Abstract

Abstract - Enterprise networks are vital infrastructures that need to be continuously watched after to guarantee excellent performance and availability. Conventional defect detection techniques frequently depend on reactive monitoring or manual intervention, which can result in extended outages and decreased productivity. In order to proactively discover and address network abnormalities, this article suggests an intelligent fault detection system that makes use of predictive analytics and Python-based automation. The suggested approach facilitates improved network resilience and quicker fault resolution by combining automation frameworks, real-time data processing, and machine learning algorithms. When compared to traditional methods, our methodology dramatically lowers mean time to detection (MTTD) and mean time to resolution (MTTR), according to experimental results from simulated business environments.

Keywords

Fault Detection, Enterprise Networks, Predictive Analytics, Network Monitoring, Python Automation, Machine Learning, Anomaly Detection, Proactive Maintenance, Network Reliability, AI in Networking.

Conclution

A. An overview of the results

B. Advantages of Using Predictive Analytics and Automation Together

For enterprise network management, the integration of automation and predictive analytics offers a number of clear benefits. The capacity to identify errors early on, before they become serious problems, is one of the main advantages. By examining past data, predictive models can spot trends and foresee network outages, enabling administrators to take preventative measures. This is further improved by automation, which enables instantaneous defect mitigation by doing away with the need for personal intervention. For instance, automated scripts can be set up to change the device's configuration or isolate the problematic component without human intervention if a network device is found to be malfunctioning or misconfigured. This improves overall network reliability, decreases human error, and expedites response times. The system also gets better at identifying flaws that were previously invisible by continuously learning from fresh data, which enhances the network's capacity to self-correct and adjust to shifting circumstances. In conclusion, integrating automation and predictive analytics increase’s fault detection while also streamlining network operations, cutting expenses, and increasing network uptime.

C. Limitations and Challenges

The suggested system offers notable improvements, however there are still a number of drawbacks and difficulties. The reliance on high-quality, labelled training data is one of the main drawbacks. Large datasets that precisely reflect every potential failure state are necessary for supervised learning models to function well, but these datasets aren't usually accessible in business settings. Furthermore, real-time processing may occasionally be difficult due to the massive amount of data produced by enterprise networks, particularly in large-scale deployments. Even though time-series data can be accurately predicted by machine learning models like LSTM, these models are computationally demanding and might not be appropriate for all network setups, especially those with limited resources. Integrating the technology with legacy network management infrastructure presents another difficulty. Although the system was intended to be modular, it might be difficult to adapt to current systems that have different protocols, interfaces, and formats. Lastly, there are some hazards associated with the system's reliance on automation, especially if the automated remediation scripts are not adequately designed or verified. Unintentional outages may result from improper acts, such as isolating a crucial network path or changing the incorrect device. Strong testing, validation, and fail-safes in the automated processes are therefore required.

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