## 3 Proposed Solution and Methodology
### Intrusion Detection System (IDS) Phase Used in Proposed DLMIDPS Model  
Based on our proposed DLMIDPSM in Figure 1, we deploy deep learning as a unique machine learning technique for the better extraction of features, for better understanding, and for machine perception. Subsequently, we utilize the IoT dataset, such as CICIoT2023, which in our proposed method has IDPS capability. As shown below, we use multiple consecutive deep learning layers (DL) for our Algorithm 1. DL layers interconnect with each layer that receives input as an output of the previous layer. Based on this, we ensure that DL provides efficient algorithm usage and provides an advantage in hierarchical feature extraction. In Figure 1, we briefly discuss the intrusion prevention (IP) phase, which we will discuss in detail in a subsequent section. In the IP phase, a script is generated and uses all of the Canadian Institute of Cybersecurity (CIC) admin privileges. This script can prevent intrusion and all malicious activity requests, including DoS intrusion attacks. The script terminates connections and informs the CIC admin about all potential intrusions and malicious activities in the network, which begins the IP phase (we will discuss this in detail in a later section). We deploy both the intrusion detection (ID) and intrusion prevention (IP) phases and integrate the two as a software in Algorithm 1, which we implement below. Since the intrusion attacks detected in [31] only concern attacks which are not of IoT dataset devices, data representation was based on just detecting manual features in deep learning [48]. To investigate this and provide a solution, we aim to investigate intrusion attacks, including MITM, Recon, DoS, DDoS, and benign traffic typical of the proposed large-scale CICIoT2023 dataset that utilizes an artificial neural network architecture, named multilayer perceptron, convolutional neural network, and recurrent neural network. The proposed DLMIDPSM uses multilayer perceptron (MLP). MLP is a neural network type that consists of one or many neuron layers. We feed data to the input layer, which consists of one or many hidden layers that provide some abstraction levels and make predictions at the output layer, known as the visible layer. We consider MLP to be a feed-forward artificial neural network class. MLP generally consists of three nodes: the input, output, and hidden layers. Each node may also be a neutron that uses a nonlinear activation function. MLP also uses a guided learning method for teaching, known as backpropagation. Additionally, MLP is distinguished from linear perceptron [49] by the use of multiple layers and linear activations. It also can distinguish datasets that are not linearly independent. ```
**Algorithm 1**Framework Algorithm for Deep Learning Multilayer Perceptron Intrusion Detection and Prevention System (DLMIDPS) Model

Notations : \(D_{0}\)--initial file captured from CICIOT2023 dataset; DL--deep learning; MLP--multilayer perceptron; \(a_{i}\)--subsequent intrusion attacks; IDPSM model--the combined output model in Figure 1

1. Begin
2. Input:\(D_{0}\): Industrial Internet of Things (IIoT) office-based devices IDPST Topology data
3. Output: IDPS: Proposed Intrusion Detection and Prevention System Model Action
4. Procedure: DLMPIDPSM Model: Deep learning Multilayer Perceptron Intrusion Detection and Prevention System \((D_{0})\)
5. Industrial Office Environment:
6. {AI, IoT, IIoT, cloud computing, Sensor Devices}
7. IoT Sensor Devices = {Real-time intrusion attacks detection and prevention from large-scale CICIoT2023 Dataset, Training, and Validation/Testing}
8. IoT Intrusion-based attacks and benign traffic--(IIBABT)
9. IIBABT = {Mirai, DDoS, DoS, MITM, Recon, and Benign traffic}
10. Intrusion \(attacks_{Sorted}\) = (AI \((a_{i})\))
11. Dataset Collection & Preprocessing (S): \(\gets D_{Collectal\&Preprocessing}=\{CICIoT2023\) dataset source, normalization, feature scaling, and engineering}
12. AI DL-based Intrusion Detection& Prevention and Classification (AIDPLC):
13.