General Information
NameDr. Robert Moskovitch
DepartmentDepartment of Software and Information Systems Engineering
Emailrobertmo@bgu.ac.il
Personal Web SitePersonal Web Site
Academic RankSenior lecturer


Journal Articles

 
[1] Moskovitch R., Fast time intervals mining using the transitivity of temporal relations, Knowl. Inf. Syst. (UK), 42, 1, 21 - 48, (2015).
[2] Moskovitch R., Classification-driven temporal discretization of multivariate time series, Data Min. Knowl. Discov. (USA), 29, 4, 871 - 913, (2015).
[3] Moskovitch R., An active learning framework for efficient condition severity classification, 15th Conference on Artificial Intelligence in Medicine, AIME 2015 Proceedings: LNCS 9105, 9105, 13 - 24, (2015).
[4] Moskovitch R., Novel active learning methods for enhanced PC malware detection in windows OS, Expert Syst. Appl. (UK), 41, 13, 5843 - 57, (2014).
[5] Moskovitch R., ALPD: active learning framework for enhancing the detection of malicious PDF files, 2014 IEEE Joint Intelligence and Security Informatics Conference (JISIC), 91 - 8, (2014).
[6] Moskovitch R., User identity verification via mouse dynamics, Inf. Sci. (USA), 201, 19-36, (2012).
[7] Moskovitch R., Detecting unknown computer worm activity via support vector machines and active learning, Pattern Anal. Appl. (UK), 15, 4, 459--475, (2012).
[8] Moskovitch R., Monitoring, analysis, and filtering system for purifying network traffic of known and unknown malicious content, Secur. Commun. Netw. (USA), 4, 8, 947--965, (2011).
[9] Stopel Dima, Moskovitch R., Using artificial neural networks to detect unknown computer worms, Neural Computing and Applications, 18, 7, 663-674, (2009).
[10] Moskovitch R., Vaidurya: A multiple-ontology, concept-based, context-sensitive, clinical-guideline search engine, The Journal of BioMedical Informatics, 42, 1, 11-21, (2009).
[11] Moskovitch R., Detection of Malicious Code by Applying Machine Learning Classifiers on Static Features – a State-of-the-Art Survey, Information Security Technical Report, 14, 1, 16-29, (2009).
[12] Moskovitch R., Stopel Dima, Feher Clint, Nissim Nir, Japkowicz N, Unknown Malcode Detection and the Imbalance Problem, Journal in Computer Virology, 5, 4, 295-308, (2009).
[13] Moskovitch R., Detection of unknown computer worms based on behavioral classification of the host, Computational Statistics and Data Analysis, 52, 9, 4544 - 4566, (2008).
[14] Moskovitch R., Optimization of Fire blight scouting with a decision support system based on infection risk, Comput. Electron. Agric. (Netherlands), 62, 2, 118 - 27, (2008).
[15] Moskovitch R., Application of artificial neural networks techniques to computer worm detection, 2006 International Joint Conference on Neural Networks, 2362 - 9, (2007).
[16] Moskovitch R., Multiple hierarchical classification of free-text clinical guidelines, Artificial Intelligence in Medicine, 37, 3, 177-190, (2006).
[17] Moskovitch R., Identifying risk conditions for fireBlight infection using artificial neural networks based on rare events, Computers in Agriculture and Natural Resources - Proceedings of the 4th World Congress, 315 - 320, (2006).
[18] Moskovitch R., Helping physicians to organize guidelines within conceptual hierarchies, Artificial Intelligence in Medicine. 10th Conference on Artificial Intelligence in Medicine, AIME 2005. Proceedings (Lecture Notes in Artificial Intelligence Vol.3581), 3581 LNAI, 141 - 5, (2005).

