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

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

Introduction to Opcode-Based Malware Detection

Malware detection has traditionally relied on signature-based methods, which are effective for known threats but often fail against novel or obfuscated malware. To address these limitations, researchers have explored opcode-based detection techniques, leveraging the sequences of operation codes (opcodes) from executable files to identify malicious behavior. This article synthesizes recent research on opcode-based malware detection and classification using multiclass supervised learning.

Opcode Feature Extraction and Selection

Opcode Extraction Techniques

Opcode extraction is a critical step in creating a feature set for malware detection. Various methods have been proposed to extract and utilize opcode sequences. For instance, one study introduced the Opcode Extract and Count (OPEC) algorithm to prepare opcode feature vectors, which were then used to train multiple supervised learning models, achieving a detection accuracy of 98.7%. Another approach utilized assembly opcode sequences obtained during runtime, applying natural language processing and deep learning techniques to extract deeper behavioral features.

Feature Selection Methods

Feature selection is essential to enhance the performance of machine learning models. The Extra Tree Classifier has been employed to select the most relevant opcode features, which are then fed into various classifiers such as support vector machines, naive Bayes, decision trees, random forests, logistic regression, and k-nearest neighbors. This process helps in reducing the dimensionality of the feature space and improving the model's accuracy.

Machine Learning Models for Malware Detection

Supervised Learning Algorithms

Several supervised learning algorithms have been evaluated for opcode-based malware detection. These include support vector machines, naive Bayes, decision trees, random forests, logistic regression, and k-nearest neighbors. These models have shown high accuracy in detecting malware, with some studies reporting detection rates as high as 98.7% . Additionally, deep learning models such as convolutional recurrent neural networks have been proposed, achieving a detection accuracy of 96% and a true positive rate of 95%.

Deep Learning Approaches

Deep learning techniques have also been explored for opcode-based malware detection. For example, a deep learning model combining temporal convolutional networks (TCN) and bidirectional gated recurrent units (BiGRU) was proposed to capture opcode sequences in both directions, achieving an overall performance of 99.72% for multiclass classification. Another study utilized a self-attention-based convolutional neural network (SA-CNN) to handle extremely long opcode sequences, demonstrating superior performance in ransomware classification.

Classification of Malware Families

Multiclass Classification

Multiclass classification of malware involves categorizing malware samples into different families. One study extended the MalConv neural network architecture to perform multiclass classification, showing that it performs equally well on raw byte sequences and opcode sequences. Another approach used a shared nearest neighbor (SNN) clustering algorithm to discover new malware families, achieving a classification accuracy of 98.9%.

IoT and Android Malware

Opcode-based techniques have also been applied to specific domains such as IoT and Android malware. For IoT malware, features were created using opcode categories and entropy values, achieving an accuracy of over 98% in detecting and classifying different types of IoT malware. Similarly, an n-opcode analysis approach was used to classify and categorize Android malware, achieving an f-measure of 98%.

Conclusion

Opcode-based detection and classification of malware using multiclass supervised learning have shown promising results in recent research. By leveraging opcode sequences and advanced machine learning techniques, these methods offer robust solutions for identifying both known and novel malware. The integration of feature selection methods and deep learning models further enhances the accuracy and reliability of these systems, making them effective tools in the ongoing battle against emerging malware threats.