Background

The ever increasing sophistication of intrusion approaches has led to the dire necessity for developing Intrusion Detection Systems with optimal efficacy. However, existing Intrusion Detection Systems have been developed using outdated attack datasets, with more focus on prediction accuracy and less on prediction latency. The smart Intrusion Detection System framework evolution looks forward to designing and deploying security systems that use various parameters for analyzing current and dynamic traffic trends and are highly time-efficient in predicting intrusions.

Aims

This paper proposes a novel approach for a time-efficient and smart Intrusion Detection System.

Method

Herein, we propose a Hybrid Feature Selection approach that aims to reduce the prediction latency without affecting attack prediction performance by lowering the model's complexity. Light Gradient Boosting Machine (LightGBM), a fast gradient boosting framework, is used to build the model on the latest CIC-IDS 2018 dataset.

Results

The proposed feature selection reduces the prediction latency ranging from 44.52% to 2.25% and the model building time ranging from 52.68% to 17.94% in various algorithms on the CIC-IDS 2018 dataset. The proposed model with hybrid feature selection and LightGBM gives 97.73% accuracy, 96% sensitivity, 99.3% precision rate, and comparatively low prediction latency. The proposed model successfully achieved a raise of 1.5% in accuracy rate and 3% precision rate over the existing model. An in-depth analysis of network parameters is also performed, which gives a deep insight into the variation of network parameters during the benign and malicious sessions.

中文翻译：

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一种新的基于时间高效学习的智能入侵检测系统方法

背景

入侵方法的日益复杂导致迫切需要开发具有最佳功效的入侵检测系统。然而，现有的入侵检测系统是使用过时的攻击数据集开发的，更多地关注预测准确性而不是预测延迟。智能入侵检测系统框架演变期待设计和部署使用各种参数来分析当前和动态流量趋势并在预测入侵方面具有高时间效率的安全系统。

宗旨

本文提出了一种新的方法来构建一种省时且智能的入侵检测系统。

方法

在此，我们提出了一种混合特征选择方法，旨在通过降低模型的复杂性来减少预测延迟而不影响攻击预测性能。Light Gradient Boosting Machine (LightGBM) 是一种快速梯度提升框架，用于在最新的 CIC-IDS 2018 数据集上构建模型。

结果

在 CIC-IDS 2018 数据集上的各种算法中，所提出的特征选择将预测延迟降低了 44.52% 到 2.25%，模型构建时间降低了 52.68% 到 17.94%。所提出的具有混合特征选择和 LightGBM 的模型提供了 97.73% 的准确度、96% 的灵敏度、99.3% 的准确率和相对较低的预测延迟。所提出的模型成功地实现了比现有模型提高1.5%的准确率和3%的准确率。还对网络参数进行了深入分析，可以深入了解良性和恶意会话期间网络参数的变化。