Ensemble classifier for misuse detection using N-gram feature vectors through operating system call traces

[1]  Wenxin Hu,et al.  An Efficient Algorithm for Multi-class Support Vector Machines , 2008, 2008 International Conference on Advanced Computer Theory and Engineering.

[2]  A Kahate Sandip Review of A Semantic Approach to Host-based Intrusion Detection Systems Using Contiguous and Dis-contiguous System Call Patterns , 2015 .

[3]  Haruna Chiroma,et al.  A Review of the Advances in Cyber Security Benchmark Datasets for Evaluating Data-Driven Based Intrusion Detection Systems , 2015, SCSE.

[4]  Emin Anarim,et al.  An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks , 2005, Expert Syst. Appl..

[5]  Xinghuo Yu,et al.  Evaluating Host-Based Anomaly Detection Systems: Application of the Frequency-Based Algorithms to ADFA-LD , 2014, NSS.

[6]  Ali A. Ghorbani,et al.  Towards a Reliable Intrusion Detection Benchmark Dataset , 2017 .

[7]  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).

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

[9]  Abdelwahab Hamou-Lhadj,et al.  An Anomaly Detection System Based on Ensemble of Detectors with Effective Pruning Techniques , 2015, 2015 IEEE International Conference on Software Quality, Reliability and Security.

[10]  Muttukrishnan Rajarajan,et al.  A survey of intrusion detection techniques in Cloud , 2013, J. Netw. Comput. Appl..

[11]  B. Kavya,et al.  A Survey on SVM Classifiers for Intrusion Detection , 2014 .

[12]  Rashmi Pandey,et al.  Extensive Survey on MIMO Technology using V-BLAST Detection Technique , 2014 .

[13]  Hedieh Sajedi,et al.  Detection of malicious web pages by evolutionary ensemble learning , 2016, Int. J. Hybrid Intell. Syst..

[14]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[15]  Leandros A. Maglaras,et al.  Data Mining and Intrusion Detection Systems , 2016 .

[16]  Gürsel Serpen,et al.  Hybrid random subsample classifier ensemble for high dimensional data sets , 2012, Int. J. Hybrid Intell. Syst..

[17]  Rebecca Gurley Bace,et al.  Intrusion Detection , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[18]  Jingtao Yao,et al.  An Enhanced Support Vector Machine Model for Intrusion Detection , 2006, RSKT.

[19]  David G. Stork,et al.  Pattern Classification , 1973 .

[20]  Andrew H. Sung,et al.  Intrusion detection using neural networks and support vector machines , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

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

[22]  Dhiren Patel,et al.  Evaluation of Modified Vector Space Representation Using ADFA-LD and ADFA-WD Datasets , 2015 .

[23]  David Barber,et al.  Bayesian reasoning and machine learning , 2012 .

[24]  Jiankun Hu,et al.  Generation of a new IDS test dataset: Time to retire the KDD collection , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[25]  I. Jolliffe Principal Component Analysis , 2002 .

[26]  Neeraj Bhargava,et al.  Decision Tree Analysis on J48 Algorithm for Data Mining , 2013 .

[27]  Mohammad Zulkernine,et al.  Random-Forests-Based Network Intrusion Detection Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[28]  I. J. Myung,et al.  Tutorial on maximum likelihood estimation , 2003 .

[29]  Peter Mell,et al.  Intrusion Detection Systems , 2001 .

[30]  Dae-Ki Kang,et al.  Learning classifiers for misuse and anomaly detection using a bag of system calls representation , 2005, Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop.

[31]  Helmut Berger,et al.  Exploiting partial decision trees for feature subset selection in e-mail categorization , 2006, SAC.

[32]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[33]  S. Shankar Sastry,et al.  Optimal thresholds for intrusion detection systems , 2016, HotSoS.

[34]  Charu C. Aggarwal,et al.  Mining Text Data , 2012 .

[35]  J. Kalbfleisch,et al.  The Analysis of Panel Data under a Markov Assumption , 1985 .

[36]  Ajith Abraham,et al.  Class imbalance problem using a hybrid ensemble approach , 2015, Int. J. Hybrid Intell. Syst..

[37]  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.

[38]  V.V. Phoha,et al.  Dimension reduction using feature extraction methods for real-time misuse detection systems , 2004, Proceedings from the Fifth Annual IEEE SMC Information Assurance Workshop, 2004..

[39]  Barak A. Pearlmutter,et al.  Detecting intrusions using system calls: alternative data models , 1999, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344).

[40]  Christin Schäfer,et al.  Learning Intrusion Detection: Supervised or Unsupervised? , 2005, ICIAP.

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