Visible to the public Anomaly Based Detection Analysis for Intrusion Detection System Using Big Data Technique with Learning Vector Quantization (LVQ) and Principal Component Analysis (PCA)

TitleAnomaly Based Detection Analysis for Intrusion Detection System Using Big Data Technique with Learning Vector Quantization (LVQ) and Principal Component Analysis (PCA)
Publication TypeConference Paper
Year of Publication2018
AuthorsSalman, Muhammad, Husna, Diyanatul, Apriliani, Stella Gabriella, Pinem, Josua Geovani
Conference NameProceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality
PublisherACM
ISBN Number978-1-4503-6641-0
KeywordsBig Data, IDS, Learning Vector Quantization, Metrics, Network security, Principal Component Analysis (key words), privacy, pubcrawl, threat vectors
Abstract

Data security has become a very serious parf of any organizational information system. More and more threats across the Internet has evolved and capable to deceive firewall as well as antivirus software. In addition, the number of attacks become larger and become more dificult to be processed by the firewall or antivirus software. To improve the security of the system is usually done by adding Intrusion Detection System(IDS), which divided into anomaly-based detection and signature-based detection. In this research to process a huge amount of data, Big Data technique is used. Anomaly-based detection is proposed using Learning Vector Quantization Algorithm to detect the attacks. Learning Vector Quantization is a neural network technique that learn the input itself and then give the appropriate output according to the input. Modifications were made to improve test accuracy by varying the test parameters that present in LVQ. Varying the learning rate, epoch and k-fold cross validation resulted in a more efficient output. The output is obtained by calculating the value of information retrieval from the confusion matrix table from each attack classes. Principal Component Analysis technique is used along with Learning Vector Quantization to improve system performance by reducing the data dimensionality. By using 18-Principal Component, dataset successfully reduced by 47.3%, with the best Recognition Rate of 96.52% and time efficiency improvement up to 43.16%.

URLhttps://dl.acm.org/doi/10.1145/3293663.3293683
DOI10.1145/3293663.3293683
Citation Keysalman_anomaly_2018