Intrusion detection as well distributed denial of service (DDoS) are vital in ensuring computer network security. Some researchers claim that current approaches cannot meet the requirements of today’s networks are either not workable or sustainable. In a more specific sense, these concerns are related to an increasing number of human interactions, along with reducing levels of detection ability. With our study, a novel deep learning model for intrusion detection is developed for addressing these issues. We proposed a novel deep learning classification algorithm constructed using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) named CLSTMNet. Our proposed model has been implemented and evaluated using the benchmark NSL-KDD datasets. Compared with many conventional machine learning algorithms, the satisfied outcomes have been obtained from our model.

入侵检测以及分布式拒绝服务 (DDoS) 对于确保计算机网络安全至关重要。一些研究人员声称，当前的方法无法满足当今网络的要求，要么不可行，要么不可持续。在更具体的意义上，这些担忧与越来越多的人类互动以及检测能力水平的降低有关。通过我们的研究，开发了一种用于入侵检测的新型深度学习模型来解决这些问题。我们提出了一种使用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 构建的新型深度学习分类算法，名为 CLSTMNet。我们提出的模型已经使用基准 NSL-KDD 数据集实现和评估。与许多传统的机器学习算法相比，