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Conference Proceedings

 
[1] Moskovitch R., Clustering di-graphs for continuously verifying users according to their typing patterns,2010 IEEE 26th Convention of Electrical & Electronics Engineers in Israel (IEEEI 2010), IEEE, 000445--000449, (2010).
[2] Moskovitch R., Continuous verification using keystroke dynamics,Proceedings 2010 International Conference on Computational Intelligence and Security (CIS 2010), IEEE Computer Society, 411--415, (2010).
[3] Moskovitch R., Identity Theft, Computers and Behavioral Biometrics, IEEE International Conference on Intelligence and Security Informatics (IEEE ISI-2009),2009 IEEE International Conference on Intelligence and Security Informatics (ISI), IEEE, 155 - 60, (2009).
[4] Moskovitch R., Peek Niels, Classificaton of ICU patients via temporal abstractions and temporal pattern mining, IDAMAP-2009, (2009).
[5] Moskovitch R., Unknown Malicious Code Detection – Practical Issues, ECIW 2008. The 7th European Conference on Information Warfare and Security, University of Plymouth, UK, June 30– July 1st, 2008, (2008).
[6] Moskovitch R., Stopel D., Feher C., Nissim N., Unknown Malcode Detection via Text Categorization and the Imbalance Problem, ”, IEEE International Conference on Intelligence and Security Informatics (IEEE ISI-2008), Taipei, Taiwan, June 17-20, 2008, IEEE, (2008).
[7] Moskovitch R., Unknown Malcode Detection – A Chronological Evaluation, IEEE International Conference on Intelligence and Security Informatics (IEEE ISI-2008), Taipei, Taiwan, June 17-20, 2008, (2008).
[8] Moskovitch R., Feher Clint, Tzahar Nir, Berger E, Gitelman M, Dolev Shlomi, Unknown Malcode Detection Using OPCODE Representation, European Conference on Intelligence and Security Informatics 2008 (EuroISI08) Esbjerg, Denmark, December 3-5, 2008, (2008).
[9] Moskovitch R., Experiments with hierarchical concept-based search. MEDINFO-2007, IOS Press; 1999, (2007).
[10] Moskovitch R., Nissim Nir, Stopel Dima, Feher Clint, Englert Roman, Improving the Detection of Unknown Computer Worms Activity using Active Learning, 30th Annual German Conference on Artificial Intelligence (KI-2007), Springer, (2007).
[11] Moskovitch R., Gus Ido, Pluderman Shay, Stopel Dima, Parmet Y., Detection of Unknown Computer Worms Activity Based on Computer Behaviour Using Data Mining, ", IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007, Honolulu, Hawaii, USA, 1-5 April 2007, (2007).
[12] Moskovitch R., Nissim N., Malicious Code Detection and Acquisition Using Active Learning, ", IEEE International Conference on Intelligence and Security Informatics (IEEE ISI-2007), Rutgers University, New Jersey, USA, May 23-24, 2007, IEEE, (2007).
[13] Moskovitch R., Gus Ido, Pluderman Shay, Stopel Dima, Feher Clint, Host Based Intrusion Detection using Machine Learning, IEEE Information and Security Informatics Rutgers University, New Jersey, US, May 2007, (2007).
[14] Moskovitch R., Applying machine learning techniques for detection of malicious code in network traffic,Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 44 - 50, (2007).
[15] Moskovitch R., Application of Artificial Neural Networks Techniques to Computer Worm Detection, IEEE World Congress on Computational Intelligence (IEEE WCCI 2006), Vancouver, BC, Canada, July 16-21, 2006,IEEE International Conference on Neural Networks - Conference Proceedings, 2362 - 2369, (2006).
[16] Moskovitch R., Vaidurya – A Concept-Based, Context-Sensitive Search Engine For Clinical Guidelines, Medinfo 2004, 5, (2004).
[17] Moskovitch R., A Multi Ontology Customized Search Query Interface for Searching Clinical Guidelines, CGP-2004, 12, (2004).
[18] Moskovitch R., DEGEL: A Hybrid, Multiple-Ontology Framework forSpecification and Retrieval of Clinical Guidelines, AIME 03, 10, (2003).
[19] Moskovitch R., Nissim Nir, Improving the Detection of Unknown Computer Worms Activity using Active Learning, The 11th International Conference on Information Fusion, ().
[20] Moskovitch R., Nissim N., Malicious Code Detection Using Active Learning, 2nd ACM SIGKDD International Workshop on Privacy, Security, and Trust in KDD, PinKDD08, ().

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Technical Report

 
[1] Shknevsky Alexander, Moskovitch R., (24410/2017), The Semantic Adjacency Criterion in Time IntervalsMining, bgu (2017).
[2] Moskovitch R., (21843/2008), KarmaLego – An Algorithm for Fast Time Intervals Mining, (2008).
[3] Moskovitch R., (297/2004), A Framework for a Distributed, Hybrid, Multiple-Ontology Clinical-Guideline Library and Automated Guideline-Support Tools, Ben Gurion University of the Negev (2004).

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