Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification (1511.04317v2)

Abstract: Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Categorization of malware samples on the basis of their behaviors is essential for the computer security community, because they receive huge number of malware everyday, and the signature extraction process is usually based on malicious parts characterizing malware families. Microsoft released a malware classification challenge in 2015 with a huge dataset of near 0.5 terabytes of data, containing more than 20K malware samples. The analysis of this dataset inspired the development of a novel paradigm that is effective in categorizing malware variants into their actual family groups. This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples. Features can be grouped according to different characteristics of malware behavior, and their fusion is performed according to a per-class weighting paradigm. The proposed method achieved a very high accuracy ($\approx$ 0.998) on the Microsoft Malware Challenge dataset.

• The paper develops a comprehensive feature extraction approach by fusing hex dump and disassembled file analysis to tackle malware obfuscation challenges.
• The paper achieves 99.77% training accuracy through an ensemble XGBoost model with forward stepwise feature fusion, validating its robust performance.
• The paper demonstrates a streamlined, efficient malware classification pipeline that minimizes manual intervention and improves detection turnaround.

An Evaluation of Novel Feature Extraction, Selection, and Fusion for Malware Family Classification

This paper presents a comprehensive methodology for malware classification, addressing significant challenges posed by the evolving landscape of malware variants due to polymorphism and metamorphism. The authors aim to improve the categorization of malware by advancing feature selection and fusion techniques, a pressing necessity given the enormous quantities of novel malware emerging daily. The methodology is rigorously evaluated using the Microsoft Malware Classification Challenge dataset, achieving a remarkably high accuracy rate.

Analysis of Feature Extraction and Fusion Techniques

The paper leverages a dataset released by Microsoft, encompassing 20,000+ malware samples spread over nine distinct families. The authors propose a multi-faceted approach to feature extraction, harnessing both content-based and structural features of executables. Specifically, the paper emphasizes the challenge associated with the stripped PE header, necessitating innovation in feature derivation.

The extracted features include:

• Hex Dump-based Features: N-grams, metadata, entropy, image representation, and string length are derived directly from the hexadecimal representation of the malware. The entropy measurements, in particular, serve as detectors for obfuscation, providing insight into the disorder within the bytecode.
• Disassembled Files Analysis: Features such as OPC (operation codes), and SYM (symbols) are mined from the disassembled code. The operational semantics captured here are vital for clustering similar malware instances, especially when facing obfuscation techniques that aim to disrupt static analysis.

The paper employs an ensemble classification model founded upon XGBoost, incorporating bagging and forward stepwise feature fusion. This model selection is informed by the capability of XGBoost to handle large feature sets with gradient-boosting algorithms, which are notably effective in improving prediction margins through ensemble learning.

Results and Numerical Evaluation

The research claims 99.77% accuracy on the training dataset, underscoring the efficacy of its approach in real-world situations. This result is obtained by meticulously fine-tuning the parameters of XGBoost and implementing a strategic combination of features. Through cross-validation, the robustness of this setup against overfitting is further validated, with consistent performance across varied data splits.

The computational efficiency of this pipeline is particularly notable, factoring in the rapid detection needs of anti-malware companies. The necessity for fast turnaround times in industry settings makes the timing measurements for feature extraction particularly relevant. The hex dump feature extraction and processing detail provide benchmarks for implementation in resource-constrained environments.

Implications and Future Research Directions

The tool's efficiency in accurately assigning malware to families without requiring unpacking or deobfuscation processes presents substantial implications for computer security applications. It simplifies the pipeline in scenarios where the extraction of malware signatures is automated, thus reducing manual intervention.

Future exploration could extend into adapting the approach for zero-day detection, which remains a critical gap in current malware detection frameworks. Moreover, the robustness of classification systems against adversarial attacks—for example, via evasion or poisoning—warrants further scrutiny. Advancements in these areas have the potential to enhance both the resilience and adaptability of malware classification models.

In conclusion, this research contributes significantly to the field of malware analysis by marrying computational efficiency with high classification accuracy, addressing a key challenge faced by industry professionals in malware management and identification. The innovative feature set proposed holds promise for ongoing developments in static and dynamic analysis of complex executable threats.