Add to ORKGA technique to improve computer security is to test an executable for the presence of malicious code without running the executable. This publication describes systems and techniques for machine learning on application-programming-interface-call (API-call) n-grams from static analysis to automatically determine whether an executable or shared library binary file includes indicators of malicious code. The systems and techniques generate API-call graphs from the file. From the API-call graphs, the systems and techniques generate n-grams. A machine-learned model, using the n-grams, then identifies malicious code or code that performs unwanted behavior
Recently, most researchers have employed behaviour based detection systems to classify Portable Exec...
The widespread development of the malware industry is considered the main threat to our e-society. T...
This project aims to present the functionality and accuracy of five different machine learning algor...
Malware is a serious threat being posed and it has been a continuous process of protecting the syste...
Today, the amount of malware is growing very rapidly, and the types and behaviors of malware are bec...
Researchers employ behavior based malware detection models that depend on API tracking and analyzing...
Abstract: The recent growth in Internet usage has motivated the creation of new malicious code for v...
Malicious code detection is a critical part of any cyber security operation. Typically, the behavior...
A behavior model of a program captures the correct ways of invoking its Application Programming Inte...
Malicious software authors have shifted their focus from illegal and clearly malicious software to p...
Abstract: malicious software also known as malware are the critical security threat experienced by t...
Abstract — Signature-based malicious code detection is the standard technique in all commercial anti...
Although automatically finding software vulnerabilities is an important problem, existing code analy...
Certain techniques for testing for the presence of malicious code within executables are based on th...
Abstract — Signature-based malicious code detection is the standard technique in all commercial anti...
Recently, most researchers have employed behaviour based detection systems to classify Portable Exec...
The widespread development of the malware industry is considered the main threat to our e-society. T...
This project aims to present the functionality and accuracy of five different machine learning algor...
Malware is a serious threat being posed and it has been a continuous process of protecting the syste...
Today, the amount of malware is growing very rapidly, and the types and behaviors of malware are bec...
Researchers employ behavior based malware detection models that depend on API tracking and analyzing...
Abstract: The recent growth in Internet usage has motivated the creation of new malicious code for v...
Malicious code detection is a critical part of any cyber security operation. Typically, the behavior...
A behavior model of a program captures the correct ways of invoking its Application Programming Inte...
Malicious software authors have shifted their focus from illegal and clearly malicious software to p...
Abstract: malicious software also known as malware are the critical security threat experienced by t...
Abstract — Signature-based malicious code detection is the standard technique in all commercial anti...
Although automatically finding software vulnerabilities is an important problem, existing code analy...
Certain techniques for testing for the presence of malicious code within executables are based on th...
Abstract — Signature-based malicious code detection is the standard technique in all commercial anti...
Recently, most researchers have employed behaviour based detection systems to classify Portable Exec...
The widespread development of the malware industry is considered the main threat to our e-society. T...
This project aims to present the functionality and accuracy of five different machine learning algor...