| Title | Detection of web attacks using machine learning based URL classification techniques |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Saxena, Aditi, Arora, Akarshi, Saxena, Saumya, Kumar, Ashwni |
| Conference Name | 2022 2nd International Conference on Intelligent Technologies (CONIT) |
| Keywords | Classification algorithms, command injection, command injection attacks, composability, Cross Site Scripting, cyber security, Deep Learning, feature extraction, machine learning, Malicious URL, Metrics, path traversal, phishing, privacy, pubcrawl, resilience, Resiliency, SQL Injection, Systematics, uniform resource locator, Uniform resource locators |
| Abstract | For a long time, online attacks were regarded to pose a severe threat to web - based applications, websites, and clients. It can bypass authentication methods, steal sensitive information from datasets and clients, and also gain ultimate authority of servers. A variety of ways for safeguarding online apps have been developed and used to deal the website risks. Based on the studies about the intersection of cybersecurity and machine learning, countermeasures for identifying typical web assaults have recently been presented (ML). In order to establish a better understanding on this essential topic, it is necessary to study ML methodologies, feature extraction techniques, evaluate datasets, and performance metrics utilised in a systematic manner. In this paper, we go through web security flaws like SQLi, XSS, malicious URLs, phishing attacks, path traversal, and CMDi in detail. We also go through the existing security methods for detecting these threats using machine learning approaches for URL classification. Finally, we discuss potential research opportunities for ML and DL-based techniques in this category, based on a thorough examination of existing solutions in the literature. |
| DOI | 10.1109/CONIT55038.2022.9847838 |
| Citation Key | saxena_detection_2022 |
Detection of web attacks using machine learning based URL classification techniques