Search engines are the instruments for website navigation and search, because the Internet is big and has expanded greatly. By continuously downloading web pages for processing, search engines provide search facilities and maintain indices for web documents. Online crawling is the term for this process of downloading web pages. This paper proposes solution to network traffic problem in migrating parallel web crawler. The primary benefit of a parallel web crawler is that it does local analysis at the data's residence rather than inside the web search engine repository. As a result, network load and traffic are greatly reduced, which enhances the performance, efficacy, and efficiency of the crawling process. Another benefit of moving to a parallel crawler is that as the web gets bigger, it becomes important to parallelize crawling operations in order to retrieve web pages more quickly. A web crawler will produce pages of excellent quality. When the crawling process moves to a host or server with a specific domain, it begins downloading pages from that domain. Incremental crawling will maintain the quality of downloaded pages and keep the pages in the local database updated. Java is used to implement the crawler. The model that was put into practice supports all aspects of a three-tier, realtime architecture. An implementation of a parallel web crawler migration is shown in this paper. The method for efficient parallel web migration detects changes in the content and structure using neural network-based change detection techniques in parallel web migration. This will produce highquality pages and detection for changes will always download new pages. Either of the following strategies is used to carry out the crawling process: either crawlers are given generous permission to speak with one another, or they are not given permission to communicate with one another at all. Both strategies increase network traffic. Here, a fuzzy logic-based system that predicts the load at a specific node and the path of network traffic is presented and implemented in MATLAB using the fuzzy logic toolbox.

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Fog computing acts as an intermediate component to reduce the delays in communication among end-users and the cloud that offer local processing of requests among end-users through fog devices. Thus, the primary aim of fog devices is to ensure the authenticity of incoming network traffic. Anyhow, these fog devices are susceptible to malicious attacks. An efficient Intrusion Detection System (IDS) or Intrusion Prevention System (IPS) is necessary to offer secure functioning of fog for improving efficiency. IDSs are a fundamental component for any security system like the Internet of things (IoT) and fog networks for ensuring the Quality of Service (QoS). Even though different machine learning and deep learning models have shown their efficiency in intrusion detection, the deep insight of managing the incremental data is a complex part. Therefore, the main intent of this paper is to implement an effective model for intrusion detection in a fog computing platform. Initially, the data dealing with intrusion are collected from diverse benchmark sources. Further, data cleaning is performed, which is to identify and remove errors and duplicate data, to create a reliable dataset. This improves the quality of the training data for analytics and enables accurate decision making. The conceptual and temporal features are extracted. Concerning reducing the data length for reducing the training complexity, optimal feature selection is performed based on an improved meta-heuristic concept termed Modified Active Electrolocation-based Electric Fish Optimization (MAE-EFO). With the optimally selected features or data, incremental learning-based detection is accomplished by Incremental Deep Neural Network (I-DNN). This deep learning model optimises the testing weight using the proposed MAE-EFO by concerning the objective as to minimise the error difference between the predicted and actual results, thus enhancing the performance of new incremental data. The validation of the proposed model on the benchmark datasets and other datasets achieves an attractive performance when compared over other state-of-the-art IDSs.