# Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection

arXiv: https://arxiv.org/abs/2006.15344 · HTML: https://mlsanity.com/paper/2006.15344/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2020.

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

- [Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs](https://mlsanity.com/paper/2509.06550/) (2025)
- [Few-Shot Network Intrusion Detection Using Online Triplet Mining](https://mlsanity.com/paper/2605.17530/) (2026)

## Abstract (arXiv)

Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outlier-based zero-day detection research suffers from high false-negative rates, thus limiting their practical use and performance. This paper proposes an autoencoder implementation for detecting zero-day attacks. The aim is to build an IDS model with high recall while keeping the miss rate (false-negatives) to an acceptable minimum. Two well-known IDS datasets are used for evaluation-CICIDS2017 and NSL-KDD. In order to demonstrate the efficacy of our model, we compare its results against a One-Class Support Vector Machine (SVM). The manuscript highlights the performance of a One-Class SVM when zero-day attacks are distinctive from normal behaviour. The proposed model benefits greatly from autoencoders encoding-decoding capabilities. The results show that autoencoders are well-suited at detecting complex zero-day attacks. The results demonstrate a zero-day detection accuracy of [89-99%] for the NSL-KDD dataset and [75-98%] for the CICIDS2017 dataset. Finally, the paper outlines the observed trade-off between recall and fallout.