Summary of Towards Autonomous Cybersecurity: An Intelligent Automl Framework For Autonomous Intrusion Detection, by Li Yang et al.
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection
by Li Yang, Abdallah Shami
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Automated Machine Learning (AutoML)-based autonomous IDS framework aims to achieve autonomous cybersecurity for next-generation networks by automating the data analytics pipeline. The framework utilizes techniques such as Tabular Variational Auto-Encoder (TVAE) for data balancing, tree-based ML models for feature selection and base model learning, Bayesian Optimization (BO) for hyperparameter optimization, and Optimized Confidence-based Stacking Ensemble (OCSE) method for automated model ensemble. The proposed IDS was evaluated on two public benchmark network security datasets, CICIDS2017 and 5G-NIDD, demonstrating improved performance compared to state-of-the-art cybersecurity methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to keep networks safe from cyber attacks. It uses special computer programs called Automated Machine Learning (AutoML) to help detect and prevent these attacks. The AutoML program takes in information about the network and then learns how to recognize patterns that might be malicious. This helps it make better decisions about what is a real threat and what is not. The researchers tested their system on two big datasets and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Encoder » Feature selection » Hyperparameter » Machine learning » Optimization
