proactive network failure detection using machine learning


"Network and system security is of paramount importance in the present data communication environment. Hackers and intruders can create many successful attempts to cause the crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to prevent these threats are emerging together with the secure system evolution.A new approach to proactively maintain a network is described. This approach has been applied to the detection and prediction of faults in networks. A windowing technique was applied to large volumes of diagnostic data and this data was analyzed by machine learning methods. A set of conditions is to be found that cause failure and are likely to continue in the immediate future without diagnosis and repair. Moreover a few conditions have been found that are predictive of problems that affect the network. Such analyses over the complete network are helpful in proactively maintaining the network and in spotting trends for circuit problems. Proactive failure detection of the network can help in enhancing the quality of a system by recognizing conceivably difficult issues in advance. This project will calculate the probability of occurrence of an issue by using Naive Bayes algorithm. The proposed method will send alerts to the admin so that a corrective action can be taken and also notify the users regarding the same"



naïve bayes


machine learning


₹12000 (INR)