A Survey of the Recent Trends in Deep Learning Based Malware Detection

A Survey of the Recent Trends in Deep Learning Based Malware Detection

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Monitoring Indicators of Compromise (IOC) leads to malware detection for identifying malicious activity.Malicious activities potentially lead to a system breach or data compromise.Various tools and anti-malware products exist for the detection of malware and cyberattacks utilizing IOCs, but all have several shortcomings.

For instance, anti-malware systems make use of malware signatures, requiring a database containing such signatures to be constantly updated.Additionally, this technique does not work for zero-day attacks or variants of existing malware.In the quest to fight zero-day attacks, the research paradigm shifted from primitive methods to classical machine learning-based methods.

Primitive redken shades 9gi methods are limited in catering to anti-analysis techniques against zero-day attacks.Hence, the direction of research moved towards methods utilizing classic machine learning, however, machine learning methods also come with certain limitations.They may include but not limited to the latency/lag introduced by feature-engineering phase on the entire training dataset as opposed to the real-time analysis requirement.

Likewise, additional layers of data engineering to cater to the increasing volume of data introduces further delays.It led to the use of deep learning-based methods for malware detection.With the speedy occurrence of zero-day malware, researchers chose to experiment with few shot learning so that reliable solutions can be produced for malware detection with even a small amount of data at hand for training.

In this paper, we surveyed several possible strategies to support the real-time detection of malware and propose a hierarchical model to discover security events or threats in real-time.A key focus in this survey is on the use of Deep Learning-based methods.Deep Learning based methods dominate this research area by providing automatic feature engineering, the capability of dealing nitrile gloves in a bucket with large datasets, enabling the mining of features from limited data samples, and supporting one-shot learning.

We compare Deep Learning-based approaches with conventional machine learning based approaches and primitive (statistical analysis based) methods commonly reported in the literature.