In this paper, we combine static and dynamic analysis features with deep neural networks for Windows malware classification.
We envision an intelligent anti-malware system that utilizes the power of deep learning (DL) models.
This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples.
Recent work has proposed the Lempel-Ziv Jaccard Distance (LZJD) as a method to measure the similarity between binary byte sequences for malware classification.
Open set recognition problems exist in many domains.
N-grams have been a common tool for information retrieval and machine learning applications for decades.
To solve this, we investigated four cases: a text-only model, a hexadecimal-only model, a multi-input model using both text and hexadecimal inputs, and a model based on combining the individual results.
In this paper, we consider the problem of malware detection and classification based on image analysis.
These models target the core of the malicious operation by learning the presence and pattern of co-occurrence of malicious event actions from within these sequences.