Design of distributed denial-of-service attack prevention mechanism for IoT-driven data fusion system

In the current era, informatics systems technology is advancing at a rapid pace, and as a result, the Internet of Things (IoT) has become increasingly important to daily life in many ways. Multisensor fusion is the integration of data from several sensors/ sensing devices (i.e., smart IoT devices) to produce a more accurate and reliable representation of the environment. It is a crucial technology across numerous fields, including robotics, autonomous vehicles, smart cities, and other IoT-driven applications. The availability of several devices that serve as IoT enablers, such as smartwatches, smartphones, security cameras, and smart sensors, has led to an increase in the popularity of IoT applications compared to earlier times. In order to create a bidirectional distributed denial-of-service (DDoS) detection mechanism for an IoT-driven data fusion system, this study proposed a scheme by making use of three deep/ machine learning algorithms, K-Nearest neighbor (KNN), Gaussian Mixture Model (GMM), and Support Vector Machine (SVM). In order to identify the most efficient model against DDoS attacks that can precisely detect and discriminate DDoS from legal traffic, the KNN, GMM, SVM are tested and put into practice using SVM model for highest accuracy. An SDN-specific data set created with Mini Net emulator involves designing a network topology, generating traffic, and collecting data to evaluate SDN applications and controllers. Confusion Matrix is used to test and evaluate the three models in relation to four widely-used criteria: accuracy, precision, recall, and F-measure. Network simulation is used to analyze malicious traffic, which consists of a combination of ICMP, UDP Flood, and TCP Syn attack, as well as benign TCP, UDP, and ICMP traffic.