Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications

Nicholas Lasky; Benjamin Hallis; Mounika Vanamala; Rushit Dave; Jim Seliya

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arXiv · 2302.05530

Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications

Nicholas Lasky·Benjamin Hallis·Mounika Vanamala·Rushit Dave·Jim Seliya

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Abstract

Engineering more secure software has become a critical challenge in the cyber world. It is very important to develop methodologies, techniques, and tools for developing secure software. To develop secure software, software developers need to think like an attacker through mining software repositories. These aim to analyze and understand the data repositories related to software development. The main goal is to use these software repositories to support the decision-making process of software development. There are different vulnerability databases like Common Weakness Enumeration (CWE), Common Vulnerabilities and Exposures database (CVE), and CAPEC. We utilized a database called MITRE. MITRE ATT&CK tactics and techniques have been used in various ways and methods, but tools for utilizing these tactics and techniques in the early stages of the software development life cycle (SDLC) are lacking. In this paper, we use machine learning algorithms to map requirements to the MITRE ATT&CK database and determine the accuracy of each mapping depending on the data split.

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Nicholas Lasky, Benjamin Hallis, Mounika Vanamala, Rushit Dave, Jim Seliya. 2023-02-10. Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications. https://arxiv.org/abs/2302.05530

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