# CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks

arXiv: https://arxiv.org/abs/2104.08080 · HTML: https://mlsanity.com/paper/2104.08080/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 68 references that are in the map.
- First posted on arXiv 28 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data](https://mlsanity.com/paper/1902.07588/) (2019)

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

- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection](https://mlsanity.com/paper/2306.13029/) (2023)

## Abstract (arXiv)

Detecting cyber-anomalies and attacks are becoming a rising concern these days in the domain of cybersecurity. The knowledge of artificial intelligence, particularly, the machine learning techniques can be used to tackle these issues. However, the effectiveness of a learning-based security model may vary depending on the security features and the data characteristics. In this paper, we present "CyberLearning", a machine learning-based cybersecurity modeling with correlated-feature selection, and a comprehensive empirical analysis on the effectiveness of various machine learning based security models. In our CyberLearning modeling, we take into account a binary classification model for detecting anomalies, and multi-class classification model for various types of cyber-attacks. To build the security model, we first employ the popular ten machine learning classification techniques, such as naive Bayes, Logistic regression, Stochastic gradient descent, K-nearest neighbors, Support vector machine, Decision Tree, Random Forest, Adaptive Boosting, eXtreme Gradient Boosting, as well as Linear discriminant analysis. We then present the artificial neural network-based security model considering multiple hidden layers. The effectiveness of these learning-based security models is examined by conducting a range of experiments utilizing the two most popular security datasets, UNSW-NB15 and NSL-KDD. Overall, this paper aims to serve as a reference point for data-driven security modeling through our experimental analysis and findings in the context of cybersecurity.