@INPROCEEDINGS{15.esann.oneclass,
   author =  "Patric Nader and Paul Honeine and Pierre Beauseroy",
   title =  "Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance",
   booktitle =  "Proc. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)",
   address =  "Bruges, Belgium",
   year  =  "2015",
   month =  "22 - 24~" # apr,
   pages    =  {567 - 572},
   isbn = "978-287587014-8",
   acronym =  "ESANN",
   url_paper  =  "http://honeine.fr/paul/publi/15.esann.oneclass.pdf",
   keywords  =  "machine learning, one-class, cybersecurity, computer crime, critical infrastructures, firewalls, learning (artificial intelligence), pattern classification, principal component analysis, radial basis function networks, SCADA systems, support vector machines, lp-norms, SCADA systems, information and communication technologies, supervisory control and data acquisition systems, cyberattacks heterogeneity, critical infrastructures, SCADA networks, systems vulnerabilities, intrusion detection systems, IDS, cyberattacks modeling, malicious intrusions detection, firewalls, machine learning, one-class classification algorithms, support vector data description, SVDD, kernel principle component analysis, radial basis function kernels, RBF kernels, bandwidth parameter, Kernel, Machine learning, SCADA systems, Intrusion detection, Optimization, Intrusion detection, kernel methods, ${\mbi {l_p}}$ -norms, one-class classification, supervisory control and data acquisition (SCADA) systems, Mahalanobis distance",
   abstract = "Machine learning techniques have been very popular in the past decade for their ability to detect hidden patterns in large volumes of data. Researchers have been developing online intrusion detection algorithms based on these techniques. In this paper, we propose an online one-class classification approach based on the Mahalanobis distance which takes into account the covariance in each feature direction and the different scaling of the coordinate axes. We define the one-class problem by two concentric hyperspheres enclosing the support vectors of the description. We update the classifier at each time step. The tests are conducted on real data.",
}

{"_id":"nFhYjSRrD2ejAJHh4","bibbaseid":"nader-honeine-beauseroy-onlineoneclassclassificationforintrusiondetectionbasedonthemahalanobisdistance-2015","author_short":["Nader, P.","Honeine, P.","Beauseroy, P."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Patric"],"propositions":[],"lastnames":["Nader"],"suffixes":[]},{"firstnames":["Paul"],"propositions":[],"lastnames":["Honeine"],"suffixes":[]},{"firstnames":["Pierre"],"propositions":[],"lastnames":["Beauseroy"],"suffixes":[]}],"title":"Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance","booktitle":"Proc. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)","address":"Bruges, Belgium","year":"2015","month":"22 - 24 April","pages":"567 - 572","isbn":"978-287587014-8","acronym":"ESANN","url_paper":"http://honeine.fr/paul/publi/15.esann.oneclass.pdf","keywords":"machine learning, one-class, cybersecurity, computer crime, critical infrastructures, firewalls, learning (artificial intelligence), pattern classification, principal component analysis, radial basis function networks, SCADA systems, support vector machines, lp-norms, SCADA systems, information and communication technologies, supervisory control and data acquisition systems, cyberattacks heterogeneity, critical infrastructures, SCADA networks, systems vulnerabilities, intrusion detection systems, IDS, cyberattacks modeling, malicious intrusions detection, firewalls, machine learning, one-class classification algorithms, support vector data description, SVDD, kernel principle component analysis, radial basis function kernels, RBF kernels, bandwidth parameter, Kernel, Machine learning, SCADA systems, Intrusion detection, Optimization, Intrusion detection, kernel methods, ${\\mbi {l_p}}$ -norms, one-class classification, supervisory control and data acquisition (SCADA) systems, Mahalanobis distance","abstract":"Machine learning techniques have been very popular in the past decade for their ability to detect hidden patterns in large volumes of data. Researchers have been developing online intrusion detection algorithms based on these techniques. In this paper, we propose an online one-class classification approach based on the Mahalanobis distance which takes into account the covariance in each feature direction and the different scaling of the coordinate axes. We define the one-class problem by two concentric hyperspheres enclosing the support vectors of the description. We update the classifier at each time step. The tests are conducted on real data.","bibtex":"@INPROCEEDINGS{15.esann.oneclass,\n author = \"Patric Nader and Paul Honeine and Pierre Beauseroy\",\n title = \"Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance\",\n booktitle = \"Proc. