Implementasi Analisis Kinerja Alogaritma Random Forest Dalam Deteksi Anomali Jaringan Pada Cloud Computing

Maulana, Fairuz Azka (2024) Implementasi Analisis Kinerja Alogaritma Random Forest Dalam Deteksi Anomali Jaringan Pada Cloud Computing. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pemanfaatan cloud computing kian meningkat dalam dunia teknologi informasi, memberikan fleksibilitas dan skalabilitas dalam pengelolaan sumber daya. Namun, pengawasan terhadap keamanan menjadi tantangan signifikan, terutama dalam mendeteksi dan merespons serangan jaringan secara efisien. Penelitian ini mengimplementasikan dan mengevaluasi algoritma Random Forest dalam deteksi anomali jaringan pada cloud computing, diuji dengan skenario serangan DDoS dan pengintegrasian dengan platform monitoring Zabbix. Algoritma Random Forest, yang dikenal dengan kemampuannya dalam mengatasi overfitting dan keandalan dalam klasifikasi, dipilih karena dapat menyesuaikan diri dengan variasi data dan mengurangi false positives. Evaluasi meliputi pengujian terhadap log operasional dengan simulasi serangan untuk memverifikasi efektivitasnya dalam lingkungan dinamis cloud. Hasil dari penelitian ini menunjukkan bahwa penggunaan Random Forest meningkatkan respons keamanan dengan deteksi yang lebih akurat dan cepat dengan akurasi 83% tanpa menggunakan hyperparameter dan 87% jika menggunakan hyperparameter, dan menekankan pentingnya integrasi machine learning canggih dalam sistem keamanan jaringan cloud
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The use of cloud computing is increasing in the world of information technology, providing flexibility and scalability in resource management. However, security oversight is a significant challenge, especially in detecting and responding efficiently to network attacks. This research implements and evaluates the Random Forest algorithm in network anomaly detection in cloud computing, tested with DDoS attack scenarios and integration with the Zabbix monitoring platform. The Random Forest algorithm, which is known for its ability to overcome overfitting and reliability in classification, was chosen because it can adapt to data variations and reduce false positives. The evaluation includes testing operational logs with simulated attacks to verify its effectiveness in a dynamic cloud environment. The results of this research show that the use of Random Forest improves security response with more accurate and faster detection with an accuracy of 83% without using hyperparameters and 87% when using hyperparameters, and emphasizes the importance of integrating advanced machine learning in cloud network security systems

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Cloud Computing, DDos, Deteksi Anomali, Random Forest Zabbix, Anomaly Detection, Cloud Computing, DDoS, Random Forest, Zabbix.
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information Technology > 59201-(S1) Undergraduate Thesis
Depositing User:	Fairuz Azka Maulana
Date Deposited:	25 Jul 2024 01:10
Last Modified:	25 Jul 2024 01:10
URI:	http://repository.its.ac.id/id/eprint/108718

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