Abstract
As the number of internet users increases so does the number of malicious attacks using malware. The detection of malicious code is becoming critical, and the existing approaches need to be improved. Here, we propose a feature fusion method to combine the features extracted from pre-trained AlexNet and Inception-v3 deep neural networks with features attained using segmentation-based fractal texture analysis (SFTA) of images representing the malware code. In this work, we use distinctive pre-trained models (AlexNet and Inception-V3) for feature extraction. The purpose of deep convolutional neural network (CNN) feature extraction from two models is to improve the malware classifier accuracy, because both models have characteristics and qualities to extract different features. This technique produces a fusion of features to build a multimodal representation of malicious code that can be used to classify the grayscale images, separating the malware into 25 malware classes. The features that are extracted from malware images are then classified using different variants of support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and other classifiers. To improve the classification results, we also adopted data augmentation based on affine image transforms. The presented method is evaluated on a Malimg malware image dataset, achieving an accuracy of 99.3%, which makes it the best among the competing approaches.
Highlights
Malware programs are undesirable harmful threats that are intended to damage the security of a computer system
The results are generated into three groups: (a) selection of features using traditional method (SFTA), (b) selection of features using traditional approach (SFTA) and pre-trained network (AlexNet, Inception-v3), and (c) deep convolutional neural network (CNN) and segmentation-based fractal texture analysis (SFTA) feature fusion lengthwise with principal component component analysis (PCA) selection method
We claim that we can build a high level of accuracy in recognizing malware from trusted samples deep learning with features derived from the pre-trained network and handcrafted method
Summary
Malware programs are undesirable harmful threats that are intended to damage the security of a computer system. Malware detection has become an essential concern in the cybersecurity community since malware is capable of causing excessive loss and harm to computer security. A huge amount of malware is generated intentionally. A recent Symantec report in 2019 [1] demonstrated that malware is growing by 36% annually and the total samples of malware are estimated to be beyond 430 million. The rapid growth of malware causes an extensive threat in our daily life. Data breaches due to malware activity often incur huge financial losses for major corporations [3]. Trojans and spyware are used in cyber espionage resulting in damage of geopolitical
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