# Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm
## 1 Introduction  
With the increasing risks in network security, the implementation of effective intrusion-detection mechanisms has become a crucial strategy for safeguarding computer systems and network security [1; 2; 3; 4]. Traditional intrusion-detection methods heavily rely on known attack patterns and behaviors, acquired through expert knowledge or historical data. Consequently, their effectiveness in detecting new and unknown attack methods is limited [5; 6]. In addressing this issue, decision tree algorithms [7], in the form of attribute splitting, have improved the efficiency of intrusion detection by classifying network behaviors and determining their involvement in the intrusion process. However, these methods often neglect the aspect of detection accuracy. Support Vector Machines, on the other hand, excel in intrusion detection with superior accuracy and classification efficiency when dealing with a limited-data experience. However, they face significant challenges in encoding and normalizing data, particularly with the emergence of novel unknown attack methods, making them less suitable for the evolving landscape of information technology [8]. K-means clustering, known for its speed and interpretability, has also found its place in intrusion detection. It excels in grouping similar instances into subsets for intrusion determination. However, it exhibits sensitivity to outliers and the difficulty of determining the optimal K value [9]. Currently, with the development of new information technologies, such as the Internet of Things, cloud computing, and big data, the issue of information security is increasingly emphasized. In order to guarantee the security of data, a large number of models forclassification and avoidance, facilitated by focusing on the behavior of different nodes, have been generated, such as the Enhanced Random Forest Classifier (ERF-KMC) with the K-mean clustering algorithm, which helps to accurately classify the attacks so that the system can more accurately block the attacking information and protect the security of the information [10], and the Improvement of Mutant Bald Eagle Search (IMBES) optimizer optimization of the Kernel Extreme Learning Machine (KELM) model, which predicts data information as points and analyzes parameter confidence intervals through point density metrics as system security intervals and reduces the interference caused by errors [11]; encrypting traditional standard protocols through elliptic curve encryption technology, which guarantees the network and data security while meeting the network time constraints, is also a way to solve the problem [12]. And the intrusion-detection mechanism as the second security protection mechanism, through the real-time monitoring of the network, can ensure the security of network resources at the same time but also effectively reduces the losses caused by network attacks. In contrast, the Random Forest algorithm stands out as a favorable approach in the field of intrusion detection and has seen extensive exploration and applications [13; 14]. As research has progressed, the literature [15; 16; 17; 18] has compared various datasets commonly used in intrusion detection, revealing their complex high-dimensional nature and the imbalance between positive and negative classes. These characteristics can lead to lower detection rates for certain classes. Nevertheless, the Random Forest algorithm, when dealing with large datasets, demands the construction of multiple decision trees and ensemble integration. When computational resources are limited, this can result in excessive resource consumption and reduced computational efficiency. Additionally, when high-dimensional sparse data serve as the input to the classification algorithm, the similarity between data samples becomes pronounced, making it challenging to identify effective split points.