IMCLNet: A lightweight deep neural network for Image-based Malware Classification

Zou, Binghui; Cao, Chunjie; Tao, Fangjian; Wang, Longjuan

doi:10.1016/j.jisa.2022.103313

Cited by 8 publications

(3 citation statements)

References 49 publications

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“…XRan provides comprehensive explanations for the detection scheme, the proposed RFSA extends this analysis to include a financial perspective. A capsule network named FACILE was designed by [24] to address challenges in malware classification. This model specifically focused on the efficiency and performance of capsule networks.…”

Section: Comparative Analysis Of Existing Studiesmentioning

confidence: 99%

“…FACILE achieves this by utilizing fewer capsules and introducing balance coefficients during routing to enhance representational power and stabilize the training process. The study conducted experiments on various datasets, demonstrating that FACILE requires significantly fewer capsules and parameters compared to the original CapsNet [24]. While FACILE excels in addressing challenges in capsule networks for malware classification, the RFSA enhances the study by broadening the analysis to encompass a financial perspective.…”

Section: Comparative Analysis Of Existing Studiesmentioning

confidence: 99%

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RFSA: A Ransomware Feature Selection Algorithm for Multivariate Analysis of Malware Behavior in Cryptocurrency

Nkongolo Wa Nkongolo

2024

IJCDS

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This research introduces innovative features tailored to capture distinctive characteristics of ransomware activity within the cryptocurrency ecosystem. The study employs a multifaceted analysis to delve into ransomware-related data encompassing transaction metadata, ransom analysis, behavioral patterns, and financial aspects. A feature selection algorithm is explored to discern ransomware transactions in Bitcoin (BTC) and the United States Dollar (USD) using the UGRansome dataset. This comprehensive dataset of ransomware-related transactions facilitates the proposal of novel features designed to capture the unique traits of ransomware activity. The correlation matrix and temporal analysis of these features contribute to a nuanced understanding of the dynamic nature of ransomware threats. The research presents the Ransomware Feature Selection Algorithm (RFSA) based on Gini Impurity and Mutual Information (MI) to effectively select crucial ransomware features. Evaluation metrics such as precision, recall, accuracy, and F1 score highlight the effectiveness of the RFSA. The analysis reveals that approximately 68% of ransomware incidents involve BTC transactions ranging from 1.46 to 2.56, with an average of 2.01 BTC transactions per attack. Moreover, ransomware causes financial damages ranging from 4.38 to 172.36 USD, with an average damage of 88.37 USD. The RFSA identifies 17 ransomware types and their associated malware to shed light on their characteristics. The study investigates the pricing of ransomware and reveals that TowerWeb is associated with the highest fee, amounting to 135.26 BTC, while CryptoLocker has the lowest fee, recorded at 10.51 BTC. Additionally, the impact of ransomware duration on financial gains and network flow is investigated, disclosing a correlation between extended duration and higher financial gains. The research achieves outstanding performance metrics, including an MI score of 95%, accuracy of 93%, recall of 92%, and precision of 89%. These results showcase the superiority of the proposed approach over existing studies, emphasizing the dynamic and adaptable nature of ransomware demands. The findings suggest that there is no fixed amount for specific cyberattacks. This underscores the importance of adapting to the evolving landscape of ransomware threats.

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Section: Comparative Analysis Of Existing Studiesmentioning

confidence: 99%

Section: Comparative Analysis Of Existing Studiesmentioning

confidence: 99%

RFSA: A Ransomware Feature Selection Algorithm for Multivariate Analysis of Malware Behavior in Cryptocurrency

Nkongolo Wa Nkongolo

2024

IJCDS

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“…At present, the common methods for identifying out-of-vocabulary words are divided into the following two categories: rule-based approach and statistics-based approach [1][2] . Among them, the recognition of out-of-vocabulary words based on rules is based on the relevant principles of word formation and characteristics of speech to construct rules [3][4] . Then by building custom rules to identify and find the out-of-vocabulary words.…”

Section: Related Workmentioning

confidence: 99%

Design and research of Chinese word segmentation method in architecture field

Fan¹,

Cao²,

Guan³

2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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Novel hybrid classifier based on fuzzy type-III decision maker and ensemble deep learning model and improved chaos game optimization

Mehrabi Hashjin,

Amiri,

Mohammadzadeh

et al. 2024

Cluster Comput

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This paper presents a unique hybrid classifier that combines deep neural networks with a type-III fuzzy system for decision-making. The ensemble incorporates ResNet-18, Efficient Capsule neural network, ResNet-50, the Histogram of Oriented Gradients (HOG) for feature extraction, neighborhood component analysis (NCA) for feature selection, and Support Vector Machine (SVM) for classification. The innovative inputs fed into the type-III fuzzy system come from the outputs of the mentioned neural networks. The system’s rule parameters are fine-tuned using the Improved Chaos Game Optimization algorithm (ICGO). The conventional CGO’s simple random mutation is substituted with wavelet mutation to enhance the CGO algorithm while preserving non-parametricity and computational complexity. The ICGO was evaluated using 126 benchmark functions and 5 engineering problems, comparing its performance with well-known algorithms. It achieved the best results across all functions except for 2 benchmark functions. The introduced classifier is applied to seven malware datasets and consistently outperforms notable networks like AlexNet, ResNet-18, GoogleNet, and Efficient Capsule neural network in 35 separate runs, achieving over 96% accuracy. Additionally, the classifier’s performance is tested on the MNIST and Fashion-MNIST in 10 separate runs. The results show that the new classifier excels in accuracy, precision, sensitivity, specificity, and F1-score compared to other recent classifiers. Based on the statistical analysis, it has been concluded that the ICGO and propose method exhibit significant superiority compared to the examined algorithms and methods. The source code for ICGO is available publicly at https://nimakhodadadi.com/algorithms-%2B-codes. Graphical abstract

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IMCLNet: A lightweight deep neural network for Image-based Malware Classification

Cited by 8 publications

References 49 publications

RFSA: A Ransomware Feature Selection Algorithm for Multivariate Analysis of Malware Behavior in Cryptocurrency

RFSA: A Ransomware Feature Selection Algorithm for Multivariate Analysis of Malware Behavior in Cryptocurrency

Design and research of Chinese word segmentation method in architecture field

Novel hybrid classifier based on fuzzy type-III decision maker and ensemble deep learning model and improved chaos game optimization

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