Malware Classification Based on System Call Sequences Using Deep Learning

Deep Learning for Classification of Malware System Call Sequences

The increase in number and variety of malware samples amplifies the need for improvement in automatic detection and classification of the malware variants. Machine learning is a natural choice to cope with this increase, because it addresses the need of discovering underlying patterns in large-scale datasets. Nowadays, neural network methodology has been grown to the state that can surpass limi...

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...

Android Malware Detection using Deep Learning on API Method Sequences

Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware...

SCMA: Scalable and Collaborative Malware Analysis using System Call Sequences

Malware is huge and growing at an exponential pace. Symantec observes 403 million new malware samples in 2011. Therefore, that efficiently and effectively analysis so many malware samples becomes a great challenge. Centralized systems cause problems of single point of failure as well as processing bottlenecks. Previous distributed systems are mainly applied for specific or simple malware. This ...

Malware Characterization Using Windows API Call Sequences