Securing IoT Networks: Dual Feature Selection with ANN, KNN, and DT for Attack Detection using Modified-IFS and ECFS Algorithm

Problem Definition

From the literature review provided, it is evident that there exists a gap in the current systems for detecting intrusions in IoT networks using AI-based ML and DL models. While many models have been proposed, they struggle to accurately identify and categorize attacks, leaving the systems vulnerable to potential risks. The inefficiency of current ML models in handling large datasets has led to the loss of critical information, highlighting the need for more advanced approaches such as DL methods. However, the lack of focus on feature selection techniques in DL-based intrusion detection systems has resulted in reduced accuracy and high false alarm rates. Therefore, there is a pressing need to develop a model that utilizes effective feature selection techniques to retrieve important features from large datasets while also reducing dimensionality.

By incorporating efficient classifiers into the proposed model, the detection rate can be significantly enhanced to address the limitations and shortcomings of existing systems in the domain of IoT network security.

Objective

The objective is to develop a model that utilizes effective feature selection techniques to accurately detect and categorize intrusions in IoT networks using AI-based ML and DL models. By incorporating popular classifiers such as Artificial Neural Network (ANN), k-nearest neighbours algorithm (KNN), and random forest (RF), the proposed model aims to enhance the detection rate and reduce false alarm rates. The focus is on addressing the limitations of existing systems by utilizing a hybrid approach of enhanced infinite feature selection and Eigenvector Centrality and Ranking. The model will go through two main phases - feature selection and classification, using standard datasets KDD-Cup99 and NSL-KDD for training and testing. Ultimately, the objective is to provide a more effective and accurate intrusion detection system to protect IoT networks from potential risks.

Proposed Work

With the increasing number of AI-based ML and DL models proposed for detecting intrusions in IoT networks, it has been noted that there is a gap in identifying and categorizing attacks that leave systems vulnerable. Traditional ML models struggle with handling large datasets, leading to a loss of critical information. As a result, researchers have shifted their focus to DL methods, specifically in the area of feature selection techniques. This proposed work aims to address the limitations of existing systems by utilizing a hybrid approach of enhanced infinite feature selection and Eigenvector Centrality and Ranking with popular classifiers such as Artificial Neural Network (ANN), k-nearest neighbours algorithm (KNN), and random forest (RF) for the intrusion detection system. In order to achieve this objective, the proposed model will go through two main phases - feature selection and classification.

The raw data will be pre-processed and refined to ensure balance and normalization, followed by the application of feature selection algorithms to select only the most relevant features for enhancing the accuracy of the detection rate. Two standard datasets, KDD-Cup99 and NSL-KDD, will be used for training and testing the model, with the performance of ANN, KNN, and Decision Tree classifiers analyzed. By improving the detection rate and reducing false alarm rates, this approach aims to provide a more effective and accurate intrusion detection system that can better protect IoT networks from potential threats.

Application Area for Industry

This project can be applied in various industrial sectors such as cybersecurity, telecommunications, finance, healthcare, and manufacturing. The proposed solutions in this project address the challenge of effectively detecting and categorizing intrusions in IoT networks, which is a critical issue faced by industries that rely on interconnected systems for their operations. By utilizing efficient feature selection techniques and classifiers, the accuracy of intrusion detection models can be significantly enhanced, leading to improved cybersecurity measures and reduced vulnerability to cyber attacks. Implementing these solutions in different industrial domains can help in safeguarding sensitive data, minimizing potential threats, and ensuring the smooth functioning of interconnected systems, ultimately resulting in increased operational efficiency and protection of critical information.

Application Area for Academics

The proposed project aims to enrich academic research, education, and training by providing a comprehensive approach to intrusion detection in IoT networks using machine learning and deep learning techniques. This project has the potential to contribute significantly to the field of cybersecurity and data analysis within educational settings. The relevance of this project lies in its focus on addressing the limitations of existing intrusion detection systems by incorporating effective feature selection techniques and utilizing efficient classifiers to enhance the accuracy of threat detection. By analyzing and comparing the performance of various classifiers such as Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Decision Tree (DT) on standard datasets like KDD-Cup99 and NSL-KDD, this project can provide valuable insights into the effectiveness of different algorithms in detecting and categorizing attacks in IoT networks. Researchers, MTech students, and PhD scholars in the field of cybersecurity and machine learning can benefit from the code and literature of this project for their academic work.

The algorithms used in this project, including Modified-IFS, ECFS, ANN, KNN, and Random Forest (RF), can serve as valuable tools for developing innovative research methods, simulations, and data analysis techniques in the domain of intrusion detection in IoT networks. Moreover, the future scope of this project includes exploring advanced machine learning and deep learning techniques, as well as incorporating real-time data processing and anomaly detection mechanisms to further improve the performance and efficiency of the intrusion detection system. Additionally, the application of this project can be extended to other domains such as network security, anomaly detection, and predictive maintenance, thereby offering a wide range of research opportunities for academic scholars and students.

Algorithms Used

The proposed work in this project involves the use of several algorithms to enhance the accuracy of intrusion detection in an IoT environment. The Modified-IFS and ECFS algorithms are used for feature selection, which helps in refining and processing raw data to improve the accuracy of the detection rate. These algorithms focus on selecting only the most relevant features from the input data, reducing complexity and improving efficiency. In the classification phase, the Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Random Forest (RF) algorithms are employed to classify the data into either an intrusion or regular data traffic. These classifiers are trained on the pre-processed and selected features to effectively categorize incoming data and identify potential attacks.

Overall, the combined use of feature selection and classification algorithms plays a crucial role in achieving the project's objectives of enhancing accuracy and efficiency in intrusion detection. The algorithms work together to process and classify data effectively, improving the overall performance of the detection model in detecting and preventing cyber attacks in IoT systems.

Keywords

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SEO Tags

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