Network intrusions are a significant problem in all industries today. A critical part of the solution is being able to effectively detect intrusions. With recent advances in artificial intelligence, current research has begun adopting deep learning approaches for intrusion detection. Current approaches for multi-class intrusion detection include the use of a deep neural network. However, it fails to take into account spatial relationships between the data objects and long term dependencies present in the dataset. The paper proposes a novel architecture to combat intrusion detection that has a Convolutional Neural Network (CNN) module, along with a Long Short Term Memory(LSTM) module and with a Support Vector Machine (SVM) classification function. The analysis is followed by a comparison of both conventional machine learning techniques and deep learning methodologies, which highlights areas that could be further explored.

翻译：网络入侵是当今所有行业面临的重大问题。解决方案的一个关键部分在于能够有效检测入侵。随着人工智能的最新进展，当前研究已开始采用深度学习方法进行入侵检测。当前用于多类入侵检测的方法包括使用深度神经网络。然而，该方法未能考虑数据对象之间的空间关系以及数据集中存在的长期依赖性。本文提出一种新型架构来应对入侵检测，该架构包含卷积神经网络（CNN）模块、长短期记忆（LSTM）模块以及支持向量机（SVM）分类功能。随后通过对比传统机器学习技术与深度学习方法进行分析，指出了可进一步探索的方向。