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Malware detection has become a major concern due to the increasing number and complexity of malware.
The program dependence graph and its use in optimization
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Signature verification using a" siamese" time delay neural network
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Convolutional networks for images, speech, and time series
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The pagerank citation ranking: Bringing order to the web
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Gene selection for cancer classification using support vector machines
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Code obfuscation literature survey
Arini Balakrishnan and Chloe Schulze · 2005
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Dynamic analysis of malicious code
Ulrich Bayer, Andreas Moser, Christopher Kruegel, and Engin Kirda · 2006
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Polymorphic worm detection using structural information of executables
Christopher Kruegel, Engin Kirda, Darren Mutz, William Robertson, and Giovanni Vigna · 2006
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg · 2009
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and Léon Bottou · 2010
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Detecting metamorphic malwares using code graphs
Jusuk Lee, Kyoochang Jeong, and Heejo Lee · 2010
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Improving malware classification: bridging the static/dynamic gap
Blake Anderson, Curtis Storlie, and Terran Lane · 2012
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https://www.w3.org/TR/2012/WD-html-markup-20121025/elements.html , [Accessed on 01/10/2023]
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http://www.xssed.com/ , [Accessed on 02/06/2023]
XSSed | Cross Site Scripting (XSS) attacks information and archive · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Structural detection of android malware using embedded call graphs
Hugo Gascon, Fabian Yamaguchi, Daniel Arp, and Konrad Rieck · 2013
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Droidminer: Automated mining and characterization of fine-grained malicious behaviors in android applications
Chao Yang, Zhaoyan Xu, Guofei Gu, Vinod Yegneswaran, and Phillip Porras · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Drebin: Effective and explainable detection of android malware in your pocket
Daniel Arp, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck, and CERT Siemens · 2014
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Robust static analysis of portable executable malware
Katja Hahn and I Register · 2014
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An empirical comparison of botnet detection methods
Sebastian Garcia, Martin Grill, Jan Stiborek, and Alejandro Zunino · 2014
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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Be sensitive to your errors: Chaining neyman-pearson criteria for automated malware classification
Guanhua Yan · 2015
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Android malware analysis approach based on control flow graphs and machine learning algorithms
Mehmet Ali Atici, Seref Sagiroglu, and Ibrahim Alper Dogru · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
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Androzoo: Collecting millions of android apps for the research community
Kevin Allix, Tegawendé F Bissyandé, Jacques Klein, and Yves Le Traon · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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A survey of heterogeneous information network analysis
Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and S Yu Philip · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Artificial immune system: applications in computer security
Ying Tan · 2016
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A survey on malware detection using data mining techniques
Yanfang Ye, Tao Li, Donald Adjeroh, and S Sitharama Iyengar · 2017
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Hindroid: An intelligent android malware detection system based on structured heterogeneous information network
Shifu Hou, Yanfang Ye, Yangqiu Song, and Melih Abdulhayoglu · 2017
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Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Topology adaptive graph convolutional networks
Jian Du, Shanghang Zhang, Guanhang Wu, José MF Moura, and Soummya Kar · 2017
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Deep ground truth analysis of current android malware
Fengguo Wei, Yuping Li, Sankardas Roy, Xinming Ou, and Wu Zhou · 2017
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https://www.unb.ca/cic/research/applications.html , [Accessed on 02/07/2023]
Applications | Research | Canadian Institute for Cybersecurity | UNB · 2017
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Detecting http-based application layer dos attacks on web servers in the presence of sampling
Hossein Hadian Jazi, Hugo Gonzalez, Natalia Stakhanova, and Ali A Ghorbani · 2017
