This paper introduces an innovative intrusion detection system that harnesses
Generative Adversarial Networks (GANs), Multi-Scale Convolutional Neural
Networks (MSCNNs), and Bidirectional Long Short-Term Memory (BiLSTM) networks,
supplemented by Local Interpretable Model-Agnostic Explanations (LIME) for
interpretability. Employing a GAN, the system generates realistic network
traffic data, encompassing both normal and attack patterns. This synthesized
data is then fed into an MSCNN-BiLSTM architecture for intrusion detection. The
MSCNN layer extracts features from the network traffic data at different
scales, while the BiLSTM layer captures temporal dependencies within the
traffic sequences. Integration of LIME allows for explaining the model's
decisions. Evaluation on the Hogzilla dataset, a standard benchmark, showcases
an impressive accuracy of 99.16\% for multi-class classification and 99.10\%
for binary classification, while ensuring interpretability through LIME. This
fusion of deep learning and interpretability presents a promising avenue for
enhancing intrusion detection systems by improving transparency and decision
support in network security.

通过生成对抗网络（GANs）、多尺度卷积神经网络（MSCNNs）和双向长短期记忆（BiLSTM）网络，以及与 Local Interpretable Model-Agnostic Explanations（LIME）进行解释性补充，引入、创新了一种入侵检测系统。该系统生成真实的包含正常和攻击模式的网络流量数据，通过 MSCNN-BiLSTM 架构进行入侵检测。MSCNN 层从不同尺度的网络流量数据中提取特征，而 BiLSTM 层捕获流量序列中的时间依赖关系。整合 LIME 可解释模型的决策。在 Hogzilla 数据集上的评估展示了多类分类和二分类的令人印象深刻的准确率，分别为 99.16％和 99.10％，同时通过 LIME 保证了可解释性。通过结合深度学习和可解释性，为提高入侵检测系统的透明度和决策支持提供了一个有前途的途径。

入侵检测的新方法：利用 GAN-MSCNN-BILSTM 和 LIME 预测

Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with  LIME Predictions

Time series classification (TSC), the problem of predicting class labels of
time series, has been around for decades within the community of data mining
and machine learning, and found many important applications such as biomedical
engineering and clinical prediction. However, it still remains challenging and
falls short of classification accuracy and efficiency. Traditional approaches
typically involve extracting discriminative features from the original time
series using dynamic time warping (DTW) or shapelet transformation, based on
which an off-the-shelf classifier can be applied. These methods are ad-hoc and
separate the feature extraction part with the classification part, which limits
their accuracy performance. Plus, most existing methods fail to take into
account the fact that time series often have features at different time scales.
To address these problems, we propose a novel end-to-end neural network model,
Multi-Scale Convolutional Neural Networks (MCNN), which incorporates feature
extraction and classification in a single framework. Leveraging a novel
multi-branch layer and learnable convolutional layers, MCNN automatically
extracts features at different scales and frequencies, leading to superior
feature representation. MCNN is also computationally efficient, as it naturally
leverages GPU computing. We conduct comprehensive empirical evaluation with
various existing methods on a large number of benchmark datasets, and show that
MCNN advances the state-of-the-art by achieving superior accuracy performance
than other leading methods.

提出了一种基于多尺度卷积神经网络的时间序列分类模型，该模型将特征提取和分类集成在一个框架中，具有较高的准确性和计算效率，并在大量基准数据集上进行了全面的实证评估，取得了优于其他先进方法的性能。