行動ベースの分析とBERTテクニックを用いた堅牢でダイナミックなマルウェア検出および分類モデル
1Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Kingdom of Saudi Arabia.
PloS one
|September 4, 2025
まとめ
この研究では,特性を抽出するために BERT を使用した行動ベースのマルウェア分類モデルを導入し, 92.25%の精度を達成しました. Support Vector MachinesとRandom Forestは,マルウェアファミリーを特定する上で強力なパフォーマンスを示しました.
科学分野:
- サイバーセキュリティ
- 機械学習
- ソフトウェアエンジニアリング
背景:
- マルウェアの分類は,進化する脅威のために困難です.
- シグネチャーベースの 静的な分析方法は 洗練されたマルウェアには不十分です
- 効果的なマルウェア検出には 行動ベースの分析が不可欠です
研究 の 目的:
- 実行可能なファイルの動作を分析する新しいマルウェア検出モデルを提案する.
- 機能抽出のためのBERTを使用してマルウェア分類の精度を高める.
- マルウェアファミリーの異なる機械学習分類器のパフォーマンスを評価する.
主な方法:
- 実行ファイル (.exe) は,VirusTotal を通じてセキュアな環境での動作を分析した.
- 行動ログから特性を抽出するためにBERTモデルが使用されました.
- サポートベクトルマシン (SVM),ランダムフォレスト,ナイヴベイズ分類器が評価されました.
主要な成果:
- 提案された行動ベースのモデルは92.25%の精度と100の時代後に91.22%のF1スコアを達成した.
- SVMとRandom ForestはAdware (0.98) とBackDoor (0.91) に対して高いF1スコアを示した.
- ネイヴ・ベイズはフェイクアラートでは不十分でした (F1スコア: 0.64).
結論:
- BERT機能と組み合わせた行動ベースの分析は,マルウェアの分類に有効です.
- SVMとランダムフォレストは,このタスクのための信頼できる分類器です.
- 関連分析を通してクラス間の関係を理解することは価値があります.
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