Machine Learning Techniques for Anomaly Detection: An Overview

By S. Omar, A. Ngadi, Hamid Hantoosh Jebur. 2013

347 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0278bbaf1db5df036f02393679d485260b1daeb7

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. Anca Dragan
2. Dylan Hadfield-Menell
3. Dorsa Sadigh
4. J'er'emy Scheurer
5. David Krueger
6. Wajih Ul Hassan
7. Claudia Shi
8. Micah Carroll

In Computer Networks and Communications

1. Wajih Ul Hassan
2. Wei Cheng
3. Zhengzhang Chen
4. Dorsa Sadigh
5. Carl A. Gunter
6. Haifeng Chen
7. Anca Dragan

In Signal Processing

1. Anca Dragan
2. David Krueger
3. Dorsa Sadigh
4. Carl A. Gunter
5. Qi Wang
6. Wajih Ul Hassan
7. Stefano Ermon
8. Diederik P. Kingma

Citations in the corpus

• You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis (2020, methodology): “We design PROVDETECTOR as an anomaly detection based technique [37] for two reasons: first, it is able to detect unknown attacks (as well as zero-day attacks) as it models the normal operation of a system; second, as the normal profiles are tailored for every application or system, it is very diffic”
• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023, methodology): “Anomaly detection techniques [Omar et al., 2013] can also be useful for flagging abnormal inputs likely to trigger bad behavior.”