Learning rules and clusters for anomaly detection in network traffic
PK Chan, MV Mahoney, MH Arshad - Managing Cyber Threats: Issues …, 2005 - Springer
Much of the intrusion detection research focuses on signature (misuse) detection, where
models are built to recognize known attacks. However, signature detection, by its nature …
models are built to recognize known attacks. However, signature detection, by its nature …
A machine learning approach to anomaly detection
PK Chan, MV Mahoney, MH Arshad - 2003 - repository.fit.edu
Much of the intrusion detection research focuses on signature (misuse) detection, where
models are built to recognize known attacks. However, signature detection, by its nature …
models are built to recognize known attacks. However, signature detection, by its nature …
Behavioral features for network anomaly detection
JP Early, CE Brodley - Machine learning and data mining for computer …, 2006 - Springer
Research in network intrusion detection has traditionally been divided into two components–
misuse detection and anomaly detection. The distinction between the two comes from the …
misuse detection and anomaly detection. The distinction between the two comes from the …
Modern intrusion detection, data mining, and degrees of attack guilt
S Noel, D Wijesekera, C Youman - Applications of data mining in computer …, 2002 - Springer
This chapter examines the state of modern intrusion detection, with a particular emphasis on
the emerging approach of data mining. The discussion parallels two important aspects of …
the emerging approach of data mining. The discussion parallels two important aspects of …
Enhancing cyber threat detection through machine learning-based behavioral modeling of network traffic patterns
F Bouchama, M Kamal - International Journal of Business …, 2021 - research.tensorgate.org
Cyber threats and data breaches have become more sophisticated and stealthier over time.
Traditional rule-based intrusion detection systems fail to detect many modern attacks. This …
Traditional rule-based intrusion detection systems fail to detect many modern attacks. This …
[图书][B] A machine learning approach to detecting attacks by identifying anomalies in network traffic
MV Mahoney - 2003 - search.proquest.com
The current approach to detecting novel attacks in network traffic is to model the normal
frequency of session IP addresses and server port usage and to signal unusual …
frequency of session IP addresses and server port usage and to signal unusual …
[图书][B] Network anomaly detection: A machine learning perspective
DK Bhattacharyya, JK Kalita - 2013 - books.google.com
With the rapid rise in the ubiquity and sophistication of Internet technology and the
accompanying growth in the number of network attacks, network intrusion detection has …
accompanying growth in the number of network attacks, network intrusion detection has …
[PDF][PDF] Data mining machine learning techniques–A study on abnormal anomaly detection system
MS Narayana, B Prasad, A Srividhya… - International Journal of …, 2011 - academia.edu
HE explosive increase in the number of networked machines and the widespread use of the
internet in organizations have led to an increase in the number of unauthorized activities, not …
internet in organizations have led to an increase in the number of unauthorized activities, not …
Toward a reliable anomaly-based intrusion detection in real-world environments
A popular approach for detecting network intrusion attempts is to monitor the network traffic
for anomalies. Extensive research effort has been invested in anomaly-based network …
for anomalies. Extensive research effort has been invested in anomaly-based network …
Anomaly-based network intrusion detection using machine learning
M Labonne - 2020 - theses.hal.science
In recent years, hacking has become an industry unto itself, increasing the number and
diversity of cyber attacks. Threats on computer networks range from malware to denial of …
diversity of cyber attacks. Threats on computer networks range from malware to denial of …