# SoK: Applying Machine Learning in Security - A Survey

**Authors:** Heju Jiang, Jasvir Nagra, Parvez Ahammad  
**arXiv:** [1611.03186](https://arxiv.org/abs/1611.03186)  
**Date:** 2016-11-10  
**Summary page:** https://www.summarizepaper.com/en/paper/1611.03186v1/

## Key points

- ML for security purposes has been around for nearly three decades
- Increasing availability of data and growing ubiquity of information and communications lead to a rise in security risks
- Research on applying ML algorithms and systems to address security concerns has rapidly expanded
- Areas of research include intrusion detection systems (IDS), malware classification, security policy management (SPM), and information leak checking
- The authors systematically study academic publications from 2008 to 2015 that applied ML techniques in security domains
- They develop a taxonomy categorizing ML paradigms based on their application to different security domains
- They outline open challenges and upcoming areas of research within the field
- They suggest treating security as a game theory problem rather than relying solely on batch-trained ML approaches
- The paper provides valuable insights into the application of ML in security domains

## Summary

In the paper titled "SoK: Applying Machine Learning in Security - A Survey," authors Heju Jiang, Jasvir Nagra, and Parvez Ahammad explore the application of machine learning (ML) in solving security problems. They highlight that the concept of using ML for security purposes has been around for nearly three decades. With the increasing availability of data and the growing ubiquity of information and communications, there is a corresponding rise in security risks. This has led to a greater interest in managing and mitigating these risks. The authors note that research on applying ML algorithms and systems to address security concerns has rapidly expanded. This research encompasses various areas such as intrusion detection systems (IDS), malware classification, security policy management (SPM), and information leak checking. To gain insights into this field, the authors systematically study academic publications from 2008 to 2015 that applied ML techniques in security domains. Their analysis focuses on papers presented at highly ranked academic security conferences, as well as one conference known for pioneering ML applications in security. By examining the methods, algorithms, system designs, underlying assumptions, measurements, and use cases presented in these publications, the authors aim to provide a comprehensive understanding of current research trends. Based on their survey findings, the authors propose two key outcomes. Firstly, they develop a taxonomy that categorizes ML paradigms based on their application to different security domains. This taxonomy serves as a foundation for future exploration and exploitation of ML techniques in security contexts. Secondly, they outline an agenda that highlights open challenges and upcoming areas of research within this field. The identified challenges can guide researchers towards addressing critical issues related to applying ML in security effectively. Additionally, the authors suggest a novel perspective on treating security as a game theory problem rather than solely relying on batch-trained ML approaches. This viewpoint opens up new avenues for exploring innovative solutions by leveraging concepts from game theory. Overall, this paper provides valuable insights into the application of ML in security domains. It offers a comprehensive analysis of existing research literature; presents a taxonomy for future exploration; outlines key challenges and opportunities for further investigation; and suggests an alternative approach based on game theory principles which could lead to more effective solutions when applied to certain scenarios involving machine learning models used for cybersecurity purposes.