Intelligent Agent for AI-Based Network Troubleshooting, Predictive Diagnosis and SelfHealing for Reliable Connectivity

International Journal of Inventive Engineering and Sciences (IJIES) ISSN: 2319-9598 (Online), Volume-12 Issue-10, October 2025 11 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number: 100.1/ijies.H111212080825 DOI: 10.35940/ijies.H1112.12101025 Journal Website: www.ijies.org Intelligent Agent for AI-Based Network Troubleshooting, Predictive Diagnosis and SelfHealing for Reliable Connectivity Monisha D. R., Poornima Devi M. Abstract: This paper presents an intelligent AI-enabled chatbot system that will automatically solve network issues, allow predictive diagnostics, and launch self-healing activities to provide stable and reliable connections. The chatbot employs an improved Natural Language Processing (NLP) that listens and answers user queries in real-time, providing instant support for frequent and intricate network troubles. One of the central parts of the system is a predictive analysis engine fueled by a set of machine learning and neural networks trained on a historical network log and performance data. This engine knows where to look to detect trends that indicate a possible failure or deterioration in upstream network speed. It enables the chatbot to warn users and offer tips on preventive actions before a more significant problem occurs. In addition, it is supported by a self-healing module that works with automated scripts and orchestration tools to run pre-configured recovery procedures, such as restarting a service or reconfiguring a component, without human involvement. Keywords: Chatbot, AI, NLP, Predictive Diagnosis, Network Troubleshooting, Self-Healing, Machine Learning, Network Automation. Nomenclature: NOCs: Network Operation Centres AI: Artificial Intelligence NLP: Natural Language Processing MTTR: Mean Time to Repair NMS: Network Management Systems I. INTRODUCTION In today's hyper-connected digital world, computer network performance and reliability are decisive factors in the success of organisations across all industries. With the increasing complexity of networks —i.e., their ability to support cloud computing, IoT devices, and large volumes of data traffic —the challenge of keeping networks highly connected with minimal downtimes, which translates into proactive issue resolutions promptly, has become harder to manage in the traditional, manual manner. Manuscript received on 28 July 2025 | First Revised Manuscript received on 12 August 2025 | Second Revised Manuscript received on 06 September 2025 | Manuscript Accepted on 15 October 2025 | Manuscript published on 30 October 2025. *Correspondence Author(s) Monisha D. R.*, Department of Computer Science, Sri Ramachandra Institute of Higher Education, Chennai (Tamil Nadu), India. Email ID: [email protected] Poornima Devi M., Department of Computer Science, Sri Ramachandra Institute of Higher Education, Chennai (Tamil Nadu), India. Email ID: [email protected] © The Authors. Published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/ Network managers usually face the problem of slow diagnosis, reactive troubleshooting and a lack of ability to prevent failures proactively. This paper will propose an innovative solution to these issues through a clever chatbot mechanism that uses Artificial Intelligence (AI) to transform network operations by automating troubleshooting, prediction, and self-healing processes. The combination of Natural Language Processing (NLP), machine learning algorithms, and automation tools can create a chatbot that acts as a virtual network assistant. It helps users interact with the system, analyse network information, and predict and perform corrective actions, all requiring only a few human interactions. This AI solution provides network administrators with enough data to troubleshoot problems faster, minimise mean time to repair (MTTR), and, by extension, significantly enhance the network's reliability and resilience. Moreover, the chatbot can be educated on past incidents, improve its knowledge base in the future, and adjust to new network sources; hence, it is scalable and future-proof for modern network infrastructure [1]. Alongside the convergence of AI and network management comes an increase in operational efficiency, setting the stage for another generation of autonomous and self-managed networks. Using AI technologies along with automation, this chatbot becomes a formidable weapon for network administrators, enhancing responsiveness, reliability, and aiding the development of autonomic network systems. It not only simplifies network operations but also adjusts and learns from previous incidents to ensure continuous progress and growth, preparing for the future needs of the networks. The chatbot