An Efficient Deep Learning Based Approach for Malware Classification

2021 ◽  
pp. 193-201
Author(s):  
Madhurima Rana ◽  
Swathi Edem
Keyword(s):  
Deep Learning ◽  
Malware Classification

scholarly journals AI-HydRa: Advanced hybrid approach using random forest and deep learning for malware classification

2021 ◽  
Vol 546 ◽  
pp. 420-435
Author(s):  
Suyeon Yoo ◽  
Sungjin Kim ◽  
Seungjae Kim ◽  
Brent Byunghoon Kang
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Hybrid Approach ◽  
Malware Classification

scholarly journals MalDeep: A Deep Learning Classification Framework against Malware Variants Based on Texture Visualization

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yuntao Zhao ◽  
Chunyu Xu ◽  
Bo Bo ◽  
Yongxin Feng
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Feature Space ◽  
Image Texture ◽  
Accuracy Rate ◽  
Texture Representation ◽  
Malware Classification ◽  
Classification Framework ◽  
Average Accuracy ◽  
New Feature

The increasing sophistication of malware variants such as encryption, polymorphism, and obfuscation calls for the new detection and classification technology. In this paper, MalDeep, a novel malware classification framework of deep learning based on texture visualization, is proposed against malicious variants. Through code mapping, texture partitioning, and texture extracting, we can study malware classification in a new feature space of image texture representation without decryption and disassembly. Furthermore, we built a malware classifier on convolutional neural network with two convolutional layers, two downsampling layers, and many full connection layers. We adopt the dataset, from Microsoft Malware Classification Challenge including 9 categories of malware families and 10868 variant samples, to train the model. The experiment results show that the established MalDeep has a higher accuracy rate for malware classification. In particular, for some backdoor families, the classification accuracy of the model reaches over 99%. Moreover, compared with other main antivirus software, MalDeep also outperforms others in the average accuracy for the variants from different families.

scholarly journals Malware Classification Based on System Call Sequences Using Deep Learning

2020 ◽  
Vol 5 (4) ◽  
pp. 207-216
Author(s):  
Rizki Jaka Maulana ◽  
Gede Putra Kusuma
Keyword(s):  
Deep Learning ◽  
System Call ◽  
Malware Classification ◽  
System Call Sequences

scholarly journals Understanding Deep Learning Architecture to Various Problems of Cyber Security

2021 ◽  
Vol 9 (12) ◽  
pp. 740-746
Author(s):  
Dr. Diwakar Ramanuj Tripathi
Keyword(s):  
Deep Learning ◽  
Cyber Security ◽  
Feature Representation ◽  
Generative Adversarial Networks ◽  
Android Malware ◽  
Security Applications ◽  
Malware Classification ◽  
Android Malware Detection ◽  
Adversarial Networks ◽  
Phishing Detection

Abstract: Traditional machine learning has evolved into deep learning. It's capable of extracting the best feature representation from raw input samples. Intrusion detection, malware classification, Android malware detection, spam and phishing detection, and binary analysis are just a few examples of how this has been used in cyber security. Deep auto encoders, limited Boltzmann machines, recurrent neural networks, generative adversarial networks, and other DL methods are all described in this study in a brief tutorial-style method. After that, we'll go over how each of the DL methods is employed in security applications. Keywords: Machine, Cyber, Security, Architecture, Technology.

scholarly journals A Survey on Malware Classification Using Machine Learning and Deep Learning

2021 ◽  
Vol 8 (6) ◽  
pp. 758
Author(s):  
Manish Goyal ◽  
Raman Kumar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Classification

Deep learning-based approach for malware classification

2021 ◽  
Vol 6 (2) ◽  
pp. 61
Author(s):  
Harisha Airbail ◽  
G. Mamatha ◽  
Rahul V. Hedge ◽  
P.R. Sushmika ◽  
Reshma Kumari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Malware Classification

A Novel Image-Based Malware Classification Model Using Deep Learning

2019 ◽  
pp. 150-161
Author(s):  
Yongkang Jiang ◽  
Shenghong Li ◽  
Yue Wu ◽  
Futai Zou
Keyword(s):  
Deep Learning ◽  
Classification Model ◽  
Malware Classification

Byte-level malware classification based on markov images and deep learning

2020 ◽  
Vol 92 ◽  
pp. 101740 ◽  
Author(s):  
Baoguo Yuan ◽  
Junfeng Wang ◽  
Dong Liu ◽  
Wen Guo ◽  
Peng Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Malware Classification

scholarly journals MalCaps: A Capsule Network Based Model for the Malware Classification

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 929
Author(s):  
Xiaoliang Zhang ◽  
Kehe Wu ◽  
Zuge Chen ◽  
Chenyi Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Network Architecture ◽  
Malware Detection ◽  
Complex Model ◽  
Detection Methods ◽  
Precise Position ◽  
Malware Classification ◽  
Model Generalization ◽  
Deep Feature Extraction

The research on malware detection enabled by deep learning has become a hot issue in the field of network security. The existing malware detection methods based on deep learning suffer from some issues, such as weak ability of deep feature extraction, relatively complex model, and insufficient ability of model generalization. Traditional deep learning architectures, such as convolutional neural networks (CNNs) variants, do not consider the spatial hierarchies between features, and lose some information on the precise position of a feature within the feature region, which is crucial for a malware file which has specific sections. In this paper, we draw on the idea of image classification in the field of computer vision and propose a novel malware detection method based on capsule network architecture with hyper-parameter optimized convolutional layers (MalCaps), which overcomes CNNs limitations by removing the need for a pooling layer and introduces capsule layers. Firstly, the malware is transformed into a grayscale image. Then, the dynamic routing-based capsule network is used to detect and classify the image. Without advanced feature extraction and with only a small number of labeled samples, the presented method is tested on an unbalanced Microsoft Malware Classification Challenge (MMCC) dataset and experimental results produce testing accuracy of 99.34%, improving on a number of traditional deep learning models posited in recent malware classification literature.

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