一個有效的深度學習超參數選擇方法應用於入侵偵測系統

An Effective Hyperparameter Selection for Deep Learning Algorithm in Intrusion Detection System

張維晏(Wei-Yan Chang) ； 陳羿霖(Yi-Lin Chen) ； 陳煥(Huang Chen) ； 蔡崇煒(Chun-Wei Tsai)

《資訊安全通訊》 26卷4期 (2020/12) Pp. 1-16

摘要

許多近期的研究利用機器學習或深度學習方法，作為入侵偵測系統的判別模組方法。部分研究主要注重於如何改良深度學習架構，來提升深度學習的準確率。其中一個影響深度學習的效能的原因是超參數的設定，包含了神經元的個數、層數或學習率等。然而目前超參數的設定，通常是依據個人的經驗或窮舉法方式來設定超參數的數值。在本論文中將差分進化演算法與深度學習結合，提出了一種可以自適應調整超參數的方法，並將其應用於入侵偵測系統中。本論文將所提出的方法與其他機器學習和深度學習進行比較。最終實驗結果說明，本論文所提的超啟發式演算法，在較複雜的入侵偵測問題上，可以有效的找到深度學習的最佳超參數，能有效提升深度學習模型對於入侵攻擊的檢測能力。

關鍵字

入侵偵測系統 ； 深度學習 ； 超啟發式演算法 ； 超參數

並列摘要

With the great success of artificial intelligence, machine learning and deep learning have been applied to intrusion detection as the core methods in recent years. The primary focus of many researches is on how to improve the performance by tuning the deep learning model and the associated hyperparameters. Hyperparameters are critical factors for deep learning-based method which includes how many layers should be used in the model; how many neurons are there for each layer; and how to set up the learning rate properly. However, most of studies set the hyperparameters depending on personal experience or using inefficient brute force exhaustive search. In this paper, we will present a self-adaptive method to decide the setting of hyperparameters that integrated deep learning and differential evolution. The proposed scheme is then be used for intrusion detection system. To evaluate the effectiveness of the proposed method, the performance of the scheme is compared to those of other machine learning and deep learning methods. Three performance metrics in terms of accuracy, recall and precision are used. Results show that the proposed algorithm can find the best hyperparameters setting for deep learning model than other methods compared in this paper.

並列關鍵字

Intrusion detection system ； deep learning ； metaheuristic algorithm ； hyperparameters

參考文獻

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