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Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset

机译：使用嵌入式特征选择和CNN用于CCD-inid-V1-A新型物联网数据集的分类

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摘要

As Internet of Things (IoT) networks expand globally with an annual increase of active devices, providing better safeguards to threats is becoming more prominent. An intrusion detection system (IDS) is the most viable solution that mitigates the threats of cyberattacks. Given the many constraints of the ever-changing network environment of IoT devices, an effective yet lightweight IDS is required to detect cyber anomalies and categorize various cyberattacks. Additionally, most publicly available datasets used for research do not reflect the recent network behaviors, nor are they made from IoT networks. To address these issues, in this paper, we have the following contributions: (1) we create a dataset from IoT networks, namely, the Center for Cyber Defense (CCD) IoT Network Intrusion Dataset V1 (CCD-INID-V1); (2) we propose a hybrid lightweight form of IDS—an embedded model (EM) for feature selection and a convolutional neural network (CNN) for attack detection and classification. The proposed method has two models: (a) RCNN: Random Forest (RF) is combined with CNN and (b) XCNN: eXtreme Gradient Boosting (XGBoost) is combined with CNN. RF and XGBoost are the embedded models to reduce less impactful features. (3) We attempt anomaly (binary) classifications and attack-based (multiclass) classifications on CCD-INID-V1 and two other IoT datasets, the detection_of_IoT_botnet_attacks_N_BaIoT dataset (Balot) and the CIRA-CIC-DoHBrw-2020 dataset (DoH20), to explore the effectiveness of these learning-based security models. Using RCNN, we achieved an Area under the Receiver Characteristic Operator (ROC) Curve (AUC) score of 0.956 with a runtime of 32.28 s on CCD-INID-V1, 0.999 with a runtime of 71.46 s on Balot, and 0.986 with a runtime of 35.45 s on DoH20. Using XCNN, we achieved an AUC score of 0.998 with a runtime of 51.38 s for CCD-INID-V1, 0.999 with a runtime of 72.12 s for Balot, and 0.999 with a runtime of 72.91 s for DoH20. Compared to KNN, XCNN required 86.98% less computational time, and RCNN required 91.74% less computational time to achieve equal or better accurate anomaly detections. We find XCNN and RCNN are consistently efficient and handle scalability well; in particular, 1000 times faster than KNN when dealing with a relatively larger dataset-Balot. Finally, we highlight RCNN and XCNN’s ability to accurately detect anomalies with a significant reduction in computational time. This advantage grants flexibility for the IDS placement strategy. Our IDS can be placed at a central server as well as resource-constrained edge devices. Our lightweight IDS requires low train time and hence decreases reaction time to zero-day attacks.
机译：由于事物互联网（IOT）网络随着积极设备的年增长率而全球扩展，为威胁提供更好的保障措施变得更加突出。入侵检测系统（IDS）是最有可行的解决方案，这些解决方案可减轻网络攻击威胁。鉴于IOT设备的不断变化的网络环境的许多限制，需要有效但轻量级ID来检测网络异常并分类各种网络攻击。此外，用于研究的大多数公共数据集不反映最近的网络行为，也不是由IoT网络制成的。要解决这些问题，请在本文中，我们有以下贡献：（1）我们从IoT网络创建一个数据集，即网络防御中心（CCD）IoT网络入侵数据集V1（CCD-INID-V1）; （2）我们提出了一种混合轻量级形式的IDS-A嵌入式模型（EM），用于特征选择和用于攻击检测和分类的卷积神经网络（CNN）。该方法具有两种型号：（a）rcnn：随机森林（rf）与cnn和（b）xcnn组合：极端梯度升压（xgboost）与cnn结合。 RF和XGBoost是嵌入式模型，可以减少较少的抗冲功能。 （3）我们尝试异常（二进制）分类和基于攻击（多字符）CCD-V1和其他IOT数据集的分类，TheRedion_of_iot_botnet_Attacks_n_baiot DataSet（Salot）和CiC-CIC-Dohbrw-2020数据集（DOH20），探讨基于学习的安全模型的有效性。使用RCNN，我们在接收器特征运算符（ROC）曲线（AUC）曲线（AUC）曲线（AUC）曲线下实现了0.956的区域，在CCD-INID-V1上的运行时间为32.28秒，0.999，在甘露板上的运行时间为71.46秒，并且运行时的运行时间为0.986在DOH20的35.45秒。使用XCNN，我们实现了0.998的AUC分数，运行时间为CCD-ind-V1,0.999，运行时间为72.12秒，为0.999，运行时间为DOH20。与KNN相比，XCNN需要较低的计算时间86.98％，并且RCNN所需的计算时间较低，以实现相同或更好的准确异常检测。我们发现XCNN和RCNN始终如一地有效，处理可扩展性;特别是，在处理相对较大的数据集 - 甘托管时，比KNN快1000倍。最后，我们突出了RCNN和XCNN准确检测异常的能力，在计算时间显着降低。这一优势授予IDS放置策略的灵活性。我们的ID可以放在中央服务器以及资源受限的边缘设备。我们的轻质ID需要低列车时间，因此降低了零日攻击的反应时间。

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