# Anticipated Network Surveillance -- An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics

arXiv: https://arxiv.org/abs/2312.17270 · HTML: https://mlsanity.com/paper/2312.17270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 52 references that are in the map.
- First posted on arXiv 27 Dec 2023.

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

- [Cyber Threat Intelligence : Challenges and Opportunities](https://mlsanity.com/paper/1808.01162/) (2018)
- [Predicting Cyber Attack Rates with Extreme Values](https://mlsanity.com/paper/1603.07432/) (2016)

## Abstract (arXiv)

Machine learning and data mining techniques are utiized for enhancement of the security of any network. Researchers used machine learning for pattern detection, anomaly detection, dynamic policy setting, etc. The methods allow the program to learn from data and make decisions without human intervention, consuming a huge training period and computation power. This paper discusses a novel technique to predict an upcoming attack in a network based on several data parameters. The dataset is continuous in real-time implementation. The proposed model comprises dataset pre-processing, and training, followed by the testing phase. Based on the results of the testing phase, the best model is selected using which, event class which may lead to an attack is extracted. The event statistics are used for attack