Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Machine Learning Algorithm Used for Detecting Malicious PDF Document


Affiliations
1 Department of Computer Science, The NorthCap University, Gurugram, India
     

   Subscribe/Renew Journal


In computer security field, Malware is a constancy problem and its involvement is increasing rapidly .Cyber criminals are heavily using PDF documents for launching attacks. These attacks routinely results in the loss of confidential information. Attackers attach the malicious PDF documents to emails to deliver malicious code to normal users and make use of social engineering to open the email, attachment. This article outlines machine learning based approach for differentiating between the malicious and benign PDF document by analyzing the essential differences in the structural properties of the document. We have compared the proposed system with the other machine learning classifiers over 6000 real world Benign and Malicious files. Finally, this research work provides you some machine learning technique for the detection of malicious PDF documents.


Keywords

Portable Document Format (PDF), Malicious PDF Document, Machine Learning, Malware Detection.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 182

PDF Views: 0




  • Machine Learning Algorithm Used for Detecting Malicious PDF Document

Abstract Views: 182  |  PDF Views: 0

Authors

Manju Dudy
Department of Computer Science, The NorthCap University, Gurugram, India
Yogita Gigras
Department of Computer Science, The NorthCap University, Gurugram, India
Anuradha
Department of Computer Science, The NorthCap University, Gurugram, India

Abstract


In computer security field, Malware is a constancy problem and its involvement is increasing rapidly .Cyber criminals are heavily using PDF documents for launching attacks. These attacks routinely results in the loss of confidential information. Attackers attach the malicious PDF documents to emails to deliver malicious code to normal users and make use of social engineering to open the email, attachment. This article outlines machine learning based approach for differentiating between the malicious and benign PDF document by analyzing the essential differences in the structural properties of the document. We have compared the proposed system with the other machine learning classifiers over 6000 real world Benign and Malicious files. Finally, this research work provides you some machine learning technique for the detection of malicious PDF documents.


Keywords


Portable Document Format (PDF), Malicious PDF Document, Machine Learning, Malware Detection.