Survey of Machine Learning Techniques for Malware Analysis (1710.08189v3)

Abstract: Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies to keep pace with malware evolution. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis in Windows environments, i.e. for the analysis of Portable Executables. We systematize surveyed papers according to their objectives (i.e., the expected output), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of issues and challenges, including those concerning the used datasets, and identify the main current topical trends and how to possibly advance them. In particular, we introduce the novel concept of malware analysis economics, regarding the study of existing trade-offs among key metrics, such as analysis accuracy and economical costs.

• The paper introduces a taxonomy categorizing ML-based approaches for malware detection, similarity analysis, and category assignment.
• It highlights challenges such as anti-analysis techniques, limited feature sets, and dataset quality issues that hinder current research.
• The survey explores emerging trends like malware analysis economics and calls for dynamic, reproducible datasets to advance the field.

Overview of Machine Learning Techniques for Malware Analysis

The paper under review, "Survey of Machine Learning Techniques for Malware Analysis," offers a comprehensive examination of the application of ML methodologies to analyze and identify malware, especially targeting Windows Portable Executables (PEs). The authors present a meticulous survey and categorize existing literature based on the analysis objectives, types of features analyzed, and the machine learning algorithms employed. This paper is crucial as it elucidates previous research efforts, identifies gaps, and points to potential future directions in malware analysis using machine learning.

Key Aspects of the Survey

Taxonomy and Characterization

The authors propose a taxonomy that categorizes the surveyed studies based on three main dimensions:

1. Analysis Objectives: The objectives are granularly divided into malware detection, similarity analysis, and category detection. Studies targeting malware detection aim to discern whether a sample is malicious. Similarity analysis looks for resemblances in malware to recognize variants or detect family affiliations, leveraging features such as API calls and byte sequences. Category detection assigns malware to predefined behavioral categories, providing a general understanding of malware actions.
2. Features: Features are detailed into byte sequences, opcodes, system/API calls, network activity, file system interactions, CPU registers, PE file characteristics, and others. These features can be extracted via static or dynamic analysis or a hybrid approach, enhancing the informative aspect required to model complex malware behaviors effectively.
3. Machine Learning Algorithms: The paper reviews various machine learning methods, extending from classical supervised and unsupervised learning approaches like SVM, Decision Trees, and Clustering to ascending trends in semi-supervised applications. The paper thus paints a detailed picture of the algorithm's landscape in the context of malware analysis.

Limitations and Challenges

Despite the advancements highlighted, the paper brings to the forefront several pressing challenges:

• Anti-analysis Techniques: Malware continues to evolve with sophisticated anti-analysis techniques. Encryption, virtual machine detection, and methods obfuscating malware behavior pose considerable hurdles. The survey suggests that tackling these advancements requires a deeper integration of symbolic execution and reinforced learning mechanisms.
• Operation Set Completeness: An issue recognized by the authors is the incomplete operation set used in modeling malware behaviors. Effective feature selection must expand beyond generic operation detection to a more nuanced understanding of functional behaviors within binaries.
• Dataset Scarcity and Quality: The authors emphasize a critical challenge wherein datasets used across studies lack size, contemporary relevance, and a shared benchmark. An appropriate solution would be dynamic and publicly maintained datasets that mirror real-world distributions and evolutions. This point is crucial for reproducibility and cross-paper comparability in malware analyses, something which literature currently lacks on a significant scale.

Topical Trends and Malware Analysis Economics

The paper sheds light on emerging topical trends, notably the trend of attributing malware to specific threat actors and prioritizing incident responses through effective malware triage. Another innovative idea presented is the concept of "malware analysis economics," which studies the cost-efficiency trade-offs concerning analysis precision, timeliness, and economic resources.

Conclusion and Future Directions

The survey advocates for further exploration into advanced feature extraction, manipulation, and more potent integration of ML applications meeting complex malware signature challenges. It invites the research community to address identified gaps, particularly focusing on machine learning approaches better equipped to handle obfuscation, phenomena prediction, and malicious variants' long-term evolution. The novel introduction of malware analysis economics as a field promises a systematic approach in resource allocation for effective security measure implementation. Hence, this paper acts as both a reflective document of existing works and a proactive call for future refinements and initiatives within the field of cyber threat intelligence and automation.