Biblio

Linux is one of the operating systems to support the increasingly rapid development of internet technology. Apart from the speed of the process, security also needs to be considered. Center for Internet Security (CIS) Benchmark is an example of a security standard. This study implements the CIS Benchmark using the Chef Inspec application. This research focuses on building a tool to perform security audits on the Ubuntu 20.04 operating system. 232 controls on CIS Benchmark were successfully implemented using Chef Inspec application. The results of this study were 87 controls succeeded, 118 controls failed, and 27 controls were skipped. This research is expected to be a reference for information system managers in managing system security.

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%.