# Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

arXiv: https://arxiv.org/abs/2403.02232 · HTML: https://mlsanity.com/paper/2403.02232/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2402.10350/) (2024)
- [A Study of the Attention Abnormality in Trojaned BERTs](https://mlsanity.com/paper/2205.08305/) (2022)
- [Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms](https://mlsanity.com/paper/2312.12872/) (2023)
- [BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning](https://mlsanity.com/paper/2304.03144/) (2023)
- [An Explainer for Temporal Graph Neural Networks](https://mlsanity.com/paper/2209.00807/) (2022)
- [BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns](https://mlsanity.com/paper/2303.10214/) (2023)
- [LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection](https://mlsanity.com/paper/2401.16001/) (2024)
- [Attention Hijacking in Trojan Transformers](https://mlsanity.com/paper/2208.04946/) (2022)
- [MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression](https://mlsanity.com/paper/2305.11742/) (2023)
- [Deviance Matrix Factorization](https://mlsanity.com/paper/2110.05674/) (2021)
- [Understand Legal Documents with Contextualized Large Language Models](https://mlsanity.com/paper/2303.12135/) (2023)
- [Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data](https://mlsanity.com/paper/2402.09830/) (2024)
- [Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks](https://mlsanity.com/paper/2106.14344/) (2021)
- [A Benchmark API Call Dataset for Windows PE Malware Classification](https://mlsanity.com/paper/1905.01999/) (2019)
- [Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence](https://mlsanity.com/paper/2402.07915/) (2024)
- [Robust Data Preprocessing for Machine-Learning-Based Disk Failure Prediction in Cloud Production Environments](https://mlsanity.com/paper/1912.09722/) (2019)

## Built on by (most cited first, 8 of 8)

- [Task-Agnostic Detector for Insertion-Based Backdoor Attacks](https://mlsanity.com/paper/2403.17155/) (2024)
- [Image Captioning in news report scenario](https://mlsanity.com/paper/2403.16209/) (2024)
- [A survey on fairness of large language models in e-commerce: progress, application, and challenge](https://mlsanity.com/paper/2405.13025/) (2024)
- [Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis](https://mlsanity.com/paper/2403.16212/) (2024)
- [Machine learning-based system reliability analysis with Gaussian Process Regression](https://mlsanity.com/paper/2403.11125/) (2024)
- [A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes](https://mlsanity.com/paper/2407.08839/) (2024)
- [Advanced Feature Manipulation for Enhanced Change Detection Leveraging Natural Language Models](https://mlsanity.com/paper/2403.15943/) (2024)
- [StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requests](https://mlsanity.com/paper/2407.18148/) (2024)

## Abstract (arXiv)

This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset. The aim is to advance cybersecurity capabilities by identifying and mitigating threats more effectively. Both ensemble and non-ensemble machine learning methods, such as Random Forest, XGBoost, K Nearest Neighbor (KNN), and Neural Networks, are explored. Special emphasis is placed on the importance of data pre-processing techniques, particularly TF-IDF representation and Principal Component Analysis, in improving model performance. Results indicate that ensemble methods, particularly Random Forest and XGBoost, exhibit superior accuracy, precision, and recall compared to others, highlighting their effectiveness in malware detection. The paper also discusses limitations and potential future directions, emphasizing the need for continuous adaptation to address the evolving nature of malware. This research contributes to ongoing discussions in cybersecurity and provides practical insights for developing more robust malware detection systems in the digital era.