Abstract :

Secure communication in wireless sensor networks (WSNs) is essential due to the sensitivity of the data they handle and their susceptibility to various security threats. Traditional cryptographic methods often introduce significant computational overhead, which is impractical for resource-constrained sensor nodes. This research proposes a novel approach for secure communication over WSNs utilizing image steganography enhanced by Generative Adversarial Networks (GANs). The proposed method leverages the inherent redundancy in images to embed sensitive data securely, thus maintaining a low computational footprint. GANs are employed to generate highly realistic cover images that enhance the steganographic security by making the embedded data virtually undetectable. The generator network creates plausible cover images, while the discriminator network continually improves the system's ability to distinguish between genuine and steganographic images. Experimental results demonstrate the method's effectiveness in providing robust security against various steganalytic attacks while maintaining high-quality image output. This approach significantly enhances the security of WSN communications without imposing substantial computational or energy burdens on the sensor nodes. The proposed system shows promise for deployment in scenarios requiring high security and low resource consumption, marking a significant advancement in the field of secure communication for WSNs.