all InfoSec news
CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls
May 7, 2024, 4:11 a.m. | Ahmed Bensaoud, Jugal Kalita
cs.CR updates on arXiv.org arxiv.org
Abstract: In this paper, we propose a novel model for a malware classification system based on Application Programming Interface (API) calls and opcodes, to improve classification accuracy. This system uses a novel design of combined Convolutional Neural Network and Long Short-Term Memory. We extract opcode sequences and API Calls from Windows malware samples for classification. We transform these features into N-grams (N = 2, 3, and 10)-gram sequences. Our experiments on a dataset of 9,749,57 samples …
accuracy api application application programming interface arxiv classification cnn cs.ai cs.cr cs.lg design interface malware malware classification memory network neural network novel programming system transfer
More from arxiv.org / cs.CR updates on arXiv.org
A Privacy Preserving System for Movie Recommendations Using Federated Learning
2 days, 20 hours ago |
arxiv.org
Jobs in InfoSec / Cybersecurity
Information Security Engineers
@ D. E. Shaw Research | New York City
Technology Security Analyst
@ Halton Region | Oakville, Ontario, Canada
Senior Cyber Security Analyst
@ Valley Water | San Jose, CA
Computer and Forensics Investigator
@ ManTech | 221BQ - Cstmr Site,Springfield,VA
Senior Security Analyst
@ Oracle | United States
Associate Vulnerability Management Specialist
@ Diebold Nixdorf | Hyderabad, Telangana, India