Malicious mining code detection based on ensemble learning in cloud computing environment

[1]  Zhiqin Zhu,et al.  Power system structure optimization based on reinforcement learning and sparse constraints under DoS attacks in cloud environments , 2021, Simul. Model. Pract. Theory.

[2]  Antonella Santone,et al.  Model checking and machine learning techniques for HummingBad mobile malware detection and mitigation , 2020, Simul. Model. Pract. Theory.

[3]  Feng Zhu,et al.  A computing resources prediction approach based on ensemble learning for complex system simulation in cloud environment , 2021, Simul. Model. Pract. Theory.

[4]  Abdul Razaque,et al.  Deep recurrent neural network for IoT intrusion detection system , 2020, Simul. Model. Pract. Theory.

[5]  Dawei Zhao,et al.  A weighted network community detection algorithm based on deep learning , 2021, Appl. Math. Comput..

[6]  Shen Su,et al.  Real-Time Lateral Movement Detection Based on Evidence Reasoning Network for Edge Computing Environment , 2019, IEEE Transactions on Industrial Informatics.

[7]  Zhiyuan Tan,et al.  A Novel Web Attack Detection System for Internet of Things via Ensemble Classification , 2021, IEEE Transactions on Industrial Informatics.

[8]  Xiaojiang Du,et al.  Detective: Automatically identify and analyze malware processes in forensic scenarios via DLLs , 2015, 2015 IEEE International Conference on Communications (ICC).

[9]  Chandrasekar Ravi,et al.  Malware Detection using Windows Api Sequence and Machine Learning , 2012 .

[10]  Weihong Han,et al.  A Novel Solutions for Malicious Code Detection and Family Clustering Based on Machine Learning , 2019, IEEE Access.

[11]  Mohsen Guizani,et al.  Assured Data Deletion With Fine-Grained Access Control for Fog-Based Industrial Applications , 2018, IEEE Transactions on Industrial Informatics.

[12]  Rakhi Sinha,et al.  Malware detection and classification based on extraction of API sequences , 2014, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[13]  Xiaojiang Du,et al.  CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques , 2021, IEEE Internet of Things Journal.

[14]  Dan Wang,et al.  Intelligent Cognitive Radio in 5G: AI-Based Hierarchical Cognitive Cellular Networks , 2019, IEEE Wireless Communications.

[15]  Mohsen Guizani,et al.  Bus-Trajectory-Based Street-Centric Routing for Message Delivery in Urban Vehicular Ad Hoc Networks , 2018, IEEE Transactions on Vehicular Technology.

[16]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[17]  Yuval Elovici,et al.  Detecting unknown malicious code by applying classification techniques on OpCode patterns , 2012, Security Informatics.

[18]  Dawei Zhao,et al.  Functional immunization of networks based on message passing , 2020, Appl. Math. Comput..

[19]  Xiaojiang Du,et al.  IoT malicious traffic identification using wrapper-based feature selection mechanisms , 2020, Comput. Secur..

[20]  Athanasios V. Vasilakos,et al.  Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks , 2019, Future Gener. Comput. Syst..

[21]  Sheng Wen,et al.  Software Vulnerability Detection Using Deep Neural Networks: A Survey , 2020, Proceedings of the IEEE.

[22]  Xiaojiang Du,et al.  Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city , 2020, Future Gener. Comput. Syst..

[23]  Zheng Qin,et al.  A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding , 2019, Comput. Secur..

[24]  Xiao Chen,et al.  Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection , 2018, IEEE Transactions on Information Forensics and Security.