is also linked to a centralised database, which is an ever-changing knowledge base where cases and responses are fed back to enhance it continuously. The system is to be implemented either in network operation centres (NOCs) or in user support platforms, enhancing user experience and improving operational efficiency. The solution suggested with the help of AI minimises downtime, speeds up incident response, and aligns with the vision of self-managing, autonomous network systems. II. LITERATURE REVIEW A growing body of research supports the development of intelligent chatbots for network troubleshooting and predictive maintenance. Several key trends and findings from existing literature are outlined below: NLP and Chatbots in IT Support: Early implementations of chatbots in IT environments have Intelligent Agent for AI-Based Network Troubleshooting, Predictive Diagnosis and Self-Healing for Reliable Connectivity 12 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number: 100.1/ijies.H111212080825 DOI: 10.35940/ijies.H1112.12101025 Journal Website: www.ijies.org shown significant promise in improving response time and reducing support costs. Reddy et al. [2] have examined the possibility of using NLP-based bots to answer the routine IT questions effectively. A. Predictive Analytics in Networks: While Sharma et al. [3] have shown how machine learning algorithms such as Random Forests and LSTM can be used to predict network failures, they investigate how to identify the likelihood of network slowness using historical logs and performance data. B. Network Automation via Reinforcement Learning: Kim and Park [4] offered reinforcement learning-based approaches to representing network decisions in real-time routing and network resource allocation. C. Self-Healing Network Systems: Zhang et al. [5] discussed research on self-healing network systems capable of diagnosing and repairing faults without the need for human assistance. Table I: System Purpose Mapping Component Purpose Chatbot Interface Allows users to interact using text or voice NLP Engine Understands and processes user queries Intent Classifier Identifies the user's intent from the query Knowledge Base Stores network information and solutions Diagnosis Module Detects and diagnoses network issues Predictive Analytics Predicts possible failures in advance Self-Healing System Automatically fixes detected problems. Feedback System Learns from past interactions to improve performance Monitoring System Tracks network status in real-time Security Layer Protects the system from unauthorized access Table. II: Comparative Analysis Feature Proposed Chatbot System Cisco DNA Centre IBM Watson AIOps Tradition al Helpdesk User Interface Conversation al (Chatbot) Dashboardbased Dashboardbased Human interaction Predictive Diagnostics Yes (Random Forest Model) Yes (AI/MLbased) Yes (AI/ML with AIOps) No Self-Healing Capability Script-based automation Yes Yes Manual Class Imbalance Handling SMOTE applied Not specified Not specified Not applicable Accuracy (Test Scenario) ~90.5% Not publicly disclosed Not publicly disclosed Varies Scalability High (Cloud & Modular Codebase) High High Limited Cost Low (Opensource tools) Enterprise License Enterprise License Staffbased Real-Time Feedback Yes (Instant Solutions) Yes (Monitoring Focused) Yes Delayed (Queuebased) Learning Capability Supervised ML + Rule Engine Reinforceme nt + ML Unsupervis ed + ML None Customizati on for Users Easy (Edit rules or models) Moderate Moderate High (Humandependent ) III. METHODOLOGY The methodology for AI Network Troubleshooting involves a systematic approach to collecting data, training AI models, and integrating intelligent diagnostics into a network management framework. The key steps include: A. Data Collection Gather network data from various sources such as logs, SNMP traps, NetFlow, and system performance metrics. Include historical data of faults, outages, and anomaly events. Use tools like Wireshark, Cisco Packet Tracer, or real-time monitoring platforms for simulation or live capture. B. Data Preprocessing Clean and normalize raw data to handle noise, missing values, or inconsistencies. Convert data into structured formats suitable for training (e.g., CSV, JSON). Label datasets where supervised learning is required (e.g., “normal” vs. “faulty” traffic). C. Feature Engineering Extract key features such as latency, bandwidth usage, packet loss, CPU/memory utilization, etc. Perform dimensionality reduction (e.g., PCA) to focus on the most relevant metrics. D. Model Selection and Training Select appropriate AI/ML algorithms: Classification models (e.g., Random Forest, SVM) for fault detection. Clustering models (e.g., K-Means, DBSCAN) for anomaly detection. [Fig.1 AI Reliable Connectivity] Time-series models (e.g., LSTM, ARIMA) for predicting network failures. International Journal of Inventive Engineering and Sciences (IJIES) ISSN: 2319-9598 (Online), Volume-12 Issue-10, October 2025 13 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number: 100.1/ijies.H111212080825 DOI: 10.35940/ijies.H1112.12101025 Journal Website: www.ijies.org Train models using training datasets and validate using testing datasets. Use cross-validation and hyperparameter tuning to optimize performance. E. Anomaly Detection and Fault Diagnosis Deploy trained models to monitor live or simulated network data. Detect deviations from normal behavior and trigger alerts. Classify issues (e.g., bandwidth overload, hardware failure, DDoS attack). F. Automated Troubleshooting and Recommendation Implement logic or AI-based systems to suggest or automatically execute solutions (e.g., rerouting traffic, restarting services). Integrate with network management systems (NMS) for seamless actions. G. Evaluation and Testing Test the accuracy, speed, and reliability of the AI system. Compare with traditional troubleshooting methods. Use performance metrics such as precision, recall, F1 score, and latency of issue resolution. H. Deployment and Monitoring Deploy the system in a controlled or live network environment. Continuously monitor and retrain models as network behaviour evolves. Mathematical Formulation: Random Forest Prediction 𝑦 = 𝑚𝑜𝑑𝑒 {ℎ𝑡 (𝑥)| 𝑡 = 1, 2 ,… …𝑇}𝑊ℎ𝑒𝑟𝑒 ℎ𝑡 (𝑥) 𝑖𝑠 𝑡ℎ𝑒 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑡𝑡ℎ 𝑡𝑟𝑒𝑒 SMOTE Resampling 𝑥 = 𝑥𝑖 + 𝜆. (𝑥𝑛𝑛 − 𝑥𝑖)𝑊ℎ𝑒𝑟𝑒 𝜆 ∈ [0,1]𝑖𝑠 𝑎 𝑟𝑎𝑛𝑑𝑜𝑚 𝑣𝑎𝑙𝑢𝑒 , 𝑥𝑖 𝑖𝑠 𝑎 𝑚𝑖𝑛𝑜𝑟𝑖𝑡𝑦 𝑠𝑎𝑚𝑝𝑙𝑒, 𝒂𝒏𝒅 𝒙𝒏𝒏 𝒊𝒔 𝒐𝒏𝒆 𝒐𝒇 𝒊𝒕𝒔 𝒏𝒆𝒂𝒓𝒆𝒔𝒕 𝒏𝒆𝒊𝒈𝒉𝒃𝒐𝒓𝒔 Classification Accuracy = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 +𝐹𝑁 Cross-Validation Score 𝑠 = 1 𝑘∑𝑆𝑖 𝑘 𝑖=1 Where 𝑆𝑖 is the score on the 𝑖𝑡ℎ 𝐹𝑜𝑙𝑑. Probability Estimation (Logistic Function) P ( 𝑦 = 1 | 𝑥 ) = 1 1 + 𝑒−𝑧 𝑊ℎ𝑒𝑟𝑒 𝑧 = 𝑤𝑇𝑥 + b IV. RESULT [Fig.2: Cross-Validation] [Fig.3: Cross-Validation] Table III: Evaluation Model Accuracy 90.5% (Cross-Validated) Response Time Instant (for known issues) Scalability This approach supports dynamic issue learning and can be extended with unsupervised clustering for anomaly detection. User Satisfaction The service improved due to 24/7 availability and fast resolutions. V. CONCLUSION AI Network Troubleshooting presents a transformative approach to managing the complexity of modern network infrastructures. By leveraging artificial intelligence and machine learning, organizations can automate the detection, diagnosis, and resolution of network issues with greater speed and accuracy than traditional methods. This not only reduces downtime and operational costs but also enhances network reliability and user experience. The integration of AI enables proactive monitoring, realtime anomaly detection, and predictive fault management— key factors in building intelligent, self-healing networks. Although challenges such as data quality, model interpretability, and deployment complexity remain, ongoing advancements in AI technologies continue to bridge these gaps. Overall, AI-driven troubleshooting is a forward-looking solution that aligns with the evolving demands of dynamic and large-scale network environments, offering a more innovative and more efficient alternative to conventional troubleshooting techniques.3 DECLARATION STATEMENT After aggregating input from all authors, I must verify the accuracy of the following information as the article's author. ▪ Conflicts of Interest/ Competing Interests: Based on my understanding, this article has no conflicts of interest. ▪ Funding Support: This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted with objectivity and without any external influence. ▪ Ethical Approval and Consent to Participate: The content of this article does not necessitate ethical approval or consent to participate with supporting documentation. ▪ Data Access Statement and Material Availability: The adequate resources of this article are publicly accessible. ▪ Author’s Contributions: The authorship of this article is contributed equally to all participating individuals. REFERENCE 1. Zhang, Y., Chen, M., & Liu, J. (2021). AI-based Self-Optimisation in 5G Networks: From Fault Detection to Root Cause Analysis. IEEE Wireless Communications, 28(2), 56-63. DOI: https://doi.org/10.1109/MWC.001.2000242 2. Imran, A., Zoha, A., & Abu-Dayya, A. (2014). Challenges in 5G: How to Empower SON with Big Data for Enabling 5G. IEEE Network, 28(6), 27-33. 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DOI: https://doi.org/10.1109/MNET.001.1900465. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of the Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)/ journal and/or the editor(s). The Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.