# An Intrusion Detection System Using BoT-IoT
## 2 Related Works  
Several scientific articles have been published on IDS that used data mining and ML techniques. However, primarily, these previous works have only utilized ML approaches to detect intrusions in conventional networks. Hence, in this paper, the field of ML was explicitly used to discover intrusions in the field of the IoT. The applications of ML approaches in the domain of IoT are yet in the initial phases of development, particularly in the IoT security domain, since it has a remarkable ability to identify ideas from IoT information. In the IoT network, ML applications such as flaw detection, pattern recognition, and behavioural analysis could be utilized to identify acute attacks and prevent abnormal activities. For analyzing recent research on detecting attacks utilizing ML in IoT networks, several studies have been analyzed and addressed, as shown in Table 1[8]. The work in [9] created novel hybrid identifiers to detect DDoS attacks in IoT networks by selecting features of six critical objectives to reduce data from the data set. The shortened data output from the previous step is fed as input data to the model based on the deep learning algorithm. The research in [10] devised a fast and efficient Artificial Neural Network (ANN)-based threat detection mechanism to identify a wide range of attacks on IoT devices and data. The dataset is distributed into sections to validate the structure. ANN technology is implemented in the IoT console to classify malicious packets in the event of an attack. Three layers of input, hidden, and output neural networks were used. The work in [11] used a hierarchical system that detects intrusion through three classifications. The model is made up of three classifiers, the first of which takes the data set's distinct attributes as the input, the second takes distinct data set attributes as the input, and the third input includes all of the raw data set's features, as well as the first and second classifier outputs. Another approach is RDTIDS integration for IoT networks [2]. The extraction of new dataset features can help classifiers improve their prediction skills. The raw data collection was pre-processed to detect anomalies, and then flow-based features were retrieved. The calculations were carried out using two approaches. First, significance weights for each assault type were determined independently. Second, all attacks were gathered into one group, and significance weights for this group were determined. The research in [12] created successful strategies for IoT security and detection of DoS attacks using deep ML algorithms integrating the evaluation of RF, CNN, and MLP algorithms. Hash chains provide a realistic threat model for IoT devices and a secure mechanism for storing and relocating device records. E-Spion uses system information to create three-layer core profiles with varied overheads for IoT devices and detects intrusions based on anomalous behaviour [13]. [PERSON] et al. introduced a method to select features for intrusion detection systems (IDSs) that identify DoS and DDoS attacks. The proposed approach involves insertion and union operations on subsets of the top 50% of IG and GR features. The approach's effectiveness is evaluated on two datasets, IoT-BoT and KDD Cup 1999, with a JRip classifier. The proposed approach outperforms the original feature set and traditional IDSs, requiring only 16 and 19 features, respectively [14]. [PERSON] et al. developed a deep learning-based approach to enhance IoT security using a CNN-based, anomaly-based intrusion detection system (IDS). The proposed approach can efficiently examine whole traffic across the IoT and detect any possible intrusions and abnormal traffic behaviour. The approach is evaluated using the BoT-IoT datasets, achieving 92.85% [15]. [PERSON] et al. provide an ML-based IDS technique that makes use of ensemble trees, comprising DT and RF classifiers, to improve the effectiveness of IDS's attack detection and to explain the predictions made by the ML model.