Botnet Attack Detection in IoT Networks using CNN and LSTM

Anuj Sharma, Prasoon Mishra, Dr. G. Geetha
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Abstract

Botnet attacks are a major concern for IoT devices, but using deep learning (DL) to identify them requires significant memory space and network traffic, making it difficult to implement on devices with limited memory. One can use dimensionality reduction methods to decrease the number of features in IoT network traffic data. The Bot-IoT dataset is a dataset that is accessible to the public, and it can be utilized to identify botnet attacks in IoT networks., with millions of samples of botnet attack traffic classified into DDoS, DoS, reconnaissance, and information theft scenarios. Dimensionality reduction techniques like principal component analysis (PCA) and autoencoder can help reduce the feature dimensionality of the dataset. Autoencoder, an unsupervised deep learning technique generates a hidden layer's latent-space representation of the input data. The reduced feature set can be used by deep learning algorithms like Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) to detect botnet attacks. Performance measurements like accuracy, precision, recall, and confusion matrix can be used to evaluate the effectiveness of the approach. In summary, the proposed approach uses dimensionality reduction techniques like PCA and autoencoder to reduce the feature dimensionality of the Bot-IoT dataset, making it feasible to use DL algorithms like LSTM and CNN to identify botnet attacks. Performance metrics can be used to evaluate the effectiveness of the approach.
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基于CNN和LSTM的物联网网络僵尸网络攻击检测
僵尸网络攻击是物联网设备的一个主要问题,但使用深度学习(DL)来识别它们需要大量的内存空间和网络流量,因此难以在内存有限的设备上实现。可以使用降维方法来减少物联网网络流量数据中的特征数量。Bot-IoT数据集是一个对公众开放的数据集,它可以用来识别物联网网络中的僵尸网络攻击。,其中有数以百万计的僵尸网络攻击流量样本,分为DDoS、DoS、侦察和信息盗窃场景。主成分分析(PCA)和自动编码器等降维技术可以帮助降低数据集的特征维数。自动编码器,一种无监督深度学习技术,生成输入数据的隐藏层的潜在空间表示。简化后的特征集可以被长短期记忆(LSTM)和卷积神经网络(CNN)等深度学习算法用于检测僵尸网络攻击。可以使用准确度、精密度、召回率和混淆矩阵等性能度量来评估该方法的有效性。综上所述,所提出的方法使用PCA和自动编码器等降维技术来降低Bot-IoT数据集的特征维数,使得使用LSTM和CNN等深度学习算法来识别僵尸网络攻击成为可能。性能指标可用于评估该方法的有效性。
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