An Opcode-based Detection and Classification of Emerging Malware with Multiclass Supervised Learning

Introduction

The rapid evolution of malware poses significant challenges to traditional signature-based detection methods, necessitating the development of more sophisticated techniques. Opcode-based detection and classification using machine learning, particularly multiclass supervised learning, has emerged as a promising approach to address these challenges.

Key Insights

Effectiveness of Opcode Sequences in Malware Detection:
- Opcode sequences can be used to extract deeper behavioral features, making detection methods more resilient to code obfuscation and novel malware .
- Utilizing n-gram models to analyze opcode sequences enhances the detection and classification accuracy of malware .
Machine Learning and Deep Learning Techniques:
- Various machine learning algorithms, including support vector machines, decision trees, random forests, and k-nearest neighbors, have been successfully applied to opcode-based malware detection, achieving high accuracy rates .
- Deep learning techniques, such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs), have shown improved performance in classifying malware by learning from opcode sequences and API calls .
Feature Extraction and Representation:
- Techniques like Word2Vec and term frequency-inverse document frequency (TF-IDF) are used to represent opcodes and API calls, reducing dimensionality and improving classification performance .
- Automated feature discovery methods eliminate the need for expert-defined features, streamlining the detection process .
Semi-supervised Learning Approaches:
- Semi-supervised learning methods can effectively detect unknown malware by leveraging both labeled and unlabeled data, reducing the need for extensive labeling efforts while maintaining high accuracy.

Conclusion

Opcode-based detection and classification of malware using multiclass supervised learning is a robust approach that addresses the limitations of traditional signature-based methods. By leveraging machine learning and deep learning techniques, these methods can effectively detect and classify emerging malware, even in the presence of code obfuscation. The use of advanced feature extraction and representation techniques further enhances the accuracy and efficiency of these models. Overall, opcode-based methods represent a significant advancement in the field of malware detection and classification.

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An Opcode-based Detection and Classification of Emerging Malware with Multiclass Supervised Learning

Summary

Introduction

Key Insights

Conclusion

Sources and full results

Deep learning-aided runtime opcode-based Windows malware detection

This paper presents an alternative method for malware detection using assembly opcode sequences, which is impervious to code obfuscation and effective against novel malware, with a MCC score as high as 0.95 on a balanced dataset.

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The proposed word2vec-based LSTM method effectively classifies malware opcodes and API function names using fewer dimensions, achieving 0.5% higher performance than one-hot encoding-based approaches.

Opcode-Sequence-Based Semi-supervised Unknown Malware Detection

This paper proposes a new semi-supervised learning approach using opcode sequence frequencies to detect unknown malware, reducing labeling efforts while maintaining high accuracy rates.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

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Opcode and API Based Machine Learning Framework For Malware Classification

Our framework achieves a 99.3% F1-score for malware classification using opcode and API features, outperforming recent state-of-the-art techniques and reducing training time.

Support vector machine integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware

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A Review of Malware Classification Methods using Machine Learning

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Deep Android Malware Detection

Our deep convolutional neural network (CNN)-based Android malware detection system effectively classifies malware from raw opcode sequences, reducing training time and enabling efficient large-scale file scanning.

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