Add to ORKGMalware Packet-sequence Clustering and Analysis (MalPaCA) is a unsupervised clustering application for malicious network behavior, it currently uses solely sequential features to characterize network behavior. In this paper an extensive comparison between those features and statistical features is performed. During the comparison a better clustering performance achievable with statistical features for longer connection sequences is shown and advice on which features can be added to MalPaCA.CSE3000 Research ProjectComputer Science and Engineerin
We identify a new method for detecting malware within a network that can be processed in linear time...
We identify a new method for detecting malware within a network that can be processed in linear time...
We identify a new method for detecting malware within a network that can be processed in linear time...
MalPaCa is an unsupervised clustering tool, which the main purpose is to cluster unidirectional netw...
MalPaCA makes use of unsupervised machine learning to provide malware capability assessment by clust...
Identifying novel malware and their behaviour enables security engineers to prevent and protect user...
MalPaCa is a novel, unsupervised clustering algorithm, which creates based on the network flow of a ...
Malware family labels are known to be inconsistent. They are also black box since they do not repres...
Malware family labels are known to be inconsistent. They are also black-box since they do not repres...
Developing malware variants is extremely cheap for attackers because of the availability of various ...
Malware undoubtedly have become a major threat in modern society and their numbers are growing daily...
This paper describes a novel method aiming to cluster datasets containing malware behavioural data. ...
Malware samples has increased exponentially over the years, and there is a need to improve the effic...
The ever-increasing number of malware families and polymorphic variants creates a pressing need for ...
The numbers and diversity of malware variants grows exponentially over the years, and there is a nee...
We identify a new method for detecting malware within a network that can be processed in linear time...
We identify a new method for detecting malware within a network that can be processed in linear time...
We identify a new method for detecting malware within a network that can be processed in linear time...
MalPaCa is an unsupervised clustering tool, which the main purpose is to cluster unidirectional netw...
MalPaCA makes use of unsupervised machine learning to provide malware capability assessment by clust...
Identifying novel malware and their behaviour enables security engineers to prevent and protect user...
MalPaCa is a novel, unsupervised clustering algorithm, which creates based on the network flow of a ...
Malware family labels are known to be inconsistent. They are also black box since they do not repres...
Malware family labels are known to be inconsistent. They are also black-box since they do not repres...
Developing malware variants is extremely cheap for attackers because of the availability of various ...
Malware undoubtedly have become a major threat in modern society and their numbers are growing daily...
This paper describes a novel method aiming to cluster datasets containing malware behavioural data. ...
Malware samples has increased exponentially over the years, and there is a need to improve the effic...
The ever-increasing number of malware families and polymorphic variants creates a pressing need for ...
The numbers and diversity of malware variants grows exponentially over the years, and there is a nee...
We identify a new method for detecting malware within a network that can be processed in linear time...
We identify a new method for detecting malware within a network that can be processed in linear time...
We identify a new method for detecting malware within a network that can be processed in linear time...