Machine Learning and Deep Learning Methods for Cybersecurity
暂无分享,去创建一个
Chunhua Wang | Yang Xin | Hongliang Zhu | Haixia Hou | Zhi Liu | Mingcheng Gao | Yuling Chen | Lingshuang Kong | Yanmiao Li | Hongliang Zhu | Yuling Chen | Zhi Liu | Yanmiao Li | Yang Xin | Mingcheng Gao | Lingshuang Kong | Haixia Hou | Chunhua Wang
[1] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[2] Abien Fred Agarap. A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data , 2017, ICMLC.
[3] K. Raghuveer,et al. Confederation of FCM clustering, ANN and SVM techniques to implement hybrid NIDS using corrected KDD cup 99 dataset , 2014, 2014 International Conference on Communication and Signal Processing.
[4] Rakhi D. Wajgi,et al. Classification of Attacks Using Support Vector Machine (SVM) on KDDCUP'99 IDS Database , 2015, 2015 Fifth International Conference on Communication Systems and Network Technologies.
[5] Vineet Richariya,et al. Intrusion Detection in KDD99 Dataset using SVM-PSO and Feature Reduction with Information Gain , 2014 .
[6] Xinghuo Yu,et al. Evaluating Host-Based Anomaly Detection Systems: Application of the Frequency-Based Algorithms to ADFA-LD , 2014, NSS.
[7] Carla Purdy,et al. Toward an Online Anomaly Intrusion Detection System Based on Deep Learning , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).
[8] Verónica Bolón-Canedo,et al. Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset , 2011, Expert Syst. Appl..
[9] Yuefei Zhu,et al. A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks , 2017, IEEE Access.
[10] Haengnam Sung,et al. A Comparative Study on the Performance of Intrusion Detection using Decision Tree and Artificial Neural Network Models , 2015 .
[11] Shikha Agrawal,et al. A Survey on Intrusion Detection Techniques in MANET , 2015, 2015 International Conference on Computational Intelligence and Communication Networks (CICN).
[12] Ali A. Ghorbani,et al. A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.
[13] Zhifang Liu,et al. A New Method of Transductive SVM-Based Network Intrusion Detection , 2010, CCTA.
[14] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[15] Adel Ammar. A Decision Tree Classifier for Intrusion Detection Priority Tagging , 2015 .
[16] Farrukh Aslam Khan,et al. A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection , 2018, Cluster Computing.
[17] Lalu Banoth,et al. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2017 .
[18] Claudia Eckert,et al. Deep Learning for Classification of Malware System Call Sequences , 2016, Australasian Conference on Artificial Intelligence.
[19] Ming Zhu,et al. End-to-end encrypted traffic classification with one-dimensional convolution neural networks , 2017, 2017 IEEE International Conference on Intelligence and Security Informatics (ISI).
[20] Lijuan Zheng,et al. Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network , 2017, 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).
[21] Vivek Kumar Sharma,et al. An Intrusion Detection System using KNN-ACO Algorithm , 2017 .
[22] Ralf C. Staudemeyer,et al. Applying long short-term memory recurrent neural networks to intrusion detection , 2015 .
[23] A. O. Jimoh. Anomaly Intrusion Detection Using an Hybrid Of Decision Tree And K-Nearest Neighbor , 2015 .
[24] N. R. Raajan,et al. AN INTELLECTUAL INTRUSION DETECTION SYSTEM MODEL FOR ATTACKS CLASSIFICATION USING RNN , 2017 .
[25] Wei Huang,et al. An intrusion detection method based on DBN in ad hoc networks , 2016 .
[26] Hey-Jin Ha,et al. The Influence of Cervical Cancer Knowledge, Preventive Behavioral Intention on Cervical Cancer Screening of Nursing Students , 2020 .
[27] Dharmaraj R. Patil,et al. Implementation of network intrusion detection system using variant of decision tree algorithm , 2015, 2015 International Conference on Nascent Technologies in the Engineering Field (ICNTE).
[28] A. Malathi,et al. A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection , 2013 .
[29] Alexander Brenning,et al. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling , 2015, Comput. Geosci..
[30] Yang Yu,et al. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders , 2017, Secur. Commun. Networks.
[31] 이상헌,et al. Deep Belief Networks , 2010, Encyclopedia of Machine Learning.
[32] Wenjuan Li,et al. Design of intelligent KNN-based alarm filter using knowledge-based alert verification in intrusion detection , 2015, Secur. Commun. Networks.
[33] B. Basaveswara Rao,et al. Fast kNN Classifiers for Network Intrusion Detection System , 2017 .
[34] Muttukrishnan Rajarajan,et al. A survey of intrusion detection techniques in Cloud , 2013, J. Netw. Comput. Appl..
