Add to ORKGIn this paper, we propose a five-step approach to detect obfuscated malware by investigating the structural and behavioural features of API calls. We have developed a fully automated system to disassemble and extract API call features effectively from executables. Using n-gram statistical analysis of binary content, we are able to classify if an executable file is malicious or benign. Our experimental results with a dataset of 242 malwares and 72 benign files have shown a promising accuracy of 96.5% for the unigram model. We also provide a preliminary analysis by our approach using support vector machine (SVM) and by varying n-values from 1 to 5, we have analysed the performance that include accuracy, false positives and false negatives. By...
Malware is one of the most significant threats in today’s computing world since the number of websit...
This project aims to present the functionality and accuracy of five different machine learning algor...
This paper presents the detection techniques of anomalous programs based on the analysis of their sy...
In this paper, we propose a five-step approach to detect obfuscated malware by investigating the str...
In this paper, we propose a five-step approach to detect obfuscated malware by investigating the str...
One of the recent trends adopted by malware authors is to use packers or software tools that instiga...
In the era of information technology and connected world, detecting malware has been a major securit...
In the era of ubiquitous sensors and smart devices, detecting malware is becoming an endless battle ...
Data-driven public security networking and computer systems are always under threat from malicious c...
Malware is the primary attack vector against the modern enterprise. Therefore, it is crucial for bus...
This paper proposes a scalable approach for distinguishing malicious files from clean files by inves...
The widespread development of the malware industry is considered the main threat to our e-society. T...
Recently, most researchers have employed behaviour based detection systems to classify Portable Exec...
Malware is a major security threat confronting computer systems and networks and has increased in sc...
Malware replicates itself and produces offspring with the same characteristics but different signatu...
Malware is one of the most significant threats in today’s computing world since the number of websit...
This project aims to present the functionality and accuracy of five different machine learning algor...
This paper presents the detection techniques of anomalous programs based on the analysis of their sy...
In this paper, we propose a five-step approach to detect obfuscated malware by investigating the str...
In this paper, we propose a five-step approach to detect obfuscated malware by investigating the str...
One of the recent trends adopted by malware authors is to use packers or software tools that instiga...
In the era of information technology and connected world, detecting malware has been a major securit...
In the era of ubiquitous sensors and smart devices, detecting malware is becoming an endless battle ...
Data-driven public security networking and computer systems are always under threat from malicious c...
Malware is the primary attack vector against the modern enterprise. Therefore, it is crucial for bus...
This paper proposes a scalable approach for distinguishing malicious files from clean files by inves...
The widespread development of the malware industry is considered the main threat to our e-society. T...
Recently, most researchers have employed behaviour based detection systems to classify Portable Exec...
Malware is a major security threat confronting computer systems and networks and has increased in sc...
Malware replicates itself and produces offspring with the same characteristics but different signatu...
Malware is one of the most significant threats in today’s computing world since the number of websit...
This project aims to present the functionality and accuracy of five different machine learning algor...
This paper presents the detection techniques of anomalous programs based on the analysis of their sy...