Add to ORKGAutomatically classifying similar malware families is a challenging problem. In this research, we attempt to classify malware families by applying machine learning to machine learning models. Specifically, we train hidden Markov models (HMM) for each malware family in our dataset. The resulting models are then compared in two ways. First, we treat the HMM matrices as images and experiment with convolutional neural networks (CNN) for image classification. Second, we apply support vector machines (SVM) to classify the HMMs. We analyze the results and discuss the relative advantages and disadvantages of each approach
According to AV vendors malicious software has been growing exponentially last years. One of the ma...
Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to dete...
Signature and anomaly based detection have long been quintessential techniques used in malware detec...
Automatically classifying similar malware families is a challenging problem. In this research, we at...
Malware is a software which is developed for malicious intent. Malware is a rapidly evolving threat ...
Automated techniques to classify malware samples into their respective families are critical in cybe...
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...
Research in the field of malware classification often relies on machine learning models that are tra...
Malware classification is an important and challenging problem in information security. Modern malwa...
Malware is software that is designed to do harm to computer systems. Malware often evolves over a pe...
Discrete hidden Markov models (HMM) are often applied to the malware detection and classification pr...
With the ever increasing use of burgeoning volumes of data, machine learning systems involving minim...
When training a machine learning model, there is likely to be a tradeoff between the accuracy of the...
Many different machine learning and deep learning techniques have been successfully employed for ma...
According to AV vendors malicious software has been growing exponentially last years. One of the ma...
Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to dete...
Signature and anomaly based detection have long been quintessential techniques used in malware detec...
Automatically classifying similar malware families is a challenging problem. In this research, we at...
Malware is a software which is developed for malicious intent. Malware is a rapidly evolving threat ...
Automated techniques to classify malware samples into their respective families are critical in cybe...
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...
Research in the field of malware classification often relies on machine learning models that are tra...
Malware classification is an important and challenging problem in information security. Modern malwa...
Malware is software that is designed to do harm to computer systems. Malware often evolves over a pe...
Discrete hidden Markov models (HMM) are often applied to the malware detection and classification pr...
With the ever increasing use of burgeoning volumes of data, machine learning systems involving minim...
When training a machine learning model, there is likely to be a tradeoff between the accuracy of the...
Many different machine learning and deep learning techniques have been successfully employed for ma...
According to AV vendors malicious software has been growing exponentially last years. One of the ma...
Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to dete...
Signature and anomaly based detection have long been quintessential techniques used in malware detec...