Authors : Rahul Ranjan; Dr. Ram Keshwar Prasad Yadav

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/345shmfj

Scribd : https://tinyurl.com/23vkcpe3

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT246

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : A Decision Framework for Enhancing Network Stability and Security. In the modern digital era, the growing complexity of networks and increasing frequency of cyber threats demand robust strategies for ensuring network stability and security. This paper proposes a decision framework that integrates real-time analytics, proactive monitoring, and adaptive response mechanisms to enhance network resilience against failures, attacks, and performance degradation. The framework leverages advanced machine learning algorithms, network flow analysis, and security protocols to dynamically adjust to network conditions and mitigate risks .The decision framework operates in two key dimensions: Stability: It focuses on ensuring uninterrupted network performance by optimizing resource allocation, traffic management, and fault tolerance mechanisms. By analyzing network traffic patterns and identifying potential bottlenecks or vulnerabilities, the framework makes proactive decisions to reroute traffic, adjust bandwidth, and prioritize critical data flows.Security: The framework enhances security by detecting potential threats, such as Distributed Denial of Service (DDoS) attacks, unauthorized access, or malware propagation. Using a combination of intrusion detection systems (IDS), firewalls, and behavioral anomaly detection, it identifies threats in real-time and implements automatic countermeasures, such as isolating affected network segments, patching vulnerabilities, or blocking malicious traffic.A key innovation of this framework is its use of multi-criteria decision-making (MCDM) techniques to balance trade-offs between network performance and security in real time. The model continuously evaluates factors such as latency, throughput, and risk exposure to make informed, optimal decisions that ensure both stability and protection. Furthermore, the framework adapts to evolving network conditions using reinforcement learning, allowing it to learn from past incidents and improve its decision-making over time.Simulation results demonstrate that the proposed framework significantly reduces network downtime, improves threat detection response times, and mitigates the impact of security breaches. This decision framework presents a scalable solution for modern, dynamic networks, offering enhanced protection while maintaining high performance in the face of complex challenges.

Keywords : Network Stability, Network Security, Decision Framework, Machine Learning, Real-Time Analytics, Multi- Criteria Decision making, Intrusion Detection, Adaptive Response.

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