Empirical Study on Microsoft Malware Classification

Microsoft Malware Classification Challenge

The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour. To date, the dataset has been cited in more than 50 r...

Empirical Study on COTS Components Classification

COTS-based development is gaining more and more attention. Effective COTS component classification will help integrators to successfully control the development process, such as selection and integration. In this paper, we present an empirical study to investigate the characterized classification proposed by previous research. From the result of this study, we conclude that some attributes are ...

Malware Detection using Classification of Variable-Length Sequences

In this paper, a novel method based on the graph is proposed to classify the sequence of variable length as feature extraction. The proposed method overcomes the problems of the traditional graph with variable length of data, without fixing length of sequences, by determining the most frequent instructions and insertion the rest of instructions on the set of “other”, save speed and memory. Acco...

On Malware Characterization and Attack Classification

Malware is one of the significant problems in the current Internet. Often security tool vendors develop an attack signature to deal with the attacks. However attack techniques such as polymorphism and metamorphism can be used by the attacker to generate multiple variants of the malware and complicate the signature identification. In this paper we present our analysis on sample set of malware an...

Classification Algorithms for Malware Detection