A Hybrid Technique Based on RF-PCA and ANN for Detecting DDoS Attacks IoT

The increasing reliance on smart products has increased vulnerabilities in Internet of Things (IoT) traffic, which poses significant security risks. These vulnerabilities allowed some hackers to exploit them, which led to system performance degradation. Attacks can lead to these vulnerabilities to various undesirable outcomes, including data leakage, economic losses, data breaches, operational disruptions, and damage to the company's reputation. To address these security challenges, network intrusion detection alarms play a crucial role in assessing system security. In recent years, the proliferation of intelligent and soft computing-based algorithmic and structural frameworks has been evident. However, previous studies have faced challenges related to comprehensiveness, zero-day attacks, realism, and data interpretation. In light of these concerns, this study proposes to design a neural network for proactive detection of attacks. Moreover, we propose to use a hybrid system called RF-PCA to facilitate dimensionality reduction and help classifiers. Notably, this is the first application of a BOT-IoT data set in such an approach. The study also includes a discussion of relevant IoT terms in the context of our work. The proposed method uses high-level data features to represent and draw conclusive conclusions. To evaluate its effectiveness, an experiment was conducted using Python as the programming environment, achieving a remarkable detection rate of 99.73%.