# A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments
## 1 Introduction  
The security of networks is very important as they are widely used to provide communication and information-sharing among individuals, industries, and other parts of society. Various strategies in the form of firewall protection policies and antivirus software have been put into practice for maintaining client security and the protection of their sensitive data [1].  
The industry protected intrusion detection system (IDS) recognizes threats or suspicious activities on the network flows by analyzing network traffic or a specific perceived environment. One type of anomaly in a network is an intrusion or threat. Interlopers exploit network vulnerabilities by misusing network faults that bring about a breach of network security and software bugs such as overflowing of the buffer. Interlopers target low privilege clients and expect to find additional access power or programmers that are common web-based clients to hack or damage sensitive data [2].  
Although several industry protected IDS algorithms have been proposed by network researchers, most of them are based on traditional mechanisms such as encryption andauthentication procedures to protect wireless sensor units and are insufficient. Hence, the existing IDS needs a lot of progressive work to achieve high-performance authentication and security. The increased use of the artificial intelligence-machine learning ensemble technique in industries to classify multi-pattern data was the motivation for this research work to help in the recognition of traffic patterns and the methods of anomaly detection over internet-enabled wireless sensors. There is a growing use of the ensemble techniques of machine learning, a branch of artificial intelligence, and their ability to classify multi-pattern data across industries applications. Accordingly, this research has also applied machine learning ensemble techniques to IDS, so this work is focused on traffic pattern recognition and anomaly detection on Internet-enabled wireless sensors.  
To detect novel attacks, the proposed intrusion detection system handles the recently recorded benign information in data packets and analyzes the network traffic for anomalous data flows.  
Intrusion detection techniques are categorized into two types: signature-based and anomaly-based. In signature-based intrusion detection techniques, the system monitors the flow of packets within the network and compares the value obtained with previously identified values which are configured as the signature of known attacks. The anomaly-based method detects attacks by capturing user parameters that show deviation from the legitimate user parameters [3].  
The major contributions of this research paper are as follows:  
* A focus on mobile network appliances such as IoT-enabled platforms for IDSs.
* A machine learning-based binary classifier for identifying network traffic as benign or malicious to provide network security.
* Training, validating, and testing of the model using the random forest and gradient boosting machine (GBM) ensemble approach with a hyperparameter optimizer using the 'CSE-CIC-IDS2018-V2' dataset and demonstrating the performance test with attack categories like infiltration, SQL injection, etc.  
This paper consists of six sections. In Section 2, the focus is on the background of the present research and its challenges. Section 3 covers the system architecture of the proposed work. Section 4 summarizes the dataset used for training, validating, and testing of the model to build a binary classifier for IDS. Section 5 of this paper includes the demonstration of the methodology and its results. Section 5 presents the conclusions derived from the proposed work.