# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 3 Related Works
[64] combined SVM and selective logging with I.P. traceback for detecting intrusions in SDN. They reported that their IDS implementation was resource-efficient, leading to savings in memory usage. To evaluate their approach, they conducted experiments using the NSL-KDD dataset, achieving a detection accuracy of 87.74%. [PERSON] et al. [65] introduced an SVM-based machine learning method for detecting distributed denial of service (DDoS) attacks within SDN. They employed network tuple characteristics as features to identify network protocol attacks, including TCP, UDP, and ICMP attacks. They generated their dataset using network traffic in a controlled testing environment, utilizing tools like hping3. Their findings indicate a high detection accuracy of 95.34% for recognizing UDP flooding attacks. [PERSON] et al. [66] introduced a hybrid intrusion detection model within SDN, combining CNN and random forest. They also introduced a novel regularization technique aimed at enhancing intrusion detection performance. The reported results demonstrated a substantial improvement in detection accuracy, achieving a 97% increase with the hybrid model. [PERSON] et al. [67] introduce an intrusion detection tree (IntruDTree) security model that prioritizes essential security features and constructs a tree-based intrusion detection model using these critical features. This model is effective in predicting unseen test casesand reduces computational complexity by reducing the number of features. The model's effectiveness is evaluated through experiments on cybersecurity datasets, assessing metrics like precision, recall, F-score, accuracy, and ROC values. Additionally, the IntruDTree model is compared with traditional machine learning methods like naive Bayes, logistic regression, support vector machines, and k-nearest neighbor to assess its overall effectiveness in enhancing security. Authors in [68] describe a proposed intrusion detection system (IDS) consisting of two stages. In the first stage, data is collected through dedicated sniffers (DSs) to create a collaborative communication index (CCI), which is periodically sent to a super node (S.N.). In the second stage, the S.N. employs linear regression to analyze the collected CCIs from different D.S.s to distinguish between benign and malicious network nodes. The paper presents detection characterization for various extreme network scenarios involving power levels and node velocities using two mobility models: random waypoint (RWP) and Gauss Markov (G.M.). The malicious activities studied include blackhole and distributed denial of service (DDoS) attacks. The results indicate high detection rates of over 98% for scenarios with high power levels and node velocities. In comparison, these rates drop to approximately 90% for scenarios with low power levels and node velocities. [PERSON] et al. [69] introduce DF-IDS, a model for detecting intrusions in IoT traffic. DF-IDS consists of two main phases: In the first phase, it selects the best features from the feature matrix by comparing various feature selection techniques like SpiderMonkey (S.M. ), principal component analysis (PCA), information gain (I.G. ), and correlation attribute evaluation (CAE). In the second phase, these selected features and assigned labels are used to train a deep neural network for intrusion detection. DF-IDS achieves an impressive accuracy of 99.23% and an F1-score of 99.27%. It outperforms other comparative models and existing studies in terms of accuracy and the F1 score, indicating significant improvements in intrusion detection performance. [PERSON] et al. [70] suggested an intelligent cognitive computing-driven intrusion detection system designed for industrial cyber-physical systems (CPS). Their approach encompasses a comprehensive data processing pipeline, encompassing stages like data acquisition, preprocessing, feature selection, classification, and optimization, all aimed at identifying anomalies. The literature review is summarized in Table 2.