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Adversarial machine learning in malware detection: Arms race between evasion attack and defense
Lingwei Chen, Yanfang Ye, and Thirimachos Bourlai · 2017
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Evading machine learning malware detection
Hyrum S Anderson, Anant Kharkar, Bobby Filar, and Phil Roth · 2017
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A review on the use of deep learning in android malware detection
Abdelmonim Naway and Yuancheng Li · 2018
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Make evasion harder: an intelligent android malware detection system
Shifu Hou, Yanfang Ye, Yangqiu Song, and Melih Abdulhayoglu · 2018
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Android malware detection using large-scale network representation learning
Rui Zhu, Chenglin Li, Di Niu, Hongwen Zhang, and Husam Kinawi · 2018
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Dlgraph: Malware detection using deep learning and graph embedding
Haodi Jiang, Turki Turki, and Jason TL Wang · 2018
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Amandroid: A precise and general inter-component data flow analysis framework for security vetting of android apps
Fengguo Wei, Sankardas Roy, and Xinming Ou · 2018
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https://androguard.readthedocs.io/en/latest/ , [Accessed on 01/17/2023]
Welcome to Androguard’s documentation! — Androguard 3.4.0 documentation · 2018
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Contextual graph markov model: A deep and generative approach to graph processing
Davide Bacciu, Federico Errica, and Alessio Micheli · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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Toward developing a systematic approach to generate benchmark android malware datasets and classification
Arash Habibi Lashkari, Andi Fitriah A Kadir, Laya Taheri, and Ali A Ghorbani · 2018
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Microsoft malware classification challenge
Royi Ronen, Marian Radu, Corina Feuerstein, Elad Yom-Tov, and Mansour Ahmadi · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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https://tianchi.aliyun.com/competition/entrance/231694/introduction , [Accessed on 14/07/2023]
Alibaba Cloud Malware Detection Based On Behaviors · 2018
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Towards sparse hierarchical graph classifiers
Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, and Pietro Liò · 2018
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Tranco: A research-oriented top sites ranking hardened against manipulation
Victor Le Pochat, Tom Van Goethem, Samaneh Tajalizadehkhoob, Maciej Korczyński, and Wouter Joosen · 2018
Cited alongside, same era.
https://www.kaggle.com/datasets/cheedcheed/top1m , [Accessed on 02/06/2023]
Identifying att&ck tactics in android malware control flow graph through graph representation learning and interpretability
Jeffrey Fairbanks, Andres Orbe, Christine Patterson, Janet Layne, Edoardo Serra, and Marion Scheepers · 2021
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Learning features from enhanced function call graphs for android malware detection
Minghui Cai, Yuan Jiang, Cuiying Gao, Heng Li, and Wei Yuan · 2021
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Detecting and categorizing android malware with graph neural networks
Peng Xu, Claudia Eckert, and Apostolis Zarras · 2021
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Android malware detection using function call graph with graph convolutional networks
KV Vinayaka and CD Jaidhar · 2021
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Robust malware classification via deep graph networks on call graph topologies
Federico Errica, Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, and Alessio Micheli · 2021
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Alexa Top 1 Million Sites | Kaggle · 2018
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Toward generating a new intrusion detection dataset and intrusion traffic characterization
Iman Sharafaldin, Arash Habibi Lashkari, and Ali A Ghorbani · 2018
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
Cited alongside, same era.
Deceiving end-to-end deep learning malware detectors using adversarial examples
Felix Kreuk, Assi Barak, Shir Aviv-Reuven, Moran Baruch, Benny Pinkas, and Joseph Keshet · 2018
Cited alongside, same era.
Black-box attacks against rnn based malware detection algorithms
Weiwei Hu and Ying Tan · 2018
Cited alongside, same era.
Generic black-box end-to-end attack against state of the art api call based malware classifiers
Ishai Rosenberg, Asaf Shabtai, Lior Rokach, and Yuval Elovici · 2018
Cited alongside, same era.
Enhancing machine learning based malware detection model by reinforcement learning
Cangshuai Wu, Jiangyong Shi, Yuexiang Yang, and Wenhua Li · 2018
Cited alongside, same era.
Adversarial attack and defense on graph data: A survey
Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S Yu, Lifang He, and Bo Li · 2018
Cited alongside, same era.
Malware detection based on graph attention networks for intelligent transportation systems
Cagatay Catal, Hakan Gunduz, and Alper Ozcan · 2021
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Android-coco: Android malware detection with graph neural network for byte-and native-code
Peng Xu and Asbat El Khairi · 2021
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Nf-gnn: network flow graph neural networks for malware detection and classification
Julian Busch, Anton Kocheturov, Volker Tresp, and Thomas Seidl · 2021
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Hawkeye: cross-platform malware detection with representation learning on graphs
Peng Xu, Youyi Zhang, Claudia Eckert, and Apostolis Zarras · 2021
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A hierarchical graph-based neural network for malware classification
Shuai Wang, Yuran Zhao, Gongshen Liu, and Bo Su · 2021
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Utilizing graph machine learning within drug discovery and development
Thomas Gaudelet, Ben Day, Arian R Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy BR Hayter, Richard Vickers, Charles Roberts, Jian Tang, et al · 2021
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Attributed heterogeneous graph neural network for malicious domain detection
Shuai Zhang, Zhou Zhou, Da Li, Youbing Zhong, Qingyun Liu, Wei Yang, and Shu Li · 2021
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Phishing web page detection with html-level graph neural network
Linshu Ouyang and Yongzheng Zhang · 2021
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Gdroid: Android malware detection and classification with graph convolutional network
Han Gao, Shaoyin Cheng, and Weiming Zhang · 2021
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Hawk: Rapid android malware detection through heterogeneous graph attention networks
Yiming Hei, Renyu Yang, Hao Peng, Lihong Wang, Xiaolin Xu, Jianwei Liu, Hong Liu, Jie Xu, and Lichao Sun · 2021
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https://github.com/Djack1010/graph4apk , [Accessed on 01/17/2023]
Djack1010/graph4apk · 2021
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Inferential sir-gn: Scalable graph representation learning
Janet Layne and Edoardo Serra · 2021
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On explainability of graph neural networks via subgraph explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji · 2021
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Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection
Luca Demetrio, Scott E Coull, Battista Biggio, Giovanni Lagorio, Alessandro Armando, and Fabio Roli · 2021
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Structural attack against graph based android malware detection
Kaifa Zhao, Hao Zhou, Yulin Zhu, Xian Zhan, Kai Zhou, Jianfeng Li, Le Yu, Wei Yuan, and Xiapu Luo · 2021
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A survey of explainable graph neural networks for cyber malware analysis
Dana Warmsley, Alex Waagen, Jiejun Xu, Zhining Liu, and Hanghang Tong · 2022
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Deepcatra: Learning flow-and graph-based behaviors for android malware detection
Yafei Wu, Jian Shi, Peicheng Wang, Dongrui Zeng, and Cong Sun · 2022
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Graph neural network-based android malware classification with jumping knowledge
Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, and Marius Portmann · 2022
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Malware detection framework based on graph variational autoencoder extracted embeddings from api-call graphs
Hakan Gunduz · 2022
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Robust android malware detection based on subgraph network and denoising gcn network
Xiaofeng Lu, Jinglun Zhao, and Pietro Lio · 2022
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Iot-based android malware detection using graph neural network with adversarial defense
Rahul Yumlembam, Biju Issac, Seibu Mary Jacob, and Longzhi Yang · 2022
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Malgraph: Hierarchical graph neural networks for robust windows malware detection
Xiang Ling, Lingfei Wu, Wei Deng, Zhenqing Qu, Jiangyu Zhang, Sheng Zhang, Tengfei Ma, Bin Wang, Chunming Wu, and Shouling Ji · 2022
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Intelligent malware detection based on graph convolutional network
Shanxi Li, Qingguo Zhou, Rui Zhou, and Qingquan Lv · 2022
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Dmalnet: Dynamic malware analysis based on api feature engineering and graph learning
Ce Li, Zijun Cheng, He Zhu, Leiqi Wang, Qiujian Lv, Yan Wang, Ning Li, and Degang Sun · 2022
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Fewm-hgcl: Few-shot malware variants detection via heterogeneous graph contrastive learning
Chen Liu, Bo Li, Jun Zhao, Ziyang Zhen, Xudong Liu, and Qunshi Zhang · 2022
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Cfgexplainer: Explaining graph neural network-based malware classification from control flow graphs
Jerome Dinal Herath, Priti Prabhakar Wakodikar, Ping Yang, and Guanhua Yan · 2022
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Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui · 2022
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Graph neural network for traffic forecasting: A survey
Weiwei Jiang and Jiayun Luo · 2022
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Graphddos: Effective ddos attack detection using graph neural networks
Yuzhen Li, Renjie Li, Zhou Zhou, Jiang Guo, Wei Yang, Meijie Du, and Qingyun Liu · 2022
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Phishgnn: A phishing website detection framework using graph neural networks
Tristan Bilot, Grégoire Geis, and Badis Hammi · 2022
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Graphxss: an efficient xss payload detection approach based on graph convolutional network
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Jstrong: Malicious javascript detection based on code semantic representation and graph neural network
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