Summary of Explainable Ai For Comparative Analysis Of Intrusion Detection Models, by Pap M. Corea et al.
Explainable AI for Comparative Analysis of Intrusion Detection Models
by Pap M. Corea, Yongxin Liu, Jian Wang, Shuteng Niu, Houbing Song
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 paper presents a study on Explainable Artificial Intelligence (XAI) applied to intrusion detection from network traffic using various machine learning models. The authors evaluate the performance of seven different models, including Linear Regression, Logistic Regression, Linear Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Decision Trees, and Multi-Layer Perceptrons (MLP), on the UNSW-NB15 Dataset to achieve an accuracy of 90%. The results show that most classifiers rely on fewer than three critical features, highlighting the importance of effective feature engineering. The authors also find that Random Forest provides the best performance in terms of accuracy, time efficiency, and robustness. To facilitate further research, the data and code are available online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make artificial intelligence models more understandable when detecting intrusions in computer networks. It tests different types of machine learning models to see which one works best for this task. The study uses a special dataset called UNSW-NB15 and trains all the models to be 90% accurate. What’s surprising is that most models only use a few important features to get these good results, rather than relying on complex algorithms. The authors also find that a type of model called Random Forest does particularly well in terms of accuracy, speed, and reliability. You can find more information about the dataset and code used in this study online. |
Keywords
» Artificial intelligence » Feature engineering » Linear regression » Logistic regression » Machine learning » Random forest » Support vector machine