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)\",\n address = \"Bruges, Belgium\",\n year = \"2015\",\n month = \"22 - 24~\" # apr,\n pages = {567 - 572},\n isbn = \"978-287587014-8\",\n acronym = \"ESANN\",\n url_paper = \"http://honeine.fr/paul/publi/15.esann.oneclass.pdf\",\n keywords = \"machine learning, one-class, cybersecurity, computer crime, critical infrastructures, firewalls, learning (artificial intelligence), pattern classification, principal component analysis, radial basis function networks, SCADA systems, support vector machines, lp-norms, SCADA systems, information and communication technologies, supervisory control and data acquisition systems, cyberattacks heterogeneity, critical infrastructures, SCADA networks, systems vulnerabilities, intrusion detection systems, IDS, cyberattacks modeling, malicious intrusions detection, firewalls, machine learning, one-class classification algorithms, support vector data description, SVDD, kernel principle component analysis, radial basis function kernels, RBF kernels, bandwidth parameter, Kernel, Machine learning, SCADA systems, Intrusion detection, Optimization, Intrusion detection, kernel methods, ${\\mbi {l_p}}$ -norms, one-class classification, supervisory control and data acquisition (SCADA) systems, Mahalanobis distance\",\n abstract = \"Machine learning techniques have been very popular in the past decade for their ability to detect hidden patterns in large volumes of data. Researchers have been developing online intrusion detection algorithms based on these techniques. In this paper, we propose an online one-class classification approach based on the Mahalanobis distance which takes into account the covariance in each feature direction and the different scaling of the coordinate axes. We define the one-class problem by two concentric hyperspheres enclosing the support vectors of the description. We update the classifier at each time step. The tests are conducted on real data.\",\n}\n","author_short":["Nader, P.","Honeine, P.","Beauseroy, P."],"key":"15.esann.oneclass","id":"15.esann.oneclass","bibbaseid":"nader-honeine-beauseroy-onlineoneclassclassificationforintrusiondetectionbasedonthemahalanobisdistance-2015","role":"author","urls":{" paper":"http://honeine.fr/paul/publi/15.esann.oneclass.pdf"},"keyword":["machine learning","one-class","cybersecurity","computer crime","critical infrastructures","firewalls","learning (artificial intelligence)","pattern classification","principal component analysis","radial basis function networks","SCADA systems","support vector machines","lp-norms","SCADA systems","information and communication technologies","supervisory control and data acquisition systems","cyberattacks heterogeneity","critical infrastructures","SCADA networks","systems vulnerabilities","intrusion detection systems","IDS","cyberattacks modeling","malicious intrusions detection","firewalls","machine learning","one-class classification algorithms","support vector data description","SVDD","kernel principle component analysis","radial basis function kernels","RBF kernels","bandwidth parameter","Kernel","Machine learning","SCADA systems","Intrusion detection","Optimization","Intrusion detection","kernel methods","${\\mbi {l_p}}$ -norms","one-class classification","supervisory control and data acquisition (SCADA) systems","Mahalanobis distance"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"inproceedings","biburl":"http://honeine.fr/paul/biblio_ph.bib","dataSources":["DsERGQxgYm5hGq3CY"],"keywords":["machine learning","one-class","cybersecurity","computer crime","critical infrastructures","firewalls","learning (artificial intelligence)","pattern classification","principal component analysis","radial basis function networks","scada systems","support vector machines","lp-norms","scada systems","information and communication technologies","supervisory control and data acquisition systems","cyberattacks heterogeneity","critical infrastructures","scada networks","systems vulnerabilities","intrusion detection systems","ids","cyberattacks modeling","malicious intrusions detection","firewalls","machine learning","one-class classification algorithms","support vector data description","svdd","kernel principle component analysis","radial basis function kernels","rbf kernels","bandwidth parameter","kernel","machine learning","scada systems","intrusion detection","optimization","intrusion detection","kernel methods","${\\mbi {l_p}}$ -norms","one-class classification","supervisory control and data acquisition (scada) systems","mahalanobis distance"],"search_terms":["online","one","class","classification","intrusion","detection","based","mahalanobis","distance","nader","honeine","beauseroy"],"title":"Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance","year":2015}