MLIDS: Revolutionizing of IoT based Digital Security Mechanism with Machine Learning Assisted Intrusion Detection System

It is becoming more critical to detect intrusions on these devices due to the exponential growth of the Internet of Things (IoT) and the accompanying explosion in the number of IoT devices. In order to create intrusion detection systems that really work, researchers are utilizing machine learning methods. Machine Learning Assisted Intrusion Detection System (MLIDS) is a new intrusion detection system that we provide in this study. It efficiently identifies network unusual traffic. A cross-validation test using the traditional learning model XGBoost allows for a transparent evaluation of the suggested algorithm's performance. Next, the preprocessed data is categorized using the suggested MLIDS and XGBoost methods. To get the best detection performance, the model's hyperparameters are tuned using optimization logic. The evolution of cybercrime has necessitated massive advancements in intrusion detection system (IDS) technology. In order to gain access to our computers' private data, hackers nowadays deploy a wide variety of techniques. To protect against these threats, there are a plethora of intrusion detection algorithms. There are growing worries over the secure communication and protection of digital information due to the exponential expansion and usage of the internet. To gain useful information, hackers nowadays deploy a wide variety of techniques. Those various assaults may be detected with the use of several intrusion detection algorithms, methods, and approaches. The overarching goal of this paper is to present a comprehensive analysis of intrusion detection systems, including but not limited to: different types of intrusion identification techniques, types of events, a number of approaches, and tools, future research requirements, difficulties and, subsequently the development of an IDS device for research purposes that can detect and prevent intrusions.