World's Best Scientists 2026 revealed!
Salvatore J. Stolfo

Salvatore J. Stolfo

D-Index & Metrics

Computer Science

D-Index
98
Citations
43121
World Ranking
402
National Ranking
222

Research.com Recognitions

  • 2019 - ACM Fellow For contributions to machine-learning-based cybersecurity and parallel hardware for database inference systems
  • 2018 - IEEE Fellow For contributions to machine learning-based computer security

Overview

Salvatore J. Stolfo is affiliated with Columbia University in the United States. Their research spans multiple fields, with main contributions in Physics and Astronomy and Computer Science. Within these areas, Stolfo has focused on subfields such as Statistical and Nonlinear Physics and Information Systems.

The scientist's work encompasses topics including Complex Network Analysis Techniques, Opinion Dynamics and Social Influence, and Web Visibility and Informetrics. These areas reflect an interdisciplinary approach connecting physics, computational methods, and information systems.

Salvatore J. Stolfo has contributed to scholarly publications in venues such as:

  • Complexity

Among Stolfo's recent papers is "Discovering Organizational Hierarchy through a Corporate Ranking Algorithm: The Enron Case," published in 2022 in Complexity. This publication has received citations highlighting its engagement with organizational and computational topics.

Frequent collaborators in Stolfo's research include:

  • Germán G. Creamer
  • Mateo Creamer
  • Shlomo Hershkop
  • Ryan Rowe

Their work has been recognized with distinctions such as:

  • ACM Fellow, awarded in 2019, for contributions to machine-learning-based cybersecurity and parallel hardware for database inference systems
  • IEEE Fellow, awarded in 2018, for contributions to machine learning-based computer security

Best Publications

  • Data mining approaches for intrusion detection

    Wenke Lee;Salvatore J. Stolfo

  • A data mining framework for building intrusion detection models

    Wenke Lee;S.J. Stolfo;K.W. Mok

  • Data mining methods for detection of new malicious executables

    M.G. Schultz;E. Eskin;F. Zadok;S.J. Stolfo

  • A Geometric Framework for Unsupervised Anomaly Detection

    Eleazar Eskin;Andrew Arnold;Michael J. Prerau;Leonid Portnoy

  • Distributed data mining in credit card fraud detection

    P.K. Chan;W. Fan;A.L. Prodromidis;S.J. Stolfo

  • Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem

    Mauricio A. Hernández;Salvatore J. Stolfo

  • The merge/purge problem for large databases

    Mauricio A. Hernández;Salvatore J. Stolfo

  • Anomalous payload-based network intrusion detection

    Ke Wang;Salvatore J. Stolfo

  • A framework for constructing features and models for intrusion detection systems

    Wenke Lee;Salvatore J. Stolfo

  • AdaCost: Misclassification Cost-Sensitive Boosting

    Wei Fan;Salvatore J. Stolfo;Junxin Zhang;Philip K. Chan

  • Cost-based modeling for fraud and intrusion detection: results from the JAM project

    S.J. Stolfo;Wei Fan;Wenke Lee;A. Prodromidis

  • Method and system for using intelligent agents for financial transactions, services, accounting, and advice

    Daniel Schutzer;William Hull Forster;Huanrui Hu;Wenke Lee

  • Adaptive Intrusion Detection: A Data Mining Approach

    Wenke Lee;Salvatore J. Stolfo;Kui W. Mok

  • Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection

    Philip K. Chan;Salvatore J. Stolfo

  • Learning Patterns from Unix Process Execution Traces for Intrusion Detection

    Wenke Lee;Saivatore J. Stolfo;Philip K. Chan

  • JAM: java agents for meta-learning over distributed databases

    Salvatore Stolfo;Andreas L. Prodromidis;Shelley Tselepis;Wenke Lee

  • Toward cost-sensitive modeling for intrusion detection and response

    Wenke Lee;Wei Fan;Matthew Miller;Salvatore J. Stolfo

  • Electronic purchase of goods over a communications network including physical delivery while securing private and personal information of the purchasing party

    Salvotore J. Stolfo;Yechiam Yemini;Leonard P. Shaykin

  • Anagram : A content anomaly detector resistant to mimicry attack

    Ke Wang;Janak J. Parekh;Salvatore J. Stolfo

  • Mining audit data to build intrusion detection models

    Wenke Lee;Salvatore J. Stolfo;Kui W. Mok

Frequent Co-Authors

Angelos D. Keromytis
Angelos D. Keromytis Georgia Institute of Technology
Wenke Lee
Wenke Lee Georgia Institute of Technology
Angelos Stavrou
Angelos Stavrou Virginia Tech
Philip K. Chan
Philip K. Chan Florida Institute of Technology
Eleazar Eskin
Eleazar Eskin University of California, Los Angeles
Wei Fan
Wei Fan Tencent (China)
Daniel P. Miranker
Daniel P. Miranker The University of Texas at Austin
David E. Shaw
David E. Shaw D. E. Shaw Research
Gail E. Kaiser
Gail E. Kaiser Columbia University
Gerald Q. Maguire Jr.
Gerald Q. Maguire Jr. Royal Institute of Technology

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