Investigation of Dual-Flow Deep Learning Models LSTM-FCN and GRU-FCN Efficiency against Single-Flow CNN Models for the Host-Based Intrusion and Malware Detection Task on Univariate Times Series Data

[1]  Howon Kim,et al.  Network Intrusion Detection Based on Novel Feature Selection Model and Various Recurrent Neural Networks , 2019, Applied Sciences.

[2]  Nikolaj Goranin,et al.  Towards a Robust Method of Dataset Generation of Malicious Activity for Anomaly-Based HIDS Training and Presentation of AWSCTD Dataset , 2018, Balt. J. Mod. Comput..

[3]  Sabri Boughorbel,et al.  Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric , 2017, PloS one.

[4]  Ingoo Han,et al.  The neural network models for IDS based on the asymmetric costs of false negative errors and false positive errors , 2003, Expert Syst. Appl..

[5]  Ralf C. Staudemeyer,et al.  Applying long short-term memory recurrent neural networks to intrusion detection , 2015 .

[6]  Shi-Jinn Horng,et al.  A novel intrusion detection system based on hierarchical clustering and support vector machines , 2011, Expert Syst. Appl..

[7]  Pavol Zavarsky,et al.  Experimental Analysis of Ransomware on Windows and Android Platforms: Evolution and Characterization , 2016, FNC/MobiSPC.

[8]  Humphrey Waita Njogu,et al.  An efficient approach to reduce alerts generated by multiple IDS products , 2014, Int. J. Netw. Manag..

[9]  Jiankun Hu,et al.  Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling , 2017, J. Netw. Comput. Appl..

[10]  Pavlo M. Radiuk,et al.  Impact of Training Set Batch Size on the Performance of Convolutional Neural Networks for Diverse Datasets , 2017, Information Technology and Management Science.

[11]  Jinoh Kim,et al.  A survey of deep learning-based network anomaly detection , 2017, Cluster Computing.

[12]  Chen Yuanyuan,et al.  Quantitative analysis modeling of infrared spectroscopy based on ensemble convolutional neural networks , 2018, Chemometrics and Intelligent Laboratory Systems.

[13]  Danyang Li,et al.  Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition , 2017, Cognitive Computation.

[14]  Vijay Kumar Jha,et al.  Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets , 2013 .

[15]  Hyeonseung Im,et al.  A Comparative Study of Bitcoin Price Prediction Using Deep Learning , 2019, Mathematics.

[16]  Ana Lucila Sandoval Orozco,et al.  Malware Detection System by Payload Analysis of Network Traffic , 2015 .

[17]  Jiankun Hu,et al.  Windows Based Data Sets for Evaluation of Robustness of Host Based Intrusion Detection Systems (IDS) to Zero-Day and Stealth Attacks , 2016, Future Internet.

[18]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[19]  Bhavani M. Thuraisingham,et al.  A new intrusion detection system using support vector machines and hierarchical clustering , 2007, The VLDB Journal.

[20]  Mamun Bin Ibne Reaz,et al.  A novel SVM-kNN-PSO ensemble method for intrusion detection system , 2016, Appl. Soft Comput..

[21]  Jiankun Hu,et al.  A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns , 2014, IEEE Transactions on Computers.

[22]  Miriam A. M. Capretz,et al.  Machine Learning With Big Data: Challenges and Approaches , 2017, IEEE Access.

[23]  Nikolaj Goranin,et al.  Evaluation of Deep Learning Methods Efficiency for Malicious and Benign System Calls Classification on the AWSCTD , 2019, Secur. Commun. Networks.

[24]  Ronald E. Rice,et al.  The use of computer-monitored data in information science and communication research , 1983, J. Am. Soc. Inf. Sci..

[25]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[26]  Stephanie Forrest,et al.  Intrusion Detection Using Sequences of System Calls , 1998, J. Comput. Secur..

[27]  V. Rao Vemuri,et al.  Use of K-Nearest Neighbor classifier for intrusion detection , 2002, Comput. Secur..

[28]  Oded Gonda.  Understanding the threat to SCADA networks , 2014, Netw. Secur..

[29]  Nicole Radziwill.  Countdown to Zero Day: Stuxnet and the Launch of the World's First Digital Weapon , 2018 .

[30]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[31]  Houshang Darabi,et al.  LSTM Fully Convolutional Networks for Time Series Classification , 2017, IEEE Access.

[32]  LiaoYihua.  Use of K-Nearest Neighbor classifier for intrusion detection11An earlier version of this paper is to appear in the Proceedings of the 11th USENIX Security Symposium, San Francisco, CA, August 2002 , 2002 .

[33]  Jiankun Hu,et al.  A novel statistical technique for intrusion detection systems , 2018, Future Gener. Comput. Syst..

[34]  Magdy A. Bayoumi,et al.  Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification , 2018, International Journal of Advanced Computer Science and Applications.

[35]  Yu Liu,et al.  CNN-RNN: a large-scale hierarchical image classification framework , 2018, Multimedia Tools and Applications.

[36]  Yu-Lin He,et al.  Fuzziness based semi-supervised learning approach for intrusion detection system , 2017, Inf. Sci..

[37]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[38]  K. McLaughlin,et al.  Multiattribute SCADA-Specific Intrusion Detection System for Power Networks , 2014, IEEE Transactions on Power Delivery.