# Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic

arXiv: https://arxiv.org/abs/1805.03735 · HTML: https://mlsanity.com/paper/1805.03735/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 5 of 5)

- [Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation](https://mlsanity.com/paper/2105.09637/) (2021)
- [On the Evaluation of Sequential Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2106.07961/) (2021)
- [DoS and DDoS Mitigation Using Variational Autoencoders](https://mlsanity.com/paper/2105.06899/) (2021)
- [Detecting Anomalous Network Communication Patterns Using Graph Convolutional Networks](https://mlsanity.com/paper/2311.18525/) (2023)
- [Research on CNN-BiLSTM Network Traffic Anomaly Detection Model Based on MindSpore](https://mlsanity.com/paper/2504.21008/) (2025)

## Abstract (arXiv)

We evaluate methods for applying unsupervised anomaly detection to cybersecurity applications on computer network traffic data, or flow. We borrow from the natural language processing literature and conceptualize flow as a sort of "language" spoken between machines. Five sequence aggregation rules are evaluated for their efficacy in flagging multiple attack types in a labeled flow dataset, CICIDS2017. For sequence modeling, we rely on long short-term memory (LSTM) recurrent neural networks (RNN). Additionally, a simple frequency-based model is described and its performance with respect to attack detection is compared to the LSTM models. We conclude that the frequency-based model tends to perform as well as or better than the LSTM models for the tasks at hand, with a few notable exceptions.