# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 3 Related Works  
Intrusion detection has gained significant prominence in the cybersecurity sector [43]. In recent years, there has been a growing emphasis on employing deep learning (DL) solutions in this field. Numerous instances of this trend have emerged across various I.T. domains, including cloud computing [44; 45] and computer networking [46; 47]. With the pervasive integration of IoT devices into our daily lives, a substantial portion of recent research in intrusion detection systems (IDS) has been dedicated to DL solutions within the IoT domain. For example, [PERSON] et al. [48] introduced an innovative, lightweight approach centered on a dense random neural network (DnRaNN) for detecting intrusions in IoT networks. Their method was rigorously evaluated against the ToN_IoT dataset, yielding outstanding results in binary and multi-class classification scenarios. [PERSON] et al. [49] employed the same dataset to investigate their DL-driven cyber threat modeling framework designed to automate the detection and extraction of cyber threats in IoT-enabled maritime transportation systems (MTS). Similar to previous research, they achieved promising binary and multi-class classification results. In another avenue of intrusion detection, an approach based on an adaptive particle swarm optimization convolutional neural network (APSO-CNN) was proposed [50]. This approach leverages the APSO algorithm to fine-tune the hyperparameters of a one-dimensional CNN autonomously. Evaluation of the N-BaloT dataset [51] and comparison with three other models demonstrated that this solution consistently outperformed its counterparts across all metrics. Additionally, initially designed for computer vision tasks such as facial or shape recognition, CNN-based solutions have gained traction and proven effective for network intrusion detection systems (NIDS) [52; 53; 54]. Shallow deep learning (DL) methods have also been advanced for IoT IDS. For instance, one approach based on a shallow artificial neural network (ANN) was presented by [55], focusing on the UNSW-15 dataset [56]. Another similar approach termed a multi-layer perceptron (MLP), was proposed by [57] to detect denial of service (DoS) attacks in IoT environments, evaluated on their custom testbed. Further innovation includes a hybrid approach combining shallow and deep ANNs [58]. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained popularity. LSTMs are ANNs that preserve hidden states, allowing the model to retain information from previous layers, making them suitable for sequences where each data point depends on its predecessors. RNNs, including LSTMs, have been extensively applied in various domains, such as speech recognition and text generation, and their use is also prevalent in IoT intrusion detection. For instance, [PERSON] et al. [59] introduced an LSTM-based approach for intrusion detection in smart home networks, achieving exceptionally high accuracy. In [60], LSTM was employed to detect attacks in Fog computing, using the ISCX2012 dataset [61] and another dataset derived from traffic in an 802.11 network. While the results appeared promising, the authors limited their comparison to another approach based on logistic regression (L.R. ), and a more comprehensive evaluation against advanced DL solutions could have provided a more thorough assessment of their work. Additionally, LSTM models have been tested in the automotive environment, as seen in [62], where a manually generated dataset based on traffic from a controller area network (CAN) was utilized as a testbed. The results were excellent and were validated using an open-source CAN dataset. [PERSON] et al. [63] presented an intrusion detection system in SDN using random forest. They employed network flow records and their statistics as features for training the machine learning model. To validate its performance, the system was tested with various network attacks, including brute force and reconnaissance attacks. [PERSON] et al.