Skripsi

DETEKSI SERANGAN SQL INJECTION MENGGUNAKAN DEEP NEURAL NETWORKS

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Penilaian

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SQL Injection has been classified by OWASP (Open Web Application Security Project) as one of the most damaging attacks in the past 15 years. This research aims to develop an IDS (Intrusion Detection System) capable of detecting SQL Injection attacks through HTTP POST Requests. The study employs a neural network-based computational model known as DNN (Deep Neural Networks), using tabular data that undergoes pre-processing before being utilized to build the DNN model. This data consists of statements labeled to indicate whether they are SQL Injection attacks or not. Subsequently, the model will be configured and trained using different parameters. The best-performing model will be integrated with a packet capturer, forming an IDS that will be tested against real attacks. The research findings indicate that the best-performing model, configured with the parameters ngram_range (1, 2), min_df 4, max_df 0.8, and trained for 5 epochs, achieves an accuracy of 96.0% on test data and a loss of 20.0%. Furthermore, testing the integrated IDS with the best model against real attacks showed a confusion matrix with values of 2935 (True Negatives), 841 (True Positives), 137 (False Negatives), and 286 (False Positives).

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003981	T149261	T1492612024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1492612024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer., 2024

Collation

xv, VI-2 hlm.; tab.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

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Subject(s)

Sistem Pakar
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

UIN Farrah

Other version/related

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