コシン類似性ベースのデータセットバランスとハイブリッドディープラーニングによるIoT侵入検知の最適化
Arvind Prasad1, Wael Mohammad Alenazy2, Naved Ahmad3
1Department of Computer Engineering & Applications, GLA University, Mathura, India. arvind.prasad@gla.ac.in.
Scientific reports
|August 22, 2025
まとめ
サイバーセキュリティの脅威は 増加しています 侵入検知データセットを効果的にバランスさせ,脅威検知を改善し,トレーニング時間を短縮します.
科学分野:
- サイバーセキュリティ
- 機械学習
- モノのインターネット (IoT)
背景:
- モノのインターネット (IoT) デバイスの拡散は,サイバー脅威の攻撃面を大幅に拡大しています.
- 侵入検知システム (IDS) は,リアルタイムで脅威を軽減するために不可欠ですが,攻撃の例が稀である不均衡なデータセットと闘います.
- IDSを不均衡なデータで訓練すると 侵入を検知できない偏ったモデルになります
研究 の 目的:
- 侵入検知システム (IDS) の改善のための新しいデータバランス技術であるコサイヌ類似性ベースの多数クラス削減 (CSMCR) を導入する.
- IoTのサイバーセキュリティにおける機能抽出と分類の強化のためのハイブリッドのディープラーニングモデルを開発し,評価する.
- CSMCRの有効性を実証し,オーバーフィッティングを防止し,モデルの解釈性を向上させる.
主な方法:
- CSMCRは,特性の相似性を分析し,データの完全性と多様性を保ち,冗長なマジョリティクラスサンプルを選択的に削除します.
- RegNetとFBNetアーキテクチャを統合したハイブリッドのディープラーニングモデルは,高度な機能抽出のために開発されました.
- モデルパフォーマンスを最適化し,バイアスを防止するために,データセットは1:1の比率でバランスをとりました.
主要な成果:
- CSMCR技術は,SMOTEのような伝統的な方法と比較して優れた性能を達成しました.
- 提案されたハイブリッドモデルは,RT-IoT2022で0.9758,UNSW Bot-IoTで0.9275という高いF1スコアを達成しました.
- CSMCRは訓練時間を53%短縮し,多数派と少数派の1:1の比率で最適なパフォーマンスを示しました.
結論:
- CSMCRは,IoT環境におけるIDSデータセットのバランスをとるための堅牢でスケーラブルで計算効率の高い戦略です.
- 最適なデータバランスは,IDSの性能,解釈可能性を大幅に向上させ,計算オーバーヘッドを削減します.
- 結果は,効果的なIoTネットワークセキュリティのためにデータ不均衡に対処することの重要性を強調しています.
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