Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Methods of Classification and Identification01:28

Methods of Classification and Identification

181
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
181
Classification of Systems-I01:26

Classification of Systems-I

293
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
293
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

136
Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
136
Classification of Systems-II01:31

Classification of Systems-II

240
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
240
Classification of Signals01:30

Classification of Signals

875
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
875
Force Classification01:22

Force Classification

1.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.6K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

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

Abdulrahman Hassan Alhazmi1

  • 1Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Kingdom of Saudi Arabia.

PloS one
|September 4, 2025
PubMed
まとめ
この要約は機械生成です。

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.

さらに関連する動画

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

関連する実験動画

Last Updated: Sep 9, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

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.