KLASIFIKASI SERANGAN PORT SCANNING PADA INTRUSION DETECTION SYSTEM MENGGUNAKAN METODE LSTM (LONG SHORT TERM MEMORY)

JUMHADI, JUMHADI and Heryanto, Ahmad (2022) KLASIFIKASI SERANGAN PORT SCANNING PADA INTRUSION DETECTION SYSTEM MENGGUNAKAN METODE LSTM (LONG SHORT TERM MEMORY). Undergraduate thesis, Sriwijaya University.

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Abstract

Port Scanning is an attack that is carried out to identify open ports on a computer network system, open ports are also called listening ports, which are ports whose job is to receive incoming packets and also function to interact with outside networks. Port Scanning is included in the information gathering stage. Port Scanning attacks on computer networks today are still very difficult to detect because the attack pattern of Port Scanning does not establish a full connection to its target destination. This study classifies Port scanning attacks on the Intrusion Detection System using the Long Short Term Memory (LSTM) method, using the Port Scanning dataset on CSE-CIC-IDS2017. In this study, the Principal Component Analysis feature selection was applied to reduce the dimensions and also the efficiency of training time, Hyperparameter Tuning was also applied to see the best parameters to be applied to the research model, Research validation was carried out 5 times in the study. The best validation results from the overall results are 80% training data and 20 testing data where in this study the results obtained were 99.89% accuracy points, 99.88% recall, 99.90% specificity, 99.87% precision and F1 score 99, 88% and efficient training time is only 3 hours because of the use of PCA in the study.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Port Scanning, Principal Component Analysis, Tuning Hyperparameter, LSTM (Long Short Term Memory)
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mr Jumhadi Jumhadi
Date Deposited:	04 Aug 2022 07:08
Last Modified:	04 Aug 2022 07:08
URI:	http://repository.unsri.ac.id/id/eprint/76023

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