Searching for optimal machine learning algorithm for network traffic classification in intrusion detection system

Abstract

The main problem associated with the development of an effective network behaviour anomaly detection-based IDS model is the selection of the optimal network traffic classification method. This article presents the results of simulation research on the effectiveness of the use of machine learning algorithms in the network attacks detection. The research part of the work concerned finding the optimal method of network packets classification possible to implement in the intrusion detection system’s attack detection module. During the research, the performance of three machine learning algorithms (Artificial Neural Network, Support Vector Machine and Naïve Bayes Classifier) has been compared using a dataset from the KDD Cup competition. Attention was also paid to the relationship between the values of algorithm parameters and their effectiveness. The work also contains an short analysis of the state of cybersecurity in Poland.

IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

“…Various papers are available regarding the applicability of machine learning and Artificial Neural Network algorithms for intrusion detection in IoT ecosystems [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], but research in this direction is still in its infancy and requires further study and improvements. Next, we briefly present and discuss the most relevant of these papers for our work.…”