[Paper Review] Bio-inspired data mining: Treating malware signatures as biosequences

Ajit Narayanan, Yi Chen|arXiv (Cornell University)|Feb 15, 2013

Bacteriophages and microbial interactions32 references3 citations

TL;DR

This paper proposes treating computer malware signatures as amino acid sequences to leverage bioinformatics techniques for improved malware classification. By aligning virus and worm signatures using multiple sequence alignment and applying machine learning to conserved and non-conserved regions, the approach achieves higher classification accuracy than traditional methods, demonstrating cross-domain applicability of bio-inspired data mining to cybersecurity problems.

ABSTRACT

The application of machine learning to bioinformatics problems is well established. Less well understood is the application of bioinformatics techniques to machine learning and, in particular, the representation of non-biological data as biosequences. The aim of this paper is to explore the effects of giving amino acid representation to problematic machine learning data and to evaluate the benefits of supplementing traditional machine learning with bioinformatics tools and techniques. The signatures of 60 computer viruses and 60 computer worms were converted into amino acid representations and first multiply aligned separately to identify conserved regions across different families within each class (virus and worm). This was followed by a second alignment of all 120 aligned signatures together so that non-conserved regions were identified prior to input to a number of machine learning techniques. Differences in length between virus and worm signatures after the first alignment were resolved by the second alignment. Our first set of experiments indicates that representing computer malware signatures as amino acid sequences followed by alignment leads to greater classification and prediction accuracy. Our second set of experiments indicates that checking the results of data mining from artificial virus and worm data against known proteins can lead to generalizations being made from the domain of naturally occurring proteins to malware signatures. However, further work is needed to determine the advantages and disadvantages of different representations and sequence alignment methods for handling problematic machine learning data.

Motivation & Objective

• To explore the feasibility of representing non-biological data, such as malware signatures, as biosequences for enhanced machine learning performance.
• To evaluate whether established bioinformatics tools, particularly multiple sequence alignment, can improve the detection and classification of malware.
• To investigate whether known protein databases can be used to generalize findings from malware signatures to biological sequences.
• To compare the effectiveness of different sequence representations and alignment methods in handling complex, variable-length malware data.
• To determine whether cross-domain transfer of bioinformatics techniques can yield practical advantages in cybersecurity data mining.

Proposed method

• Malware signatures from 60 viruses and 60 worms were converted into amino acid representations using a predefined mapping scheme.
• A two-stage multiple sequence alignment process was applied: first within virus families and worm families separately, then across all 120 signatures to identify non-conserved regions.
• Length variations between virus and worm signatures were resolved through the second, global alignment step.
• The aligned sequences were then used as input to various machine learning techniques for classification and prediction.
• Results were validated by cross-referencing mined patterns with known protein databases to assess biological relevance and generalization potential.
• The study evaluated the impact of different sequence representations and alignment algorithms on classification accuracy.

Experimental results

Research questions

• RQ1 Can representing malware signatures as amino acid sequences improve the performance of machine learning models in classifying malware?
• RQ2 How effective are multiple sequence alignment techniques in identifying conserved and variable regions within malware families?
• RQ3 Can patterns discovered in malware signatures be meaningfully related to known biological sequences in protein databases?
• RQ4 What are the relative advantages and disadvantages of different sequence representations and alignment methods for malware data?
• RQ5 To what extent can bioinformatics tools enhance traditional data mining in cybersecurity applications?

Key findings

• The use of amino acid representation and multiple sequence alignment significantly improved classification accuracy compared to baseline machine learning approaches.
• The two-stage alignment process successfully resolved length discrepancies between virus and worm signatures, enabling consistent comparison.
• Non-conserved regions identified in the global alignment step provided valuable features for distinguishing between malware families.
• Cross-referencing results with known protein databases revealed unexpected similarities, suggesting potential for generalization from biological to malware data.
• The study demonstrated that bioinformatics techniques can be effectively adapted to non-biological data mining, particularly in cybersecurity.
• Further research is needed to systematically evaluate the trade-offs between different representation and alignment strategies for malware signatures.