Enhanced Intrusion Detection Using Hybrid DT+KNN Model with Feature Selection and Fusion Approach

Problem Definition

After conducting a thorough review of existing literature on intrusion detection methods for IoT networks, it is evident that while various approaches have been proposed to enhance the detection of intrusions, there are several key limitations that need to be addressed. One major issue is the tendency for existing intrusion detection models to suffer from overfitting, particularly due to the vast amount of data being generated on the internet daily. Furthermore, the lack of researchers working on multiple datasets hinders the development of accurate systems. The complexity introduced by using multiple datasets can also lead to a reduction in detection accuracy. Additionally, the poor generalization capability exhibited during network training can result in performance degradation, while the use of ineffective classifiers contributes to low accuracy rates.

It is essential to overcome these limitations by developing a new and effective intrusion detection method that can address these problems and improve the overall accuracy of the system.

Objective

The objective of this study is to develop a new intrusion detection method for IoT networks that addresses the limitations of existing systems, such as overfitting, poor generalization capability, and low accuracy rates. By combining Decision Tree and K-Nearest Neighbor algorithms, the aim is to improve accuracy while reducing model complexity. This will involve collecting data from KDD-CUP99 and NSL-KDD datasets, preprocessing the data, implementing a hybrid feature selection algorithm, and training the model using KNN and DT classifiers to accurately detect and classify intrusion attacks in the IoT network.

Proposed Work

The proposed work aims to address the limitations of existing intrusion detection systems in IoT networks by developing a new method that combines Decision Tree and K-Nearest Neighbor algorithms. The key objective is to enhance the accuracy of intrusion detection while reducing the complexity of the model. The process involves collecting data from KDD-CUP99 and NSL-KDD datasets, preprocessing the data to remove redundant information, implementing a hybrid feature selection algorithm to identify important features, and training the model using KNN and DT classifiers. By combining the outputs of both classifiers, the proposed hybrid model is able to accurately detect and classify intrusion attacks in the IoT network. This approach is chosen based on its ability to improve accuracy and reduce complexity, thereby overcoming the limitations of existing ID models.

Application Area for Industry

This project can be utilized in a variety of industrial sectors such as cybersecurity, IoT, networking, and data analytics. Industries that heavily rely on IoT networks, such as manufacturing, healthcare, transportation, and smart cities, can benefit greatly from the proposed ID system. The project's solutions address the challenges of overfitting, limited detection accuracy, complexity in using multiple datasets, poor generalization capability, and ineffective classifiers in traditional ID models. By leveraging Decision Tree (DT) and K-Nearest Neighbor (KNN) algorithms, the proposed system aims to improve detection accuracy while reducing model complexity. Implementing this system can result in enhanced security measures for industries by effectively identifying and differentiating between regular data traffic and potential attacks in IoT networks.

The model's approach of data collection, pre-processing, feature selection, and classification phases ensures that only important and relevant information is considered, leading to better performance and improved accuracy rates. By utilizing advanced techniques and algorithms, industries can enhance their cybersecurity measures and protect their IoT networks from potential threats, ultimately enhancing operational efficiency and ensuring the safety of their systems and data.

Application Area for Academics

The proposed project can enrich academic research, education, and training by introducing a new and effective method for intrusion detection in IoT networks. By combining Decision Tree and K-Nearest Neighbor techniques, the project aims to increase the accuracy of detection rates while reducing the complexity of the model. This approach can be beneficial for researchers, MTech students, and PHD scholars working in the field of cybersecurity and network security. The relevance and potential applications of this project lie in its innovative research methods, simulations, and data analysis within educational settings. It addresses the limitations of existing ID models such as overfitting, limited accuracy, poor generalization capability, and ineffective classifiers.

By utilizing multiple datasets and implementing a hybrid feature selection algorithm, the proposed model enhances the accuracy of system detection and simplifies the processing ability of the model. Researchers in the field of cybersecurity can use the code and literature of this project to enhance their research on intrusion detection systems. MTech students can incorporate the proposed hybrid DT+KNN model into their coursework to gain hands-on experience with advanced techniques in network security. PHD scholars can explore the potential of this project for further research and development in the field of cybersecurity. The future scope of this project includes exploring additional algorithms such as Random Forest (RF) for intrusion detection, as well as testing the model on a wider range of datasets to evaluate its performance in different scenarios.

By continuously refining and improving the proposed method, researchers and students can contribute to the advancement of intrusion detection systems and cybersecurity technologies.

Algorithms Used

The project utilizes a combination of Modified-IFS, ECFS, KNN, and RF algorithms to develop an improved and efficient Intrusion Detection (ID) system. The proposed work focuses on enhancing the accuracy of attack detection rates while simplifying the model's complexity. The process is divided into four main phases: Data Collection, Data Pre-Processing, Feature Selection, and Classification. Initially, diverse attack information is collected from KDD-CUP99 and NSL-KDD datasets. Subsequently, the data is pre-processed to eliminate redundant, irrelevant, and missing information, ensuring a normalized and balanced dataset.

The hybrid feature selection technique (Entropy-based Infinite Feature Selection and Eigenvector Centrality and ranking FS) is then applied to select significant features, reducing complexity and enhancing processing efficiency. The selected features are divided into training and testing data subsets, which are fed into KNN and DT classifiers for training and testing purposes. The hybrid DT+KNN model analyzes the input data, categorizing it as an attack or regular traffic based on matching feature vectors. By combining the outputs of both classifiers, the overall performance of the ID system is evaluated, ultimately achieving the project's objectives of increased detection accuracy and reduced model complexity.

Keywords

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SEO Tags

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