-Augmented API Sequences for Malware Detection
2022 · Xiaohui Chen, Zhiyu Hao, et al. · IEEE Transactions on Information Forensics and Security · 100
Detecting metamorphic malwares using code graphs
2010 · Jusuk Lee, Kyoochang Jeong, et al. · ACM Symposium on Applied Computing · 100
Malware Detection Based on Deep Learning of Behavior Graphs
2019 · Fei Xiao, Zhaowen Lin, et al. · Mathematical Problems in Engineering · 99
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Parameter-free probabilistic API mining across GitHub
2016 · Jaroslav Fowkes, Charles Sutton · ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE) · 99
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DMalNet: Dynamic malware analysis based on API feature engineering and graph learning
2022 · Ce Li, Zijun Cheng, et al. · Computers & Security · 90
Explainability in AI-based behavioral malware detection systems
2024 · Antonio Galli, Valerio La Gatta, et al. · Computers & Security · 88
XRan: Explainable deep learning-based ransomware detection using dynamic analysis
2024 · Sibel Gulmez, Arzu Gorgulu Kakisim, et al. · Computers & Security · 87
Malware detection and classification based on extraction of API sequences
2014 · Dolly Uppal, Rakhi Sinha, et al. · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · 87
Intelligent malware detection based on graph convolutional network
2021 · Shanxi Li, Qingguo Zhou, et al. · The Journal of Supercomputing · 83
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Classification of malware families based on runtime behaviors
2017 · Abdurrahman Pektaş, Tankut Acarman · Journal of Information Security and Applications · 81
Works count: 721
Citation count: 8,744
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