Mitigating Denial Based Service Attacks in Heterogeneous Sensor Networks: Strategies and Solutions

With the development of heterogeneous sensory networks in various applications of domains for ensuring the security against denial based service (DoS) attacks will become a paramount. In this research study, a proposal of a novel system named SecureSenseGuard(SSG) for effectively detecting DoS attacks in heterogeneous sensor networks. SecureSenseGuard(SSG) leveraging the Decision Tree (DT) algorithm augmented with Recursive type Features Elimination (RFE) for achievinga robust and efficient attack detection. The proposed system may operate by first Collection of data from diverged sensors within the network. The data, representing various network attributes, are then preprocessed and fed into the DT algorithm for classifications process. RFE will be employed as a feature selection techniques to enhance the performance of DT algorithm by iterative process for removing less relevant features, thus improving in detection accuracy and reducing computational overhead. The proposed system SecureSenseGuard(SSG) will offer several advantages over existing approaches. By employing DT, it will provide interpretable and transparent decision rules for facilitating understanding and trust in the detection process. Moreover, the integration of RFE will enhance the system's scalability and adaptability for dynamic network environments. To evaluate the effectiveness of SecureSenseGuard(SSG) an extensive experiment may be conducted by using various ranges of datasets and synthetic attack scenarios. The Results demonstrate that SecureSenseGuard(SSG) achieved high detection rates. while maintaining a low false alarm rate, outperforming traditional approaches. Overall, SecureSenseGuard(SSG) may present a promising solution in mitigating DoS attacks in heterogeneous sensory networks by offering a balance between detection accuracy, interpretability, and computational efficiency.