Machine Learning Blunts the Needle of Advanced SQL Injections
| Autor: | Petr Chmelar, Marina Volkova, Lukas Sobotka |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Word embedding
web application firewall General Computer Science Computer science SQL injection identication Intrusion detection system Machine learning computer.software_genre Theoretical Computer Science SQL injection recurrent neural networks Query string Artificial neural network business.industry Deep learning deep learning text analysis QA75.5-76.95 Computational Mathematics Recurrent neural network machine learning Multilayer perceptron Electronic computers. Computer science intrusion detection system Artificial intelligence business computer |
| Zdroj: | Mendel, Vol 25, Iss 1 (2019) Mendel. 2018 vol. 25, č. 1, s. 23-30. ISSN 1803-3814 |
| ISSN: | 2571-3701 1803-3814 |
| Popis: | SQL injection is one of the most popular and serious information security threats. By exploiting database vulnerabilities, attackers may get access to sensitive data or enable compromised computers to conduct further network attacks. Our research is focused on applying machine learning approaches for identication of injection characteristics in the HTTP query string. We compare results from Rule-based Intrusion Detection System, Support Vector Machines, Multilayer Perceptron, Neural Network with Dropout layers, and Deep Sequential Models (Long Short-Term Memory, and Gated Recurrent Units) using multiple string analysis, bag-of-word techniques, and word embedding for query string vectorization. Results proved benets of applying machine learning approach for detection malicious pattern in HTTP query string. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|