Skripsi

INTRUSION DETECTION SYSTEM MENGGUNAKAN AUTOENCODER UNTUK MONITORING JARINGAN REALTIME PADA ARSITEKTUR MICROSERVICES

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Intrusion Detection System (IDS) plays a critical role in maintaining network security by identifying abnormal or potentially malicious activities. However, the increasing complexity and volume of network traffic in distributed environments pose challenges for conventional detection systems in terms of accuracy and real-time capability. This study proposes an unsupervised learning-based IDS using an autoencoder to automatically detect network anomalies without relying on labeled data. The model is developed and evaluated using the CICIDS2017 dataset, which represents a wide range of modern network attacks. To enable real-time monitoring, the system is integrated with Apache Kafka as a message broker and Elasticsearch-Kibana for data visualization and analysis. Evaluation metrics include reconstruction error, detection accuracy, false positive rate, and processing efficiency in a distributed setting. Experimental results demonstrate that the autoencoder-based approach can effectively detect anomalies with high accuracy and maintain stable performance in distributed architectures, highlighting its potential to enhance the reliability of modern IDS for large-scale network environments.

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Detail Information

Series Title

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Call Number

T1897242025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xii, 140 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.820 7

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Intrusion Detection System
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MI

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