# Deep-Learning-Based Approach for IoT Attack and Malware Detection
## 5 Discussion  
In this study, the performances of various machine-learning methods used in the classification of IoT security data were compared. The findings reveal that the proposed model demonstrates superior performance, particularly in terms of high accuracy, precision, recall, and F1-score, when compared to other contemporary methods. The proposed model has shown more consistent and high performance across the CIC IoT 2023, CIC-MalMem-2022, and CIC-IDS2017 datasets than other studies. This indicates that the proposed model can be effectively and reliably used as a tool for classifying IoT security data. The discussion section examines the reasons behind these findings, the advantages the model offers compared to other methods, and potential areas for improvement. Addi \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Class No** & **Class Name** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline
1 & BENIGN & & 99.60 & 96.32 & 97.93 \\
2 & DDoS & & 99.90 & 97.16 & 98.51 \\
3 & PortScan & & 80.15 & 99.75 & 88.88 \\
4 & Bot & & 69.66 & 34.24 & 45.91 \\
5 & Infiltration & & 100.00 & 83.33 & 90.91 \\
6 & Web Attack Brute Force & & 100.00 & 98.67 & 99.33 \\
7 & Web Attack Sql Injection & & 100.00 & 100.00 & 100.00 \\
8 & Web Attack XSS & 96.55 & 100.00 & 92.86 & 96.30 \\
9 & FTP-Patator & & 100.00 & 51.09 & 67.63 \\
10 & SSH-Patator & & 100.00 & 49.55 & 66.26 \\
11 & DoS GoldenEye & & 97.99 & 91.77 & 94.78 \\
12 & DoS Hulk & & 83.86 & 99.93 & 91.19 \\
13 & DoS Slowhttptest & & 57.63 & 97.94 & 72.56 \\
14 & DoS slowloris & & 89.95 & 91.61 & 90.77 \\
15 & Heartbleed & & 100.00 & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of results (%) obtained for different classes in the CIC-IDS2017 dataset. tionally, the integration of these results into practical security systems and future directions of research in this area are discussed. Comparison results are tabulated in Table 6.  
Various researchers developed different classifiers using machine-learning methods on IoT security data. [PERSON] et al. achieved high-performance results using an End-to-End 1D CNN on the Edge-IIoTset dataset (Accuracy: 99.96%, Precision: 100%, and Recall: 99%, F1-score: 99). [PERSON] et al. [44] experimented with various methods on the CICloV2024 dataset, achieving 95% accuracy with Logistic Regression, Deep Neural Network, and Random Forest, while the AdaBoost method reached 87% accuracy. [PERSON] et al. [45] used Random Forest on the CIC IoT 2023 dataset for both Coarse-Grained and Fine-Grained classification, obtaining approximately 96% accuracy. [PERSON] et al. [46] demonstrated high performance with 98.82% accuracy using deep autoencoders and a Stacked Ensemble on the MemMal-D2024 dataset. [PERSON] et al. [47] achieved high F1-scores (97.0%) using memory-based features and Random Forest on the CICMalDroid2020 and CIC-AndMal2017 datasets. [PERSON] et al. [48] obtained 94.93% and 91.93% accuracy on the N-baIoT and Edge-IIoTset datasets, respectively, using Federated Learning, Quantum Computing, and Additive Homomorphic Encryption. [PERSON] et al. reached high accuracy rates (99.99%) on the UNSW-NB15, CIC-IDS2017, and CIC-IDS2018 datasets using Random Oversampling (RO), Stacking Feature Embedding, and PCA. In this proposed model, using CNN and Softmax, notable results on the CIC IoT 2023, CIC-MalMem-2022, and CIC-IDS2017 datasets were achieved. Specifically, it achieved 98.36% accuracy, 100% precision, 99.96% recall,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Year** & **Method(s)** & **Classifier** & **Dataset** & **Class** & **Results (\%)** \\ \hline Hassini  
and 99.95% F1-score on the CIC IoT 2023 dataset; 99.90% accuracy, 99.98% precision, 99.97% recall, and 99.96% F1-score on the CIC-MalMem-2022 dataset; and 99.99% accuracy, 99.99% precision, 99.98% recall, and 99.98% F1-score on the CIC-IDS2017 dataset.