# An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey

arXiv: https://arxiv.org/abs/2402.17045 · HTML: https://mlsanity.com/paper/2402.17045/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 143 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Machine Learning DDoS Detection for Consumer Internet of Things Devices](https://mlsanity.com/paper/1804.04159/) (2018)
- [Ransomware Classification and Detection With Machine Learning Algorithms](https://mlsanity.com/paper/2207.00894/) (2022)
- [AI Powered Anti-Cyber Bullying System using Machine Learning Algorithm of Multinomial Naive Bayes and Optimized Linear Support Vector Machine](https://mlsanity.com/paper/2207.11897/) (2022)
- [Adversarial Sampling for Fairness Testing in Deep Neural Network](https://mlsanity.com/paper/2303.02874/) (2023)
- [Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection](https://mlsanity.com/paper/2308.11834/) (2023)
- [Real Time Prediction of Drive by Download Attacks on Twitter](https://mlsanity.com/paper/1708.05831/) (2017)
- [Detection and Analysis of Drive-by Downloads and Malicious Websites](https://mlsanity.com/paper/2002.08007/) (2020)
- [Dishing Out DoS: How to Disable and Secure the Starlink User Terminal](https://mlsanity.com/paper/2303.00582/) (2023)

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

- [MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classification](https://mlsanity.com/paper/2512.17594/) (2025)

## Abstract (arXiv)

In this research, we analyzed the suitability of each of the current state-of-the-art machine learning models for various cyberattack detection from the past 5 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack. We also reviewed the suitability, effeciency and limitations of recent research on state-of-the-art classifiers and novel frameworks in the detection of differnet cyberattacks. Our result shows the need for; further research and exploration on machine learning approach for the detection of drive-by download attacks, an investigation into the mix performance of Naive Bayes to identify possible research direction on improvement to existing state-of-the-art Naive Bayes classifier, we also identify that current machine learning approach to the detection of SQLi attack cannot detect an already compromised database with SQLi attack signifying another possible future research direction.