1. What is the Problem (Need)? (a) Problem Statement: Design and implementation of AI based Adaptive Networks. (i) Pre-requisites. (a) Robust security measures to shield the Network from cyber threats to including firewalls, IDS, secure access cont, reg security audits and updates. (b) A well-defined infrastructure with adequate Bandwidth, a stable and high-performance internal Network and low latency for the optimal functioning. (c) Data Governance and Privacy Regulations must be strictly adhered to. Compliance with Regulatory Policies to ensure that sensitive info processed by the Network is handled responsibly. Adequate encryption integrity and trust be impl. (d) A comprehensive Backup & DR strat is of paramount imp given the critical nature of Adaptive Networks in various appls to safeguard against data loss and sys failures. Regular sch backups, redundant sys and efficient rec protocols are imperative to minimize downtime and ensure the continuity. (ii) Methodology. (a) Thorough assessment of the existing Nw infrastructure to incl eval of hardware capability Bandwidth, latency, overall Nw topology followed by a detailed risk assessment to identify potential vulnerabilities and security gaps for formulating a robust security strat tailored to the unique reqmts of an Adaptive Nw. (b) With the a/m groundwork in place, the impl involves integrating adaptive algorithms into the Nw architecture to incl use-case specific selection of appropriate machine learning models & algorithms and configuring them to adapt to changing conditions in real-time. Fine-tuning the algorithms and performance optimization using historical data or simulations is crucial. Continuous monitoring and feedback loops will allow the Adaptive Nw to learn and evolve over time. Further, regular updates and adjustments to the algorithms ensure they remain effective in dynamically changing environments. (b) Evolution of the Problem: An adaptive Network refers to a dynamic and responsive system that can adjust its parameters and behaviour in real-time based on changing conditions or inputs. These Networks often employ machine learning algorithms to optimize performance, allowing them to learn from experience and improve over time. The flexibility of Adaptive Networks makes them valuable where responsiveness to evolving scenario is paramount. (c) How is it overcome? : Non-AI based procedural mechanisms. (d) Any Innovations to Locally Overcome the Problem. Nil. 2. Who has the problem? (a) User: Network managers. (b) Operating Environment: Static and Mobile Headquarters. (c) Periodicity of Exploitation: Continuous. 3. Why is it important to solve? To achieve the benefits: - (a)Network Planning, Management with Predictive Maintenance: Enable enhanced predictive maint, optimizing Network performance, and automating fault detection and resolution. Intelligent traffic management and resource allocation, facilitated by Al algorithms, ensure efficient Network utilization. (b)Network Security: Threat detection, identification, segregation & mitigation along with Cyber Threat detection of anomalies and potential security threats within the network emanating from user end. Analysis of unusual traffic behavior to iden any breaches can be carried out. This predictive analysis helps SOC teams anticipate emerging threats and prioritize their security efforts accordingly. (c)Intelligent Traffic Management and Quality of Service (QoS) Improvement: Dynamically routing traffic based on real time conditions. AI analyses network traffic and user behavior to optimize QoS parameters, ensuring a better user experience by prioritizing critical services and applications. (d)Resource Management and Intelligent Resource Allocation: AI algorithms for assistance in optimizing resource allocation, such as bandwidth allocation, spectrum optimization, and energy consumption, leading to more efficient utilization of resources, Allocation of Network resources based on real- time demand and user requirement can be achieved. (e)Predictive Maint for Network Infrastructure: AI algorithms can analyze data from Network devices and sensors to predict potential failures or performance issues before they occur. This proactive apch be expl for preventive maint, minimizing downtime and improving overall Network reliability. (f) Network Planning and Expansion: Al-based predictive analytics for network expansion re-orientation by analysing data on user demand, traffic patterns, and geographic trends, enabling informed' decisions about infrastructure realignment/resource provision based on demand. (g) Data Loss Prevention (DLP) Policy Optimization: AI modules can analyse data flow within the Network and identify potential pts of data leakage. It can then generate optimised DLP policies that specify rules and restrictions for data access, transmission and storage to prevent unauthorised data exfiltration. (h) Network Traffic Visualization: AI algorithms can analyse Network traffic data and generate visualizations that provide insights into Network behaviour and potential security risks. These visualizations can help SOC analysts identify patterns, anomalies and areas of concern more effectively.