all InfoSec news
Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection
March 5, 2024, 3:11 p.m. | Zhenglin Li, Haibei Zhu, Houze Liu, Jintong Song, Qishuo Cheng
cs.CR updates on arXiv.org arxiv.org
Abstract: 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 …
aim api arxiv capabilities classification cs.ai cs.cr cs.lg cybersecurity dataset detection effectively evaluation machine machine learning malware malware detection mitigating threats non study techniques threats
More from arxiv.org / cs.CR updates on arXiv.org
IDEA: Invariant Defense for Graph Adversarial Robustness
2 days, 14 hours ago |
arxiv.org
FairCMS: Cloud Media Sharing with Fair Copyright Protection
2 days, 14 hours ago |
arxiv.org
Efficient unitary designs and pseudorandom unitaries from permutations
2 days, 14 hours ago |
arxiv.org
Jobs in InfoSec / Cybersecurity
SOC 2 Manager, Audit and Certification
@ Deloitte | US and CA Multiple Locations
Lead Technical Product Manager - Threat Protection
@ Mastercard | Remote - United Kingdom
Data Privacy Officer
@ Banco Popular | San Juan, PR
GRC Security Program Manager
@ Meta | Bellevue, WA | Menlo Park, CA | Washington, DC | New York City
Cyber Security Engineer
@ ASSYSTEM | Warrington, United Kingdom
Privacy Engineer, Technical Audit
@ Meta | Menlo Park, CA