Finding meaningful clusters in drive-by-download malware data is a particularly difficult task.
Malware data tends to contain overlapping clusters with wide variations of cardinality. This …

Malware detection is an important problem today. New malware appears every day and in
order to be able to detect it, it is important to recognize families of existing malware. Data …

Malware clustering and classification are important tools that enable analysts to prioritize
their malware analysis efforts. The recent emergence of fully automated methods for …

Anti-malware companies receive thousands of malware samples every day. To process this
large quantity, a number of automated analysis tools were developed. These tools execute a …

The numbers and diversity of malware variants grows exponentially over the years, and
there is a need to improve the efficiency of analysing large number of malware samples …

In this paper we fully describe a novel clustering method for malware, from the
transformation of data into a manipulable standardised data matrix, finding the number of …

Nowadays, along with the development of the malware writing techniques, the diversity and
amount of malware variants are constantly increasing and proliferation of these malware has …

This paper describes a novel method aiming to cluster datasets containing malware
behavioural data. Our method transform the data into an standardised data matrix that can …

In this research, we apply clustering techniques to the malware classification problem. We
compute clusters using the well-known K-means and Expectation Maximization algorithms …

In this research, we apply clustering techniques to the malware detection problem. Our goal
is to classify malware as part of a fully automated detection strategy. We compute clusters …