Image-Based malware classification using ensemble of CNN architectures (IMCEC)

Vasan, Danish; Alazab, Mamoun; Wassan, Sobia; Safaei, Babak; Zheng, Qinghua

doi:10.1016/j.cose.2020.101748

Cited by 282 publications

(118 citation statements)

References 38 publications

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“…DBNs are used as an auto encoder for feature extraction to detect malware. Vasan et al [33] used handcrafted features as well as those of VGG16 and ResNet-50 CNNs to perform image-based malware classification.Čeponis and Goranin [34] analyzed the use of dual-flow deep learning methods, such as gated recurrent unit fully convolutional network (GRU-FCN) vs single-flow convolutional neural network (CNN) models for detection of malware signatures.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

et al. 2020

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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.

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Section: Related Workmentioning

confidence: 99%

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

et al. 2020

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“…However, this approach does not take into consideration information regarding essential market factors. We address this issue by introducing neural networks to take into account important market statistics and social sentiment [43], [44].…”

Section: B Stochastic Processesmentioning

confidence: 99%

Stochastic Neural Networks for Cryptocurrency Price Prediction

Patel¹,

Kalariya²,

Parmar³

et al. 2020

IEEE Access

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144

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Over the past few years, with the advent of blockchain technology, there has been a massive increase in the usage of Cryptocurrencies. However, Cryptocurrencies are not seen as an investment opportunity due to the market's erratic behavior and high price volatility. Most of the solutions reported in the literature for price forecasting of Cryptocurrencies may not be applicable for real-time price prediction due to their deterministic nature. Motivated by the aforementioned issues, we propose a stochastic neural network model for Cryptocurrency price prediction. The proposed approach is based on the random walk theory, which is widely used in financial markets for modeling stock prices. The proposed model induces layer-wise randomness into the observed feature activations of neural networks to simulate market volatility. Moreover, a technique to learn the pattern of the reaction of the market is also included in the prediction model. We trained the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) models for Bitcoin, Ethereum, and Litecoin. The results show that the proposed model is superior in comparison to the deterministic models. INDEX TERMS Cryptocurrency, multilayer perceptron, long short-term memory, random walk, stochasticity.

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“…This image conversion overcomes the packing and metamorphic evasion techniques. Ensemble CNN architecture for malware grayscale image classification has been addressed by the authors in [29]. They evaluated the results of VGG16 and ResNet-50 algorithms, using the Malimg dataset.…”

Section: Image Processing Techniquementioning

confidence: 99%

MSIC: Malware Spectrogram Image Classification

Azab

Khasawneh

2020

IEEE Access

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The heavy reliance on digital technology, by individuals and organizations, has reshaped the traditional economy into a digital economy. In response, cybercriminals' attention has shifted dramatically from showing off skills and conducting individual attacks into high sophisticated attacks with financial gain as the goal. This, inevitably, poses a challenge to the cybersecurity community as they strive to find solutions to preserve the confidentiality, availability and integrity of the individual users' and corporates' private data and services. Cybercriminals mainly deploy malware to achieve their goals, which could be in the form of ransomware, botnets, etc. The use of encryption, packing and polymorphism techniques makes it harder to detect the malware files, especially when these are created in great numbers every day. In this paper, a novel framework, named Malware Spectrogram Image Classification (MSIC), is proposed. It employs spectrogram images in conjunction with the convolution neural network to classify a malware file to its corresponding family and to differentiate it from a benign file. Further, this research shares with the research community two privately collected labeled malicious and benign datasets. The evaluation of MSIC showed its effectiveness to be 91.6% F-measure and 92.8% accuracy in classifying malware files to their corresponding families, in comparison to, respectively, 90.6% and 92.3% results produced by the grayscale image classification approach. Likewise, in classifying files as malicious or benign, MSIC scored 96% F-measure and accuracy results compared to 95.5% with the grayscale solution. Also, MSIC required less computational time in converting and resizing the files than the grayscale framework.

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Image-Based malware classification using ensemble of CNN architectures (IMCEC)

Cited by 282 publications

References 38 publications

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

Stochastic Neural Networks for Cryptocurrency Price Prediction

MSIC: Malware Spectrogram Image Classification

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