Research Article
Malicious Codes Detection: Deep Learning Techniques
@INPROCEEDINGS{10.1007/978-3-031-35078-8_16, author={Jasleen Gill and Rajesh Dhakad}, title={Malicious Codes Detection: Deep Learning Techniques}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={Malicious Codes Support Vector Machine(SVM) Random Forest Autoencoder Convolutional Neural Network(CNN) Machine Learning Deep Learning}, doi={10.1007/978-3-031-35078-8_16} }- Jasleen Gill
Rajesh Dhakad
Year: 2023
Malicious Codes Detection: Deep Learning Techniques
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_16
Abstract
The rapid growth of internet has led to various security threats. Today, malicious codes has emerged as one of the most serious threats to internet security. Millions of new malicious codes are created every single day. However, with increasing malicious codes, the detection of malicious codes variants with better performance have become a great challenge. In this work, we address the detection of malicious codes using deep as well as machine learning techniques. We proposed a malicious codes detection model with different approaches based on an autoencoder. The paper also, compares different approaches based on their performance. One is an autoencoder based model and the other is PCA based model. After comparing the two models, the experimental results shows that the autoencoder based model have an higher accuracy than the PCA based model. Therefore, the proposed model has better detection performance
