Concepts de base
Supervised learning models, such as Random Forest, can effectively detect unknown attacks when trained with noise data in IDS frameworks.
Résumé
The content discusses the challenges of detecting unknown attacks in IDS due to the lack of sufficient attack samples. It introduces a strategy of training supervised learning models with noise data labeled as attacks to improve detection accuracy. The study evaluates the performance of Random Forest models trained with and without noise data on synthetic and benchmark IDS datasets. Results show that incorporating noise data enhances the model's ability to identify previously unseen attacks.
- Introduction to the rapid expansion of network systems and cyber threats.
- Challenges in building effective IDS models using supervised learning.
- Strategies for semi-supervised learning based IDS.
- Comparison of performances using 10 benchmark IDS datasets.
- Evaluation of Random Forest model trained with noise data on synthetic and benchmark datasets.
Stats
サンプル数が不足しているため、実際の攻撃サンプルを収集することは困難である。
ランダムに均等に分散された合成攻撃サンプルを使用して監督学習モデルをトレーニングする戦略を導入。
結果は、ノイズデータを追加したRFモデルが以前に見られなかった攻撃サンプルを識別する能力を向上させることを示しています。
Citations
"Most unseen attacks are detected as attacks."
"RF trained with noise exhibits a superior ability in identifying unseen attacks compared to the standard RF."