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A Protocol for Computer-Based Protein Structure and Function Prediction
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パラレル・アンサンブル・予測モデルに基づくサイバー脅威の諜報機関関係の共同抽出
Huan Wang1,2,3, Shenao Zhang1,2,3, Zhe Wang1,2,3
1School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou 545006, China.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は,サイバー脅威インテリジェンス (CTI) の知識グラフ構築のための新しい並列モデルを導入し,オーダー依存性の問題を克服し,アノテーションコストを削減することにより,エンティティ関係抽出を改善します.
科学分野:
- サイバーセキュリティ
- 人工知能
- 自然言語処理
背景:
- 知識グラフはサイバー脅威インテリジェンス (CTI) に不可欠です.
- CTI 知識グラフの構築には,エンティティ・リレーションの自動抽出が重要です.
- 既存のシーケンスタギング方法は,順序依存性のために重複する関係と闘う.
研究 の 目的:
- CTIにおける共同エンティティ関係抽出のための並列のアンサンブル予測ベースのモデルを提案する.
- オーバーラップする関係を処理するシーケンスのタグ付け方法の限界に対処する.
- CTI領域におけるラベル付きデータのコストと不足を減らす.
主な方法:
- トランスフォーマーからの双方向エンコーダー表現 (BERT) と双方向ゲートリキュアントユニット (BiGRU) を組み合わせた共同ネットワークが開発されました.
- アンサンブル予測モジュールとトライアード表現は,関節抽出のために設計されました.
- 非自動回帰のデコーダーは,関係トライアードセットの並列生成に使用された.
- SecCtiのデータセットは,データ不足を軽減するために,ラベリングと拡張のためにChatGPTを使用して作成されました.
主要な成果:
- 提案されたモデルは,共同エンティティ関係抽出のベースラインよりも4.6%の絶対F1改善を達成しました.
- パラレルで非自動回帰的なアプローチは,重複する関係を効果的に処理しました.
- ChatGPTを活用してデータを拡張することで アノテーションコストを大幅に削減しました
結論:
- CTIでの共同エンティティ・リレーション抽出には,より効果的な解決策を提供している.
- このアプローチは順序依存の問題に対処し,重複関係でのパフォーマンスを改善します.
- ChatGPTを使用したデータ増強戦略は,CTIのラベル付きデータセットを作成するための費用対効果の高い方法を提供します.
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