What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Programming language
Salvatore J. Stolfo mainly investigates Intrusion detection system, Data mining, Computer security, Anomaly detection and Artificial intelligence.
His biological study spans a wide range of topics, including Set, Real-time computing, System call and Network packet.
His work in the fields of Anomaly-based intrusion detection system and Association rule learning overlaps with other areas such as Merge and Transitive closure.
His study in the field of Information security is also linked to topics like Decoy.
Salvatore J. Stolfo combines subjects such as Window, Data security, Host and Data set with his study of Anomaly detection.
The Artificial intelligence study combines topics in areas such as Machine learning, Computer graphics and Pattern recognition.
His most cited work include:
- Data mining approaches for intrusion detection (1119 citations)
- A data mining framework for building intrusion detection models (1101 citations)
- A framework for constructing features and models for intrusion detection systems (885 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Computer security, Data mining, Intrusion detection system, Artificial intelligence and Anomaly detection.
His work in the fields of Computer security, such as Network packet, overlaps with other areas such as Decoy.
The various areas that Salvatore J. Stolfo examines in his Data mining study include Process, Set and Audit.
His Intrusion detection system research includes elements of Exploit, Real-time computing and Host.
His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His Unsupervised learning and Semi-supervised learning investigations are all subjects of Machine learning research.
He most often published in these fields:
- Computer security (27.36%)
- Data mining (22.19%)
- Intrusion detection system (19.15%)
What were the highlights of his more recent work (between 2011-2021)?
- Computer security (27.36%)
- Data mining (22.19%)
- Insider threat (3.65%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Computer security, Data mining, Insider threat, Software and Decoy.
The Authentication, Data theft and Identity theft research Salvatore J. Stolfo does as part of his general Computer security study is frequently linked to other disciplines of science, such as Energy management, therefore creating a link between diverse domains of science.
His primary area of study in Data mining is in the field of Anomaly detection.
His Insider threat research integrates issues from Intrusion detection system, Encryption and Host.
His Intrusion detection system study combines topics from a wide range of disciplines, such as Real-time computing, Generator, Data format and Data processing.
Salvatore J. Stolfo has researched Pattern recognition in several fields, including Feature and Artificial intelligence.
Between 2011 and 2021, his most popular works were:
- On the feasibility of online malware detection with performance counters (236 citations)
- Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud (179 citations)
- Methods, systems, and media for masquerade attack detection by monitoring computer user behavior (156 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Programming language
His main research concerns Computer security, Malware, Data mining, Exploit and Anomaly detection.
The study incorporates disciplines such as Cloud computing, Cloud computing security and Human–computer interaction in addition to Computer security.
Salvatore J. Stolfo combines subjects such as Signature and Computer hardware with his study of Malware.
He performs multidisciplinary studies into Data mining and Gaussian process in his work.
His work deals with themes such as Set and Feature vector, which intersect with Anomaly detection.
In general Artificial intelligence, his work in Feature is often linked to Geometric framework linking many areas of study.
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