# Efficient Intrusion Detection Using Evidence Theory

arXiv: https://arxiv.org/abs/2103.08585 · HTML: https://mlsanity.com/paper/2103.08585/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)

## Built on by (most cited first, 2 of 2)

- [Adversarial Training for Deep Learning-based Intrusion Detection Systems](https://mlsanity.com/paper/2104.09852/) (2021)
- [Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems](https://mlsanity.com/paper/2112.12095/) (2021)

## Abstract (arXiv)

Intrusion Detection Systems (IDS) are now an essential element when it comes to securing computers and networks. Despite the huge research efforts done in the field, handling sources' reliability remains an open issue. To address this problem, this paper proposes a novel contextual discounting method based on sources' reliability and their distinguishing ability between normal and abnormal behavior. Dempster-Shafer theory, a general framework for reasoning under uncertainty, is used to construct an evidential classifier. The NSL-KDD dataset, a significantly revised and improved version of the existing KDDCUP'99 dataset, provides the basis for assessing the performance of our new detection approach. While giving comparable results on the KDDTest+ dataset, our approach outperformed some other state-of-the-art methods on the KDDTest-21 dataset which is more challenging.