An Adaptive Multi Objective Optimization Framework for Next Generation Resource Constrained Communication systems

未来五年，全球IP网络将支持多达100亿个新设备和连接，从2016年的163亿个增加到2020年底的363亿个。2020年以后的5G通信网络和服务环境，需要应对数十亿台物联网小设备，以及数十亿的重度数据消费者。5G网络基础设施必须提供多方面的安全态势，以减轻所有设备、所有网络域和所有应用程序（无论它们位于何处）的新威胁、恶意攻击和不断发展的漏洞。它必须是一个无缝的基础设施，可靠地满足每个人的安全通信需求，并集成新的无线电概念，如大规模MIMO、超密集网络、移动网络、设备对设备、超可靠和大规模机器通信。下一代通信安全约束求解器已成为一门重要的科学学科，其中许多多方面的复杂性值得跨学科工程社区的关注和协同。无线和有线网络基础设施不仅需要在容量方面具有高度可扩展性，而且还需要以最佳方式处理各种安全垂直领域的不同服务需求。在各方之间传输数据时采用的安全方法至关重要，涉及成本和效率之间的权衡。这种权衡的解决方案是使用安全数据通信的优化方法分析加密协议。但是，这些优化方法不能导致流程安全级别的显著降低。为了在安全需求的成本、效率和可靠性之间实现多目标权衡，我们的研究包括基于Benson算法的密码技术、基于亚置换的进化密钥交换、密码方法的二进制粒子群优化、自定义膨胀-收缩密码系统等相关方法。在许多安全网络领域（如电子银行、电子信托、电子医疗等），威胁级别是一个关键的驱动因素。每个安全通信系统都有一个功能安全级别的集合，保证了整个系统的保密性。我们提出的研究封装了新的优化方法，这些方法将用于开发先进的多目标技术，用于有效设计加密通信协议，并处理安全，成本和性能因素之间的权衡。在这项研究中，我们将开发理论和应用的技术和算法，用于开发通信安全管理和策略。

Over the next five years, global IP networks will support up to 10 billion new devices and connections, increasing from 16.3 billion in 2016 to 36.3 billion by the end of 2020. The 5G communication network and service environment beyond 2020 need to cope with billions of small devices in the Internet of Things (IoT), and billions of heavy data consumers. The 5G network infrastructure will have to provide a multi-faceted security posture that mitigates new threats, malicious attacks, and evolving vulnerabilities across all devices, all network domains, and all applications wherever they are hosted. It will have to be a seamless infrastructure satisfying everyone's secure communication needs reliably as well as integrating new radio concepts, such as massive MIMO, ultra-dense networks, moving networks, and device-to-device, ultra-reliable, and massive machine communications. Next-generation constraint solvers for communications security has emerged as an important scientific discipline where many multifaceted complexities deserve the attention and synergy of interdisciplinary engineering communities. Both wireless and wireline network infrastructure are required not only to be highly scalable in terms of their capacity, but also to optimally handle different service needs of various security verticals. Security methodologies employed in transporting data between parties are crucial and involve a trade-off between cost and efficiency. A solution to this trade-off is to analyze cryptographic protocols using optimization methods for secure data communication. However, these optimization methodologies must not result in significant reduction of security level of the process. To achieve a multi-objective trade-off between cost, efficiency and reliability of security requirements, our research includes methods related to Benson’s algorithm-based cryptographic techniques, Sub-permutation-based evolutionary key exchange, Binary particle swarm optimization for cipher method, Custom inflate-deflate cipher systems, etc. A crucial driving factor in many secure network fields (e.g. e-banking, e-trust, e-health etc.) is the threat level. Every secure communication system presents a collection of functional security levels, which guarantee the secrecy of the overall system. Our proposed research encapsulates new optimization approaches, which will be implemented to develop advanced multi-objective techniques for the efficient design of cryptographic communication protocols and to cope with the trade-offs between security, cost, and performance factors. In this research, we will develop techniques and algorithms, both theoretical and applied, for developing communication security management and policies.