# Utilising Flow Aggregation to Classify Benign Imitating Attacks

arXiv: https://arxiv.org/abs/2103.04208 · HTML: https://mlsanity.com/paper/2103.04208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 6 Mar 2021.

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

- [Leveraging Siamese Networks for One-Shot Intrusion Detection Model](https://mlsanity.com/paper/2006.15343/) (2020)

## Built on by (most cited first, 1 of 1)

- [Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial Flows](https://mlsanity.com/paper/2407.02856/) (2024)

## Abstract (arXiv)

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.