International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | The Self-Healing Shield: An Adaptive Network Security Framework Based on Artificial Intelligence |
|---|---|
| ABSTRACT | The escalating sophistication and dynamism of cyber threats have rendered traditional, signature-based, and perimeter-centric network security models increasingly obsolete. These static defenses struggle against zero-day exploits, polymorphic malware, and coordinated, multi-stage attacks that evolve in real-time. This research paper proposes, designs, and evaluates a novel Adaptive Network Security Framework (ANSF) grounded in Artificial Intelligence (AI) to enable proactive, intelligent, and self-healing defense mechanisms. The core of the ANSF is a closed-loop, cognitive architecture that integrates three AI-driven layers: (1) a Heterogeneous Multimodal Sensing Layer employing deep learning for anomaly detection across network traffic, host logs, and encrypted flow metadata; (2) a Cognitive Analysis and Fusion Layer that utilizes ensemble ML models and graph neural networks (GNNs) to correlate disparate alerts, perform attack graph reconstruction, and infer attacker intent and TTPs (Tactics, Techniques, and Procedures); and (3) an Autonomous Response Orchestration Layer powered by Reinforcement Learning (RL), which dynamically selects and deploys optimal countermeasures (e.g., micro-segmentation, deception, resource reallocation) while minimizing operational disruption. A key innovation is the framework's continuous learning feedback loop, where the outcomes of automated responses are used to retrain detection and response models, creating a virtuous cycle of adaptation. We implemented a prototype ANSF in a simulated high-fidelity network testbed emulating a corporate environment. The evaluation involved a campaign of multi-vector attacks, including Advanced Persistent Threat (APT)-style lateral movement and DDoS (Distributed Denial of Service) attacks. Compared to a state-of-the-art traditional Security Information and Event Management (SIEM) with static rules, the ANSF demonstrated a 45% higher true positive rate in detecting novel attack vectors and reduced the mean time to respond (MTTR) from 18 minutes to 94 seconds. The RL-based orchestrator successfully contained 92% of simulated breaches within their initial subnet, preventing lateral spread. Furthermore, the framework showed a 60% reduction in false positive alerts after an initial learning period, significantly alleviating alert fatigue. The results validate that an AI-centric, adaptive framework can fundamentally shift network security from a reactive, human-paced paradigm to a proactive, machine-speed defensive posture. The paper concludes by discussing integration challenges with legacy systems, the critical importance of explainable AI (XAI) for security analyst trust, and the ethical considerations of autonomous cyber response. |
| AUTHOR | DR ADILAKSHMAMMA T Associate Professor, Master of Computer Applications, Koshys Institute of Management Studies, Autonomous, Bangalore, Karnataka, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402020 |
| pdf/20_The Self-Healing Shield An Adaptive Network Security Framework Based on Artificial Intelligence.pdf | |
| KEYWORDS | |
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