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不正検出のための強化されたケプラー最適化とゴーストオポジションベース学習

Ria H Egami1, Amr A Abd El-Mageed2,3, Mona Gafar4

  • 1Department of Mathematics, College of Science and Humanity, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.

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まとめ

本研究では、オンライン脅威に対する特徴選択と精度を大幅に向上させる高度な不正検出手法であるBKOA-GOBLを紹介します。不均衡データであっても、不正およびマルウェア検出において既存のアルゴリズムを上回っています。

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ケプラー最適化アルゴリズム特徴選択不正検出ゴーストオポジションベース学習 (GOBL)機械学習メタヒューリスティックアルゴリズム

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科学分野:

  • コンピュータサイエンス
  • 人工知能
  • サイバーセキュリティ

背景:

  • 従来の不正およびマルウェア検出システムは、新しい脅威、クラス不均衡、高次元データに苦労しています。
  • オンラインアクティビティの増加は、より堅牢で適応性の高い検出方法論を必要としています。

研究 の 目的:

  • 特徴選択 (FS) を改善するために、ゴーストオポジションベース学習 (GOBL) で二値ケプラー最適化アルゴリズム (BKOA) を強化した高度な不正検出 (FD) 方法論、BKOA-GOBLを提案すること。
  • クラス不均衡に対処するためにランダムアンダーサンプリング (RUS) を使用すること。

主な方法:

  • BKOA-GOBLは、GOBLとBKOAを統合して探索と活用のバランスを取り、早期収束を防ぎ、検索の多様性を高めます。
  • ランダムアンダーサンプリング (RUS) は、不正データセットのクラス不均衡を処理するために使用されます。
  • 5つの実世界のベンチマークでk最近傍法 (K-NN) およびXGBoost (Xgb-tree) 分類器を使用して検証が実行されました。

主要な成果:

  • BKOA-GOBLは、いくつかのベンチマークで最大99.96%の分類精度と81.82%の特徴削減を達成しました。一貫して高い精度、再現率、ROC_AUC、F1スコアは信頼性の高い検出を示しましたが、一部のデータセットでは課題がありました。比較分析により、BKOA-GOBLが精度と効率において12のメタヒューリスティックアルゴリズム (MHA) および機械学習 (ML) 分類器を上回ることが示されました。

結論:

  • BKOA-GOBLは、高次元の不正およびマルウェア検出のための堅牢で適応性があり効果的な方法論です。
  • このアプローチは、実世界のシナリオにおいて統計的な優位性と実用的な適用性を示しています。
  • GOBLとRUSの統合は、従来の検出システムの限界を効果的に解決します。