IEEE Argentina tiene el agrado de informarle sobre las actividades que se realizarán durante los días 20, 21 y 22 de Septiembre con motivo de la visita del Dr. Jaideep Srivastava a la Argentina.
Las mismas cuentan con el apoyo del Programa de Conferencistas Distinguidos de la IEEE Computer Society y se realizan conjuntamente con la Universidad CAECE y la Escuela de Graduados en Ingeniería Electrónica y Telecomunicaciones de la Facultad de Ingeniería de la UBA.
Esperamos que sean de su interés y contar con su grata presencia en ellas.

Conferencias y Tutorial del Dr. Jaideep Srivastava

Conferencia: Web Mining
Lunes 20 de septiembre, 18:30
Universidad CAECE - Sede Abasto - Salón Microcine
Tte. Gral. Perón 2933 - Buenos Aires
Tel. (011) 5217-7888

Esta conferencia se dictará en idioma Inglés y no es arancelada.
Para mejor organización solicitamos inscribirse anticipadamente en

Conferencia: Data Mining for Computer Security
Miercoles 22 de septiembre, 18:30
Facultad de Ingenieria Universidad de Buenos Aires
Escuela de Graduados en Ingeniería Electrónica y Telecomunicaciones
Paseo Colón 850, piso 3 - Buenos Aires
Tel:(011) 4331-5077

Esta conferencia se dictará en idioma Inglés y no es arancelada.
Para mejor organización solicitamos inscribirse anticipadamente en

Tutorial: Web Mining Accomplishments and Future Directions
Martes 21 de septiembre, 16:00
Más información...

Conferencista: Dr. Jaideep Srivastava
Dr. Jaideep Srivastava is a professor on the faculty of the University of Minnesota. Between 1999 and 2001 he took a two-year leave, during which he spent time at and at Yodlee Inc. This wide-ranging industry experience has provided him with a unique perspective on the application of various computer science technologies in various kinds of Web-based services. As a researcher, educator, consultant, and invited speaker in the areas of data mining, databases, artificial intelligence, and multimedia for over 15 years, Dr. Srivastava continues his active collaboration with the technology industry, both for research and technology transfer. Dr. Srivastava has supervised 20 Ph.D. dissertations and 39 MS theses, and has authored/co-authored over 175 papers in journals and conferences. He has chaired/co-chaired a number of conferences, and is on the editorial board of many journals. An often-invited participant in technical and technology strategy forums, Dr. Srivastava has presented at a multitude of industry, academic and government meetings. He has been involved in the organization of a number of conferences, and serves on the editorial board of various journals. The US federal government has solicited his opinion on computer science research as an expert witness. He also served in an advisory role to the government of India on various software technologies. Dr. Srivastava received his B.Tech. in Computer Science from the Indian Institute of Technology - Kanpur , and M.S. and Ph.D. in Computer Science from the University of California - Berkeley . He has been elected an IEEE Fellow for his contributions to Computer Science research.

Conferencia: Web Mining
From its very beginning, the potential of extracting valuable knowledge from the Web has been quite evident. Web mining - i.e. the application of data mining techniques to extract knowledge from Web content, structure, and usage - is the collection of technologies to fulfill this potential. Interest in Web mining has grown rapidly in its short existence, both in the research and practitioner communities. A number of new concepts, e.g. PageRank, hubs & authorities, web communities, web interestingness measures, etc., and techniques to compute them have been developed. In addition, a wide variety of commercial enterprises regularly use Web mining in their daily operations, e.g. Amazon, Yahoo, Google, etc. This talk provides an overview of the accomplishments of the field - both in terms of technologies and applications - and outlines key future research directions.

Conferencia: Data Mining for Computer Security
Today computers control power, oil and gas delivery, communication systems, transportation networks, banking and financial services, and various other infrastructure services critical to the functioning of our society. However, as the cost of the information processing and Internet accessibility falls, more and more organizations are becoming vulnerable to a wide variety of cyber threats. According to a recent survey by CERT/CC (Computer Emergency Response Team/Coordination Center), the rate of cyber attacks has been more than doubling every year in recent times. It has become increasingly important to make our information systems, especially those used for critical functions in the military and commercial sectors, resistant to and tolerant of such attacks.
Intrusion detection, as a special form of cyber threat analysis, includes identifying a set of malicious actions that compromise the integrity, confidentiality, and availability of information resources. Traditional methods for intrusion detection are based on extensive knowledge of signatures of known attacks. The signature database has to be manually revised for each new type of intrusion that is discovered. A significant limitation of signature-based methods is that they cannot detect emerging cyber threats, since by their very nature these threats are launched using previously unknown attacks. These limitations have led to an increasing interest in intrusion detection techniques based upon data mining.
The tremendous increase of novel cyber attacks has made data mining based intrusion detection techniques extremely useful in their detection. These techniques generally fall into one of two categories; misuse detection and anomaly detection. However, both approaches attempt to detect cyber attacks that occur very infrequently, but their consequences may be quite dramatic and often in a negative sense. In misuse detection, each instance in a data set is labeled as ‘normal' or ‘attack/intrusion' and a learning algorithm is trained over the labeled data. However, standard data mining techniques are not applicable due to issues including (i) dealing with skewed class distribution (attacks/intrusions correspond to a class of interest that is much smaller, i.e. rarer, than the class representing normal behavior) and (ii) learning from data streams (attacks/intrusions very often represent sequence of events). Anomaly detection, on the other hand, builds models of normal behavior, and automatically detects new types of intrusions as deviations from normal usage.
This tutorial-style talk will provide an up-to-date introduction to the increasingly important field of the data mining in intrusion detection, as well as an overview of research directions in this field. It will cover the most representative research projects and directions in intrusion detection based on data mining. There is also an ongoing project at our center related to the data mining applications in network intrusion detection, and plan to cover some of these activities in the talk.

Acknowledgement: This is joint work with Dr. Aleksander Lazarevic and Prof. Vipin Kumar, and other members of the MINDS (Minnesota Intrusion Detection System) group. Details of MINDS are available at

Para mantenerse informado sobre novedades de IEEE Argentina
recuerde visitar periodicamente la seccion 'Noticias y Actividades'
en nuestra pagina web

IEEE Argentina - Oficina Administrativa:
Av. Córdoba 744 Piso 2 Of. D
(C1054AAT) Ciudad Autónoma de Buenos Aires - República Argentina
Tel: +(54 11) 4325 8839
Fax: +(54 11) 4325 9604

Esta Newsletter se envía sólo a aquellas personas que han aceptado recibirla, según se describe en Condiciones.
Si no desea recibir futuros envíos por favor haga click aquí.
La suscripción a esta Newsletter significa la aceptación de las Condiciones.