Enhancement performance of random forest algorithm via one hot encoding for IoT IDS

Abstract

The random forest algorithm is one of important supervised machine learning (ML) algorithms. In the present paper, the accuracy of the results of the random forest (RF) algorithm has been improved by the use of the One Hot Encoding method. The Intrusion Detection System (IDS) can be defined as a system that can predict security vulnerabilities within network traffic and is located out of range on a network infrastructure. It does not affect the efficiency of the built-in network because it analyzes a copy of the built-in traffic flow and reports results to the administrator by giving alerts. However, since IDS is a listening system only, it cannot take automatic action to prevent an attack or security vulnerability detected from infecting the system, it provides information about the source address to start the break-in, the address of the target and the type of suspected attack. The IoTID20 dataset is used to verify the improved algorithm, where this dataset is having three targets, the proposed system is compared with the state-of-art approaches and shows superiority over them.

Toddy trends and the organic conundrum: a closer look at consumer behaviour with decision tree

“…One-hot encoding is a technique used in machine learning to represent categorical variables as binary vectors, where each category is transformed into a unique binary digit (0 or 1). This process allows algorithms to efficiently work with categorical data by creating a binary representation that preserves the distinctiveness of each category in a way that is suitable for mathematical computations (Hussein et al ., 2021). Efforts to explicitly reduce redundancy in the dataset were not undertaken, as reliance was placed on the inherent capability of Decision Trees to perform feature selection through the assessment of the Gini coefficient.…”

GAMA: A multi-graph-based anomaly detection framework for business processes via graph neural networks

“…Due to the neural networks' inability to interpret language, we must transform the language to numbers. Onehot embedding [58], which transforms categorical variables into binary vectors, is one technique to accomplish this. After one-hot embedding, the value of nodes can be converted to |V a |-dimensional vectors, where |V a | is number of possible values for the attribute a.…”

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

“…cannot be directly used for these mathematical operations in their raw form. Therefore, it is necessary to transform non-numerical features into numerical form to ensure model compatibility [74]. Commonly used numerical coding methods in IDS based on deep learning models are One-hot encoding, Label encoding, etc.…”