Pengembangan Intrusion Detection System Terhadap SQL Injection Menggunakan Metode Learning Vector Quantization - Alex Sandro Irawan; Eko Sakti Pramukantoro; Ari Kusyanti (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Database is a collection of systematic data stored in a computer. Database becomes popular in its implementation on the network because the user can access data on the database without need to store data on each user's computer. But the impact of ease of access, the database is also likely to be exposed to external threats by irresponsible people. One such threat is injection. SQL injection is one of the suspicious actions that exploit vulnerabilities in the SQL database. The most commonly used detection method today is to apply rules that aimed at preventing actions that harm a SQL database. However, these rules are not always effective, especially for new SQL injection methods. For that we need a system that can adapt to the new types of attacks based on existing attack data. Intrusion detection system that can adapt to new types of SQL injection attacks. The author implements an intrusion detection system using learning vector quantization method and tests it on a local network. The test results show an accuracy of 80%.