Classification of Malware Variant Based on Ensemble Learning

Guo, Hui; Huang, Shuguang; Zhang, Min; Pan, Zhengqiang; Shi, Fan; Huang, Cheng; Li, Beibei

doi:10.1007/978-3-030-62223-7_11

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Anti‐detection technology, on the other hand, is updated on a regular basis. To combat different anti‐killing technology, malware employs shell, obfuscation, virtual machine protection, and other techniques 8 …”

Section: Introductionmentioning

confidence: 99%

Enhanced capsule network‐based executable files malware detection and classification—deep learning approach

Shelar,

Rao

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryMalware has considerably increased recently, posing a serious security danger to both people and enterprises. In order to distinguish and stop the negative effects of malware, a variety of machine and deep learning approaches have been used to detect it. However, while extracting malware features, the feature‐to‐feature spatial hierarchy is not taken into account by the existing techniques and as a result, information is lost during the pooling operation. Hence, a modified capsule deep neural network was developed in which discriminative features are extracted from three channel image derived from malware binary with considering feature‐to‐feature spatial hierarchy. Also, conventional capsule deep neural network is modified by adding a global average pooling layer before fully connected layer thereby classified the dataset as malicious or benign without any loss of information. Moreover, these malwares were not accurately classified based on their families using existing variants of convolutional neural network (CNN) since malware family variants can modify due to minute changes in malware binaries. Hence, a hybrid deep convolutional neural network (DCNN) and long‐short‐term memory (LSTM) has been utilized that determine minute changes in malware binaries using LSTM without vanishing gradient issue and effectively perform malware family classification using DCNN. As a result, the proposed approach successfully identifies malware in executable files and categorizes malware into families with 98.5% accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhanced capsule network‐based executable files malware detection and classification—deep learning approach

Shelar,

Rao

2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

Unsupervised Learning Approaches for Construction of Malware Families

Jackson

Ghosh

2022

2022 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

Deep Learning Model with Sequential Features for Malware Classification

Song

Hou

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Currently, malware shows an explosive growth trend. Demand for classifying malware is also increasing. The problem is the low accuracy of both malware detection and classification. From the static features of malicious families, a new deep learning method of TCN-BiGRU was proposed in this study, which combined temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU). First, we extracted the features of malware assembly code sequences and byte code sequences. Second, we shortened the opcode sequences by TCN to explore the features in the data and then used the BiGRU network to capture the opcode sequences in both directions to achieve deep extraction of the features of the opcode sequences. Finally, the fully connected and softmax layers were used to output predictions of the deep features. Multiple comparisons and ablation experiments demonstrated that the accuracy of malware detection and classification were effectively improved by our method. Our overall performance was 99.72% for samples comprising nine different classes, and our overall performance was 96.54% for samples comprising two different classes.

show abstract

Classification of Malware Variant Based on Ensemble Learning

Cited by 3 publications

References 11 publications

Enhanced capsule network‐based executable files malware detection and classification—deep learning approach

Enhanced capsule network‐based executable files malware detection and classification—deep learning approach

Unsupervised Learning Approaches for Construction of Malware Families

Deep Learning Model with Sequential Features for Malware Classification

Contact Info

Product

Resources

About