Efficient Vulnerability Classification in IoT Networks: An Approach Using Convolutional Neural Networks and Tabu Search Optimization

In this study, researchers propose a novel solution for efficient enhancement of vulnerability detection in several IoT environments. Efficient Vulnerability Classification has been introduced as the presented technique in IoT Networks (EVCIN). The method, EVCIN, which is a proposed approach, utilizes CNNs with Tabu Search Optimization. Customized CNN models have proven to be extremely accurate in identifying vulnerabilities for IoT classes, showing half (6 layers), Sefunten 99.03% (7 layers), and 95.71% (8 Layers). The contribution of using Tabu Search did increase the accuracy of classification through introducing an effective set of techniques that head towards the optimal solutions. Throughout the study, the superior performance of EVCIN was demonstrated in characterizing vulnerabilities when it was compared against single CNN and Tabu Search models and state-of-the-art methods. Data visualization and AUC analyses were also effective for understanding the performance and discrimination ability of models. There are numerous important implications from the study of EVCIN for enhancing cybersecurity in IoT and also adding vitality to the development of vulnerability classification in IoT networks. The above approach gives a potentially useful solution in a reliable and efficient way for vulnerability finding. This would then enhance security and flexibility in IoT-based networks.