Enhancing Information Security in Multimedia Streams Through Logic Learning Machine Assisted Moth-flame Optimization

Enhancing information security in multimedia streams is a critical endeavor in the digital age, where data breaches and cyber threats loom large. This research proposes a novel approach by integrating Logic Learning Machines (LLMs) with Moth-Flame Optimization (MFO) to fortify the defenses of multimedia data against potential vulnerabilities. Logic Learning Machines, known for their ability to make decisions based on logical reasoning, form the foundational intelligence of our proposed system. Leveraging their capacity to process complex patterns and relationships within data, LLMs become the cognitive backbone of our security enhancement model. Moth-Flame Optimization, inspired by the navigational behavior of moths around artificial lights, serves as the optimization engine in this framework. MFO mimics the natural attraction of moths to flames, translating it into an algorithmic strategy to optimize parameters and configurations for heightened security measures. By applying MFO, the system dynamically adapts and refines its security protocols in response to evolving threats. The synergy between LLMs and MFO creates a resilient defense mechanism for multimedia streams. The logic-driven decision-making of LLMs is augmented by the adaptive optimization capabilities of MFO, resulting in a robust and dynamic security infrastructure. This fusion not only enhances the detection of potential threats but also enables proactive adjustments to security parameters, thereby fortifying the system against emerging risks. The proposed framework is validated through extensive simulations and experiments, demonstrating its efficacy in real-world scenarios. The outcomes showcase improved information security for multimedia streams, providing a versatile solution for safeguarding sensitive data in diverse digital environments.