State of the Practice of Intrusion Detection Technologies

By Julia H. Allen, A. Christie, William L. Fithen, John McHugh, Jed Pickel. 2000

493 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/826a8ca52ff94c12e3e895ea55023d26a52572bb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wenke Lee
2. Vinod Yegneswaran
3. Klaus Julisch
4. Paul R. Barford
5. Salvatore J. Stolfo
6. Somesh Jha
7. M. B. Yassein
8. Monther Aldwairi

In Computer Networks and Communications

1. Wenke Lee
2. Klaus Julisch
3. Vinod Yegneswaran
4. Paul R. Barford
5. Salvatore J. Stolfo
6. Somesh Jha
7. M. B. Yassein
8. Monther Aldwairi

In Signal Processing

1. Wenke Lee
2. Somesh Jha
3. Marc Dacier
4. Vinod Yegneswaran
5. Salvatore J. Stolfo
6. Paul R. Barford
7. Klaus Julisch
8. Pavel Laskov

Citations in the corpus

• FUZZY DATA MINING AND GENETIC ALGORITHMS APPLIED TO INTRUSION DETECTION (2002, background): “Intrusion detection approaches are commonly divided into two categories: misuse detection and anomaly detection [1].”
• Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model (2017, methodology): “To assess the performance of their developed IDS, several researchers utilized different existing datasets [23].”
• Fraud detection system: A survey (2016, methodology): “Recently, fraud detection integrates anomaly based detection approach and misuse based detection approach by using data mining techniques (Allen, 2000; Sasirekha et al., 2012).”
• Toward cost-sensitive modeling for intrusion detection and response (2002, background): “After more than two decades of research and development efforts, the leading IDSs still have marginal detection rates and high false alarm rates, especially in the face of stealthy or novel intrusions [1] [17].”
• Global Intrusion Detection in the DOMINO Overlay System (2004, background): “There is potentially a wide disparity in the sizes and underlying NIDS and firewall software running in these networks, and extensions to provide support for DOMINO would be implemented as plug-ins for these systems.”
• Mining intrusion detection alarms for actionable knowledge (2002, background): “Over the past 10 years, the number as well as the severity of network-based computer attacks have significantly increased [1].”