Intelligent Intrusion Detection Model with MapReduce and Deep Learning Model

Cybersecurity has become crucial for defending networks from various cyberattacks. A conventional Intrusion Detection System (IDS) is crucial to contemporary security. However, there are limits to how intelligently it can analyse massive amounts of data in order to spot an abnormality. The MapReduce-Based Improved Deep Learning Model for ID (MR-IDLM) is a technique that may be used to intelligently automate ID. It is closely related to deep learning (DL). The DL method is employed for detecting intrusions accurately. In this study, MR-IDLM is proposed to identify network intrusions that include several data categorization jobs. The proposed MR-IDLM efficiently uses commodity technology to analyse large data volumes. According to this study’s proposed methodology, the MR-IDLM can identify intrusions by making educated guesses about hypothetical test cases and then saving that information to a database in order to prevent duplicate entries. The proposed model outperforms previously reported techniques with a detection accuracy of 100%. Since mapreduce and other preprocessing stages are added the proposed model provides these results. In future, efforts will be made to move this work in real-time.