## 2 Related Works  
As IoT Security is an emerging and essential field, much work has been conducted to create automated security systems (such as IDSs) for them. Much of this research has used AI to secure these IoT systems. AI is a pervasive paradigm within most recent technology fields and has seen great success in IoT security and the wider security field. Before any AI algorithm can be trained, there must be data available to train and test upon. As such, a large and vital portion of IoT security research has involved creating and labelling datasets of IoT attack data. The data must be realistic, relevant, and of good quality. Much surveying has been conducted in the field of IoT intrusion detection [7; 8; 9], in particular, presented extensive research on a wide range of algorithms used by researchers and summarised the popularity of well-known datasets in the reviewed work. Importantly, they realised that most of the works they surveyed used simulated or outdated datasets. This clarifies the need for as many new and up-to-date datasets as possible in cybersecurity and IoT security. The overuse of outdated datasets has led to research results, which may not translate to acceptable performance in the modern cyber threat topography. Table 1 lists some well-known datasets chronologically, along with their proportionate usage in [9]. In [9], the oldest work they reference is from 2016; when consulting Table 1, we can see a disproportionate percentage of papers using outdated datasets. The fact that researchers must rely on such outdated data illustrates the distinct lack of data in the field of IoT cybersecurity and, thus, motivates the creation of the Dragon_Pi dataset presented in this work. By consulting Table 1, it is also clear that the overwhelming majority of the dataset work conducted on IoT focuses entirely on network packets. While this makes sense, since cybersecurity has been dominated by network-based security for decades, it is imperative to consider security from as many points of view as possible in the modern cyber threat topography. Derived from the 1998 Defence Advanced Research Project Agency (DARPA) dataset [35], the Knowledge Discovery and Data Mining (KDD) 1999 dataset was created to be used in the development of security-focused ML [10; 11]. In essence, the KDD dataset transforms the DARPA dataset's network traces into a collection of features found in the connection between two hosts, with each connection constituting its own record in the dataset. These features are broken into basic (e.g., duration), content (e.g., logged_in), time-based (e.g., srv_count), and connection-based (e.g., dst_host_count) categories. Due to its acceptance as a de facto benchmark for cybersecurity research, many research works today are still conducted using the KDD dataset, regardless of its age and, indeed, its outdatedness. In 2019, ref. [13] applied three different artificial neural network (ANN) approaches to the KDD dataset. Utilising learning vector quantization (LVQ), the radial basis function (RBF), and multilayer perceptron (MLP), they achieved accuracies of 97.5%, 99.4%, and 99.86%, respectively. Notably, however, the presumably inference time performance of the algorithms was compared, and it was found that LVQs were far more time efficient, requiring only 4.56 s for the best-performing model, with time requirements of 22,610 s for RBFs and 28,811 s for MLP s.  
In 2022, ref. [12] compared K-nearest neighbour (K-NN), support vector machines (SVMs), and a novel hybrid approach utilising the SNORT IDS [36] and a naive Bayes (NB) algorithm on the KDD 99 dataset. In this hybrid approach, traffic is first analysed by the SNORT IDS and, if SNORT classifies the traffic as normal, the NB algorithm is applied to the traffic to confirm the result. The work achieved results of 93% for SVMs, 94.4% for K-NNs, and 96.6% for the proposed SNORT-NB hybrid model. Given the infancy of networking security at the time, the KDD dataset naturally leaves much to be desired, being one of the first of its type in the field.