Due to the epidemic, the majority of users and businesses turned to the internet, necessitating the necessity to preserve the populace and safeguard their data. However, after being attacked, the expense of data protection runs into the millions of dollars. The phrase "Protection is better than cure" is true. The paper deals with profiling the node for safeguarding against the cyberattack. There is a lot of research on network nodes. Here, we address the requirement to profile the node before utilizing machine learning to separate the data. In order to scan the nodes for risks and save the nature of threat as a database, node profiling is being investigated. The data is then classified using a machine learning algorithm utilizing the database. This research focuses on the application of machine learning methods, specifically Gaussian Naive Bayes and Decision Trees, for the segmentation of cyberattacks in streaming data. Given the continuous nature of cyberattack data, Gaussian Naive Bayes is introduced as a suitable approach. The research methodology involves the development and comparison of these methods in classifying detected attacks. The Bayesian method is employed to classify detected attacks, emphasizing the use of Gaussian Naive Bayes due to its adaptability to streaming data. Decision Trees are also discussed and used for comparison in the results section. The research explores the theoretical foundations of these methods and their practical implementation in the context of cyberattack classification. After classification, the paper delves into the crucial task of identifying intrusions in the streaming data. The effectiveness of intrusion detection is highlighted, emphasizing the importance of minimizing false negatives and false positives in a real-world cybersecurity setting. The implementation and results section presents empirical findings based on the application of Gaussian Naive Bayes and Decision Trees to a dataset. Precision, recall, and accuracy metrics are used to evaluate the performance of these methods. The research concludes by discussing the implications of the findings and suggests that Gaussian Naive Bayes is a suitable choice for streaming data due to its adaptability and efficiency. It also emphasizes the need for continuous monitoring and detection of cyberattacks to enhance overall cybersecurity. The paper provides insights into the practical applicability of these methods and suggests future work in the field of intrusion detection.

The internet has grown in leaps and bounds and hence all the data is now available online; be it shopping, banking, private and public institutes or universities, private public sectors are all making their presence felt online. The online data is just a click away thanks to ubiquitous systems today. The browser does not require any specific program set up hence easier for the end user. Earlier the data online was static used HTML now it’s dynamic uses ASP, ASP.NET, Servlet, JSP and other operational tools therefore the internet operation is broken down into many categories. The problem arises with the customer while trying to buy something online. There are lots of online stores sometimes it’s difficult to browse through all products to get a better deal. The pricing of products are different on different sites, this is the first gap at the customer end. The second problem arises at the provider end. The second gap here is to understand the customer need.  How can the variation in prices be checked? ; The existing prices available on sites cannot be changed but the customer can be provided with options to select the best deal of the same product. For the first problem the paper deals with an API implementation wherein the information of at least some products is compared under one roof. How can the provider know the genuine customer? ; The second problem is resolved by the use of SVM. Last problem is in detecting if a customer visiting a site will actually buy the product being compared.

The paper focuses on the selection of ASP.NET to deal with the implementation problems stated and find solution to the forecasting problem using SVM. SVM and C4.5 are used for comparison.