Adversarial networks, in a general computing sense, refer to systems or groups of entities operating with hostile intent against a target system. In machine learning, Generative Adversarial Networks (GANs) involve two competing neural networks, a generator and a discriminator, learning from each other. This dynamic competition aims to produce highly realistic synthetic data. The generator creates data, while the discriminator attempts to distinguish real data from generated data.
Context
While GANs are primarily a machine learning concept, the broader idea of adversarial networks appears in crypto news when discussing security threats or sophisticated market manipulations. The term may also apply to groups of malicious actors coordinating to undermine a blockchain protocol or digital asset exchange. Awareness of such coordinated threats is important for understanding market stability and security measures.
This research introduces a novel hybrid blockchain-SDN framework, leveraging dual-layer consensus and trust-aware clustering to secure IoT routing while optimizing energy.
We use cookies to personalize content and marketing, and to analyze our traffic. This helps us maintain the quality of our free resources. manage your preferences below.
Detailed Cookie Preferences
This helps support our free resources through personalized marketing efforts and promotions.
Analytics cookies help us understand how visitors interact with our website, improving user experience and website performance.
Personalization cookies enable us to customize the content and features of our site based on your interactions, offering a more tailored experience.
