Malicious Website Detection Through Deep Learning Algorithms
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE (LOD 2021), PT I(2022)
摘要
Traditional methods that detect malicious websites, such as blacklists, do not update frequently, and they cannot detect new attackers. A system capable of detecting malicious activity using Deep Learning (DL) has been proposed to address this need. Starting from a dataset that contains both malevolent and benign websites, classification is done by extracting, parsing, analysing, and preprocessing the data. Additionally, the study proposes a Feed-Forward Neural Network (FFNN) to classify each sample. We evaluate different combinations of neurons in the model and perform in-depth research of the best performing network. The results show up to 99.88% of detection of malicious websites and 2.61% of false hits in the testing phase (i.e. malicious websites classified as benign), and 1.026% in the validation phase.
更多查看译文
关键词
Network attacks, Deep learning, Feed Forward Neural Network, Preprocessing
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要