# DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System
## 6 DeepDCA: Deep Learning Dendritic Cell Algorithm
### Anomaly Metrics: \(Mcav\) and \(K_{\alpha}\)

Table 7: DeepDCA performance based on Features Impact among Imbalanced and Balanced Data. ### Impact of Attack Scenarios

We evaluated the proposed IDS by measuring the performance metrics in different attack scenarios as shown in Table 8. The results illustrated that DeepDCA performed well in detecting various attack types although its performance was better in DDoS/DoS attacks which may be due to the abundance of data about this attack in the BoT-IoT dataset. ### Comparison with Classifiers

The performance evaluation results of the DeepDCA model are compared with four commonly used methods for intrusion detection, namely the Support-Vector Machines (SVM), Naive Bayes (NB), K Nearest Neighbor (KNN) and Multilayer Perceptron (MLP). The comparison made is in terms of Accuracy, F-measure, Recall/sensitivity and Precision. Table 9 shows that DeepDCA slightly better than MLP and outperformed other classifiers SVM, NB and KNN. To sum up, applying the DeepDCA for the Intrusion detection system was validated against an IoT dataset demonstrating over 98.73% accuracy. It was able to identify successfully different types of attacks and showed good performances in terms of detection rate and false-positive rates. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Attack** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline
**DoS** & 99.8\% & 99.5\% & 98.53\% & 99.012\% \\ \hline
**DDoS** & 99.9\% & 100\% & 100\% & 100\% \\ \hline
**Reconnaissance** & 99.10\% & 98.88\% & 98.22\% & 98.54\% \\ \hline
**Information** & 98.56\% & 99.01\% & 98.9\% & 98.95\% \\
**Theft** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: DeepDCA Performance Evaluation Metrics in Different Attack Categories. Figure 7: Comparison of Features Impact in Classification Process (Im = Imbalanced Data, Ba = Balanced Data). ## 9 Conclusions

In this research, we develop a Deep Learning Dendritic Cell Algorithm (DeepDCA). Our framework adopts DCA and Self Normalizing Neural Network. The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smoothe the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset and to perform their signal categorization using the SNN. The experimentation results show that our DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, capable of performing better classification tasks than SVM, NB, KNN and MLP classifiers. We plan to carry out further experiments to verify the framework using more challenging datasets with missing and noisy data and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection. Conceptualization, S.A.; Formal analysis, S.A.; Funding acquisition, D.A., B.A. and A.-B. ; Investigation, S.A.; Methodology, S.A.; Project administration, D.A. ; Supervision, D.A., L.C., B.A. and A.-B. ; Validation, S.A.; Visualization, S.A.; Writing--original draft, S.A.; Writing--review and editing, D.A. All authors have read and agreed to the published version of the manuscript. This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant no. (RG-6-611-39). The authors, therefore, acknowledge with thanks DSR technical and financial support. Also, Computation for the work described in this paper was supported by High-Performance Computing Center Aziz Supercomputer ([[https://hpc.kau.edu.sa](https://hpc.kau.edu.sa)]([https://hpc.kau.edu.sa](https://hpc.kau.edu.sa))). The authors would like to thank the members of Aziz support team for the continuous support and guidance. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: