# Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey

**Authors:** Elike Hodo, Xavier Bellekens, Andrew Hamilton, Christos Tachtatzis, Robert Atkinson  
**arXiv:** [1701.02145](https://arxiv.org/abs/1701.02145)  
**Date:** 2017-01-09  
**Summary page:** https://www.summarizepaper.com/en/paper/1701.02145v1/

## Key points

- Reliable and efficient intrusion detection systems (IDS) are still needed to handle large amounts of data in real-time situations with changing patterns.
- The paper focuses on the classification of IDS and presents a taxonomy and survey specifically focused on shallow and deep networks IDS.
- The authors review previous and current works in this area to provide an overview of machine learning techniques used for detecting anomalies.
- Feature selection plays a crucial role in the effectiveness of machine learning-based IDS, influencing the classification and training phase.
- False and true positive alarm rates are addressed to help researchers model reliable and efficient machine learning-based IDS.
- The paper offers insights into machine learning techniques used for anomaly detection in shallow and deep networks IDS.
- Emphasizes the importance of feature selection in IDS performance.
- Discusses alarm rates to aid researchers in building effective intrusion detection systems.

## Summary

The paper titled "Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey" by Elike Hodo, Xavier Bellekens, Andrew Hamilton, Christos Tachtatzis, and Robert Atkinson explores the field of intrusion detection systems (IDS). The authors highlight that reliable and efficient IDS are still needed to handle large amounts of data in real-time situations with changing patterns. The main contribution of this work is the classification of IDS and the presentation of a taxonomy and survey specifically focused on shallow and deep networks intrusion detection systems. The authors review previous and current works in this area to provide an overview of machine learning techniques used for detecting anomalies. One important aspect discussed in the paper is feature selection which plays a crucial role in the effectiveness of machine learning-based IDS. They explain how feature selection influences the classification and training phase of machine learning IDS. Additionally, they address false and true positive alarm rates which helps researchers model reliable and efficient machine learning-based intrusion detection systems. In conclusion, this paper provides a comprehensive analysis of shallow and deep networks intrusion detection systems offering insights into machine learning techniques used for anomaly detection, emphasizing the importance of feature selection in IDS performance as well as discussing alarm rates to aid researchers in building effective intrusion detection systems.