Security System Performance Analysis Analytics Based

EasyChair Preprint no. 13165

Abstract

Information is a very important asset in making decisions. Diversity of data is a challenge in itself in network management, monitoring and security. Security analytics is a combination of tools used to identify, protect against, and troubleshoot security events that threaten IT systems using real-time and historical data. In this research, a big data analytical approach was used to process network traffic data. by implementing the Naive Bayes and KNN algorithms, comparing the performances between the two algorithms to produce information with the best level of accuracy. The Naïve Bayes algorithm is an algorithm used for statistical classification which can be used to predict the probability of membership of a class,while the KNN algorithm is a supervised learning algorithm which is used to classify new objects based on nearby objects. The aim is to find out attack patterns on network traffic. In this research, the dataset used is network traffic data. Spark was chosen as the big data analytics framework, with Python programming as the language used. the use of big data analytics in the performance of normal data network security systems or as indicated in the training process and network traffic classification.

Keyphrases: Classification, KNN algorithm, Naive Bayes Algorithm, network traffic, Security System

@Booklet{EasyChair:13165,
  author = {Kristine Wau and Wana Yumini and Dedy Hartama},
  title = {Security System Performance Analysis Analytics Based},
  howpublished = {EasyChair Preprint no. 13165},

  year = {EasyChair, 2024}}