[35] Ming Zhu,et al. Malware traffic classification using convolutional neural network for representation learning , 2017, 2017 International Conference on Information Networking (ICOIN).
[36] Mahdi Zamani,et al. Machine Learning Techniques for Intrusion Detection , 2013, ArXiv.
[37] Suleyman Serdar Kozat,et al. Efficient Online Learning Algorithms Based on LSTM Neural Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[38] Chirag N. Modi,et al. Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review , 2017, The Journal of Supercomputing.
[39] Dewan Md Farid,et al. Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs , 2014, The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014).
[40] Hemanta Kumar Kalita,et al. Analysis of Machine Learning Techniques Based Intrusion Detection Systems , 2016 .
[41] Sheng Chen,et al. Application of Deep Belief Networks for opcode based malware detection , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[42] Jung-Min Park,et al. An overview of anomaly detection techniques: Existing solutions and latest technological trends , 2007, Comput. Networks.
[43] Md Zahangir Alom,et al. Intrusion detection using deep belief networks , 2015, 2015 National Aerospace and Electronics Conference (NAECON).
[44] Reid G. Smith,et al. Building AI Applications: Yesterday, Today, and Tomorrow , 2017, AI Mag..
[45] Jong Hyuk Park,et al. DTB-IDS: an intrusion detection system based on decision tree using behavior analysis for preventing APT attacks , 2015, The Journal of Supercomputing.
[46] S. Saravan Kumar,et al. An Intelligent Intrusion Detection System Using Average Manhattan Distance-based Decision Tree , 2015 .
[47] Geoff Holmes,et al. Evaluation methods and decision theory for classification of streaming data with temporal dependence , 2015, Machine Learning.
[48] Ravi Raj Choudhary,et al. A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA , 2017, 2017 International Conference on Computer, Communications and Electronics (Comptelix).
[49] Nhien-An Le-Khac,et al. Collective Anomaly Detection Based on Long Short-Term Memory Recurrent Neural Networks , 2016, FDSE.
[50] Anamika Yadav,et al. Decision Tree Based Intrusion Detection System for NSL-KDD Dataset , 2017 .
[51] Claudia Eckert,et al. Empowering convolutional networks for malware classification and analysis , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[52] Frederico G. Guimarães,et al. A GPU deep learning metaheuristic based model for time series forecasting , 2017 .
[53] Howon Kim,et al. An Effective Intrusion Detection Classifier Using Long Short-Term Memory with Gradient Descent Optimization , 2017, 2017 International Conference on Platform Technology and Service (PlatCon).
[54] R.K. Cunningham,et al. Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation , 2000, Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00.
[55] Kajal Rai,et al. Decision Tree Based Algorithm for Intrusion Detection , 2016 .
[56] Steven C. H. Hoi,et al. Malicious URL Detection using Machine Learning: A Survey , 2017, ArXiv.
[57] Konstantin Berlin,et al. eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys , 2017, ArXiv.
[58] Amit Ganatra,et al. Gain Ratio and Decision Tree Classifier for Intrusion Detection , 2015 .
[59] Luiz Eduardo Soares de Oliveira,et al. Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems , 2017, IEEE Transactions on Computers.
[60] Vijay Kumar Jha,et al. Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System , 2015 .
[61] Jiankun Hu,et al. Evaluating host-based anomaly detection systems: Application of the one-class SVM algorithm to ADFA-LD , 2014, 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
[62] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[63] Shubha Puthran,et al. Intrusion Detection Using Improved Decision Tree Algorithm with Binary and Quad Split , 2016, SSCC.
[64] Dong Yu,et al. Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..
[65] Steven Aftergood,et al. Cybersecurity: The cold war online , 2017, Nature.
[66] Shilpa Lakhina,et al. Feature Reduction using Principal Component Analysis for Effective Anomaly – Based Intrusion Detection on NSL-KDD , 2010 .
[67] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[68] S. Thamarai Selvi,et al. DDoS detection and analysis in SDN-based environment using support vector machine classifier , 2014, 2014 Sixth International Conference on Advanced Computing (ICoAC).
[69] Pirooz Shamsinejad,et al. Intrusion Detection using a Novel Hybrid Method Incorporating an Improved KNN , 2017 .
[70] Sung-Bae Cho,et al. A Hybrid System of Deep Learning and Learning Classifier System for Database Intrusion Detection , 2017, HAIS.
[71] Jinoh Kim,et al. A survey of deep learning-based network anomaly detection , 2017, Cluster Computing.
[72] Howon Kim,et al. Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection , 2016, 2016 International Conference on Platform Technology and Service (PlatCon).
[73] Raquel Sánchez-Fernández,et al. Sustainability, value, and satisfaction: Model testing and cross-validation in tourist destinations , 2016 .