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@article{180890,
        author = {Lakshya Rupesh Kucheriya and Clayton Mervin Pereira and Paras Mahajan and Anushka Raj Yadav},
        title = {Advancements in Intelligent Network Systems: Security, Automation, and Resilient Architectures},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3025-3034},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180890},
        abstract = {Recent advancements in intelligent network systems integrate artificial intel- ligence to enhance security, automation, and resilience. This paper analyzes AI-driven innovations including generative adversarial networks for threat detec- tion (achieving 95% malware identification accuracy), Zero Trust architectures adopted by 86.5% of enterprises, and intent-based automation reducing tele- com energy consumption by 4%. Resilient architectures combine multi-layer redundancy with edge-5G convergence, demonstrating 65% latency reduction in real-time IoT applications. Deep learning models now classify encrypted traffic with 94.22% accuracy while maintaining low-latency service levels. The study identifies three critical challenges: mitigating AI model drift affecting 44% of secu- rity systems, standardizing multivendor automation interfaces, and preparing for post-quantum cryptographic transitions by 2028. These advancements tran- sition networks from static infrastructures to self-optimizing ecosystems capable of autonomous threat response and predictive maintenance. Implementation strategies are validated through case studies from telecom operators and cloud providers, demonstrating 10x reliability improvements in AI cluster training environments [1].},
        keywords = {Intelligent Networks, Cybersecurity, Network Automation, Resilient Architectures, Artificial Intelligence},
        month = {June},
        }