SFEM: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods

Keyphrases

• Extraction Methods 100%
• Machine Learning Techniques 100%
• Structural Feature Extraction 100%
• Office Documents 100%
• Docx 57%
• XML 28%
• Attacker 28%
• Machine Learning Algorithms 28%
• True Positive Rate 28%
• Executable File 28%
• Antivirus 28%
• Machine Learning Classifiers 28%
• Cyber-attacks 14%
• False Positive Rate 14%
• Detection Method 14%
• Unknown Malware 14%
• High Detection Rate 14%
• Feature Selection Methods 14%
• Feature Selection 14%
• Feature Extraction 14%
• Feature Combination 14%
• Feature Representation 14%
• Malicious Code 14%
• Attribute Selection 14%
• Discriminative Features 14%
• Email Server 14%
• Advanced Persistent Threat 14%
• Random Forest Classifier 14%
• Signature-based Detection 14%
• Naïve Users 14%
• Ransomware Attacks 14%
• Document-oriented 14%
• Extraction Features 14%

Computer Science

• Machine Learning 100%
• Structural Feature 100%
• Feature Extraction 100%
• Office Document 100%
• Attackers 28%
• e-Mail 28%
• Machine Learning Algorithm 28%
• Feature Selection 28%
• Executable File 28%
• True Positive Rate 28%
• Antivirus 28%
• Malware 14%
• Detection Method 14%
• Unknown Malware 14%
• False Positive Rate 14%
• Detection Rate 14%
• Malicious Code 14%
• Discriminative Feature 14%
• Knowledge Machine 14%
• Advanced Persistent Threat 14%
• Targeted Cyber Attack 14%
• Random Forest Classifier 14%