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

arXiv: https://arxiv.org/abs/2302.05530 · HTML: https://mlsanity.com/paper/2302.05530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning](https://mlsanity.com/paper/2201.08564/) (2022)
- [Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication](https://mlsanity.com/paper/2205.13646/) (2022)
- [Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning](https://mlsanity.com/paper/2201.08565/) (2022)
- [A Close Look into Human Activity Recognition Models using Deep Learning](https://mlsanity.com/paper/2204.13589/) (2022)
- [Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning](https://mlsanity.com/paper/2205.08371/) (2022)
- [Using Deep Learning to Detecting Deepfakes](https://mlsanity.com/paper/2207.13644/) (2022)
- [Continuous User Authentication Using Machine Learning and Multi-Finger Mobile Touch Dynamics with a Novel Dataset](https://mlsanity.com/paper/2207.13648/) (2022)

## Abstract (arXiv)

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.