  Malicious activities in cyberspace have gone further than simply hacking
machines and spreading viruses. It has become a challenge for a nations
survival and hence has evolved to cyber warfare. Malware is a key component of
cyber-crime, and its analysis is the first line of defence against attack. This
work proposes a novel deep boosted hybrid learning-based malware classification
framework and named as Deep boosted Feature Space-based Malware classification
(DFS-MC). In the proposed framework, the discrimination power is enhanced by
fusing the feature spaces of the best performing customized CNN architectures
models and its discrimination by an SVM for classification. The discrimination
capacity of the proposed classification framework is assessed by comparing it
against the standard customized CNNs. The customized CNN models are implemented
in two ways: softmax classifier and deep hybrid learning-based malware
classification. In the hybrid learning, Deep features are extracted from
customized CNN architectures and fed into the conventional machine learning
classifier to improve the classification performance. We also introduced the
concept of transfer learning in a customized CNN architecture based malware
classification framework through fine-tuning. The performance of the proposed
malware classification approaches are validated on the MalImg malware dataset
using the hold-out cross-validation technique. Experimental comparisons were
conducted by employing innovative, customized CNN, trained from scratch and
fine-tuning the customized CNN using transfer learning. The proposed
classification framework DFS-MC showed improved results, Accuracy: 98.61%,
F-score: 0.96, Precision: 0.96, and Recall: 0.96.
