CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment

Abstract

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website.

DCNN: A Novel Binary and Multi-Class Network Intrusion Detection Model via Deep Convolutional Neural Network

“…The corresponding confusion matrix can be found in Figure 6 As a final assessment of the outcomes derived from the process of evaluation, Table 7 provides a comprehensive analysis of the overall performance of the model proposed by our work as compared to the State-of-the-Art models on the CICIoT2023 dataset. Notwithstanding, the obtained results indicate that our model, when compared to the related-work on the CICIoT2023 dataset, achieves a level of accuracy and TPR that surpasses that of [29], although it falls slightly short in comparison to [39]. Nevertheless, our model still exhibit exceptional performance, particularly in terms of PPV and F-score metrics, surpassing those of the State-of-the-Art in both binary and multi-class classification scenarios.…”

“…It has been observed that the majority of datasets available are outdated lacking reliability due to the absence of dependable validation and testing [29]. This is primarily due to having limited volume of data and deficient diversity in terms of traffic.…”

“…Furthermore, monitoring the performance of each class is imperative in order to guarantee that the model is functioning effectively across all class. However, many models lack reporting on the performance of individual classes in multi-class classification (e.g., see [6,[28][29][30][31][32]). Hence, resulting in an incomplete comprehension of the model's performance.…”

Advancing Intrusion Detection Efficiency: A ’Less is More’ Approach via Feature Selection

“…Through our focus on this rich dataset, combined with innovative preprocessing techniques, we contributed to advancing intrusion detection systems by achieving higher F1 scores and lower training and prediction times. [27].…”

“…As stated in the related works section, the dataset used in this study was the CICIoT2023 dataset, which was made publicly available by Neto et al [27].…”