Add to ORKGAutomated techniques to classify malware samples into their respective families are critical in cybersecurity. Previously research applied ��-means clustering to scores generated by hidden Markov models (HMM) as a means of dealing with the malware classification problem. In this research, we follow a somewhat similar approach, but instead of using HMMs to generate scores, we directly cluster the HMMs themselves. We obtain good results on a challenging malware dataset
Malicious software – so called malware – poses a major threat to the security of computer systems. T...
Encrypted code is often present in some types of advanced malware, while such code virtually never a...
Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to dete...
Automated techniques to classify malware samples into their respective families are critical in cybe...
In this research, we apply clustering techniques to the malware detection problem. Our goal is to cl...
Malware is a software which is developed for malicious intent. Malware is a rapidly evolving threat ...
Automatically classifying similar malware families is a challenging problem. In this research, we at...
With the ever increasing use of burgeoning volumes of data, machine learning systems involving minim...
Digital security is an important issue today, and efficient malware detection is at the forefront of...
Previous work has shown that we can effectively cluster certain classes of mal- ware into their resp...
Malware classification is an important and challenging problem in information security. Modern malwa...
In the last decade, a lot of machine learning and data mining based approaches have been used in the...
Discrete hidden Markov models (HMM) are often applied to the malware detection and classification pr...
abstract: Malware forensics is a time-consuming process that involves a significant amount of data c...
The numbers and diversity of malware variants grows exponentially over the years, and there is a nee...
Malicious software – so called malware – poses a major threat to the security of computer systems. T...
Encrypted code is often present in some types of advanced malware, while such code virtually never a...
Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to dete...
Automated techniques to classify malware samples into their respective families are critical in cybe...
In this research, we apply clustering techniques to the malware detection problem. Our goal is to cl...
Malware is a software which is developed for malicious intent. Malware is a rapidly evolving threat ...
Automatically classifying similar malware families is a challenging problem. In this research, we at...
With the ever increasing use of burgeoning volumes of data, machine learning systems involving minim...
Digital security is an important issue today, and efficient malware detection is at the forefront of...
Previous work has shown that we can effectively cluster certain classes of mal- ware into their resp...
Malware classification is an important and challenging problem in information security. Modern malwa...
In the last decade, a lot of machine learning and data mining based approaches have been used in the...
Discrete hidden Markov models (HMM) are often applied to the malware detection and classification pr...
abstract: Malware forensics is a time-consuming process that involves a significant amount of data c...
The numbers and diversity of malware variants grows exponentially over the years, and there is a nee...
Malicious software – so called malware – poses a major threat to the security of computer systems. T...
Encrypted code is often present in some types of advanced malware, while such code virtually never a...
Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to dete...