# Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape

arXiv: https://arxiv.org/abs/2012.11354 · HTML: https://mlsanity.com/paper/2012.11354/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [ROSpace: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical System](https://mlsanity.com/paper/2402.08468/) (2024)
- [Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study](https://mlsanity.com/paper/2401.16843/) (2024)
- [Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection](https://mlsanity.com/paper/2202.13611/) (2022)
- [ENCODE: Encoding NetFlows for Network Anomaly Detection](https://mlsanity.com/paper/2207.03890/) (2022)
- [State Frequency Estimation for Anomaly Detection](https://mlsanity.com/paper/2412.03442/) (2024)
- [Architecting software monitors for control-flow anomaly detection through large language models and conformance checking](https://mlsanity.com/paper/2511.10876/) (2025)

## Abstract (arXiv)

Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a given system. It is acknowledged as a reliable answer to the identification of zero-day attacks to such extent, several ML algorithms that suit for binary classification have been proposed throughout years. However, the experimental comparison of a wide pool of unsupervised algorithms for anomaly-based intrusion detection against a comprehensive set of attacks datasets was not investigated yet. To fill such gap, we exercise seventeen unsupervised anomaly detection algorithms on eleven attack datasets. Results allow elaborating on a wide range of arguments, from the behavior of the individual algorithm to the suitability of the datasets to anomaly detection. We conclude that algorithms as Isolation Forests, One-Class Support Vector Machines and Self-Organizing Maps are more effective than their counterparts for intrusion detection, while clustering algorithms represent a good alternative due to their low computational complexity. Further, we detail how attacks with unstable, distributed or non-repeatable behavior as Fuzzing, Worms and Botnets are more difficult to detect. Ultimately, we digress on capabilities of algorithms in detecting anomalies generated by a wide pool of unknown attacks, showing that achieved metric scores do not vary with respect to identifying single attacks.