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Advanced Temporal-Difference Learning for Intrusion Detection ?

机译：入侵检测的高级时差学习 ？

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摘要

Nowadays intrusion detection for cyber security is the dynamically researched area. The main purpose of the intrusion detection is to distinguish normal usage of analyzed system from different forms of misuses and abnormal behaviors. The big amount of intrusion detection approaches such as soft computing and machine learning algorithms was made. In spite of visible progress, there are still many opportunities to improve state-of-the-art techniques. This paper presents the new intrusion detection technique that is based on temporal-difference learning for Markov decision processes. Actually, this method is the advanced form of the existing temporal- difference based approach named Temporal-Difference based Sequence Anomaly Detection (or TD_SAD). Due to this fact, our approach is called TD_SAD2. It is shown that the proposed approach can achieve at least comparable accuracy for intrusion detection by benchmarking with existing leading approaches.
机译：如今，针对网络安全的入侵检测已成为动态研究的领域。入侵检测的主要目的是将分析系统的正常使用与不同形式的滥用和异常行为区分开。大量的入侵检测方法，例如软计算和机器学习算法。尽管取得了明显进展，但仍有许多机会可以改进最新技术。本文提出了一种基于时差学习的马尔可夫决策过程新的入侵检测技术。实际上，该方法是现有的基于时差的方法的高级形式，称为基于时间差的序列异常检测（TD_SAD）。由于这个事实，我们的方法称为TD_SAD2。结果表明，通过与现有的领先方法进行基准测试，所提出的方法至少可以达到相当的入侵检测精度。

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