# Deep-Learning-Based Approach for IoT Attack and Malware Detection
### Related Works  
This section reviews the existing literature related to the proposed research. Specifically, studies that employ deep-learning- and machine-learning-based methods in the fields of IoT security and malware detection will be examined. The datasets used the performance metrics of the methods, and the challenges encountered will be discussed in detail. Additionally, the gaps in the current literature and how this research aims to address these gaps will be discussed. The focus will be on approaches that stand out for their successes and limitations, emphasizing this study's contributions to the literature. [PERSON] et al. [21] developed a model using SVM and Bayesian methods to detect and classify IoT attacks, utilizing the KDD-CUP 99 dataset for evaluation. After feature reduction in the dataset, the model achieved 91.50% accuracy in multi-class classification. [PERSON] et al. [22] conducted a study to detect Bot, DoS, and HTTP attacks using the CSE-CIC-IDS2018 dataset. They tested random forest, multilayer perceptron, and one-dimensional convolutional neural networks (Conv1D) models. The convolutional layers used 32, 39, and 64 filters, with a filter size of 5 and a batch size of 32, training the model for 50 iterations. The neural network architecture included Conv1D (32.5), Conv1D (64.5), MaxPool (2), Conv1D (39.5), MaxPool (2), and two fully connected layers (FC). This configuration resulted in accuracy, precision, recall, and F1-scores of 99.98%. [PERSON] et al. [23] developed a deep-learning-based attack detection system to identify DDoS attacks. This system was built on three different models: convolutional neural networks (CNN), artificial neural networks (ANN), and recurrent neural networks (RNN). The performance of each model was evaluated on two new real traffic datasets, CIC-DDoS2019 and TON IoT, for both binary and multi-class classification. The multi-class classification accuracy for the first dataset was 95.90%, while the binary classification achieved 99.95%. For the second dataset, the multi-class classification accuracy was 98.94%. [PERSON] et al. [24] developed an attack detection system using one-dimensional convolutional neural networks (1 DCNN). This study trained and tested the model on the CSE-CIC-IDS2017 dataset to classify DoS Hulk, DoS GoldenEye, DDoS, Portscan, and benign traffic. The model's training accuracy was 99.32% and the test accuracy was 98.96%. Additionally, the model's precision was 98.70%, the recall was 99.20%, and the F1-score was 98.94%. [PERSON] et al. [25] developed an anomaly detection system for IoT networks using a feedforward neural network based on flow and control flag features. This model was evaluated on various IoT-focused datasets for both binary and multi-class classification. The datasets used included BoT-IoT, IoT network attack, MQTT-IoT-ID S2020, MQTTset, IoT-23, and IoT-DS2. The study results showed very high accuracy rates of 99.97% for multi-class classification and 99.99% for binary classification. [PERSON] et al. [26] proposed a static malware detection method based on permissions and API calls from Android applications. They employed three machine-learning algorithms--SVM, KNN, and Naive Bayes--evaluated on the CICInvesAndMal2019 Android malware dataset. This approach aims to offer a reliable and effective solution for malware detection. [PERSON] et al. [27] enhanced a one-dimensional convolutional neural network (1D-CNN) using genetic algorithms (GA) and particle swarm optimization (PSO) to improve performance. On the CSE-CIC-IDS2017 dataset, the model achieved a test accuracy of 99.71%, precision of 100%, and recall and F1-scores of 99% with GA optimization. With PSO, the results were slightly higher, with a test accuracy of 99.74%, precision of 100%, recall of 99%, and an F1-score of 100%. Both optimization methods demonstrated excellent performance. [PERSON] et al.