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http://dx.doi.org/10.33778/kcsa.2020.20.4.081

Improvement of Attack Traffic Classification Performance of Intrusion Detection Model Using the Characteristics of Softmax Function  

Kim, Young-won (국방대학교 국방과학학과)
Lee, Soo-jin (국방대학교 국방과학학과)
Publication Information
Convergence Security Journal / v.20, no.4, 2020 , pp. 81-90 More about this Journal
Abstract
In the real world, new types of attacks or variants are constantly emerging, but attack traffic classification models developed through artificial neural networks and supervised learning do not properly detect new types of attacks that have not been trained. Most of the previous studies overlooked this problem and focused only on improving the structure of their artificial neural networks. As a result, a number of new attacks were frequently classified as normal traffic, and attack traffic classification performance was severly degraded. On the other hand, the softmax function, which outputs the probability that each class is correctly classified in the multi-class classification as a result, also has a significant impact on the classification performance because it fails to calculate the softmax score properly for a new type of attack traffic that has not been trained. In this paper, based on this characteristic of softmax function, we propose an efficient method to improve the classification performance against new types of attacks by classifying traffic with a probability below a certain level as attacks, and demonstrate the efficiency of our approach through experiments.
Keywords
IDS; AI; ML; DL; Activation Function; Softmax; Multiclass classification;
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