| Title | Detection of Malicious Webpages Using Deep Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Singh, A K, Goyal, Navneet |
| Conference Name | 2021 IEEE International Conference on Big Data (Big Data) |
| Date Published | dec |
| Keywords | Big Data, big data security metrics, Deep Learning, Internet, Malicious Webpages, Measurement, Neural networks, pubcrawl, Real-time Systems, resilience, Resiliency, Scalability, Training, web security |
| Abstract | Malicious Webpages have been a serious threat on Internet for the past few years. As per the latest Google Transparency reports, they continue to be top ranked amongst online threats. Various techniques have been used till date to identify malicious sites, to include, Static Heuristics, Honey Clients, Machine Learning, etc. Recently, with the rapid rise of Deep Learning, an interest has aroused to explore Deep Learning techniques for detecting Malicious Webpages. In this paper Deep Learning has been utilized for such classification. The model proposed in this research has used a Deep Neural Network (DNN) with two hidden layers to distinguish between Malicious and Benign Webpages. This DNN model gave high accuracy of 99.81% with very low False Positives (FP) and False Negatives (FN), and with near real-time response on test sample. The model outperformed earlier machine learning solutions in accuracy, precision, recall and time performance metrics. |
| DOI | 10.1109/BigData52589.2021.9671622 |
| Citation Key | singh_detection_2021 |
Detection of Malicious Webpages Using Deep Learning