AFS-DLA: Adaptive Feature Selection and DL Architecture for Enhanced IoT Network Intrusion Detection

Problem Definition

The literature suggests that the utilization of Machine Learning (ML) and Deep Learning (DL) techniques in intrusion detection, particularly in Internet of Things (IoT) systems, has shown great promise. However, one of the major challenges identified is the handling of dataset variations, which can impact the performance of Intrusion Detection Systems (IDS). The development of adaptive models that can effectively extract relevant information during network training is crucial to overcome this limitation. Furthermore, the optimization of feature selection algorithms is key to improving the efficiency and detection rates of IDS. Current research also highlights the need for enhancing DL model architectures to better detect intrusions in complex and diverse networks.

The existing problems in IDS technology, such as dataset variations, feature selection limitations, and the complexity of IoT networks, indicate a pressing need for innovative solutions like the proposed adaptive feature selection-based deep learning architecture. By addressing these challenges, this approach has the potential to significantly enhance the security of IoT networks and improve the overall performance of IDS. Future advancements in these areas are essential for advancing IDS technology and strengthening the security of IoT systems against evolving cyber threats.

Objective

The objective is to develop an adaptive feature selection-based deep learning architecture for intrusion detection systems in IoT networks. This approach aims to address challenges such as dataset variations, feature selection limitations, and the complexity of IoT networks by enhancing the efficiency and detection rates of IDS. By utilizing optimization algorithms and a DL-based IF-MN classification model, the goal is to improve the security of IoT networks and strengthen IDS performance against evolving cyber threats. The focus is on selecting informative features, creating a hybrid feature selection model, and designing a DL-based architecture to effectively classify attacks and improve overall accuracy and effectiveness of the IDS model. Multiple datasets will be used to evaluate adaptability and performance, showcasing the innovative approach to enhancing IoT network security and advancing IDS technology.

Proposed Work

To address the limitations and challenges in intrusion detection systems (IDS) for IoT networks, this paper proposes a comprehensive solution that combines adaptive feature selection techniques and deep learning architectures. The primary goal is to develop an IDS system that can effectively detect and identify intrusions in IoT networks by selecting only informative features from the datasets and utilizing a novel DL-based IF-MN classification model. The proposed scheme integrates two optimization algorithms, Yellow Saddle Goat fish algorithm (YSGA) and Particle Swarm Optimization (PSO), to create a hybrid feature selection model (HY-FS-PSO) that enhances the accuracy and efficiency of the IDS. By selecting optimal features from the training data and using a Decision Tree classifier, the system can achieve higher detection rates and effectively handle the complexity and high dimensionality of IoT network data. Furthermore, the proposed DL-based inverted funnel operated multilayer architecture, IF-MN, is specifically designed to classify and categorize different attacks within IoT networks.

Trained on the informative features selected through the feature selection phase, this architecture improves the overall accuracy and effectiveness of the IDS model. One of the key contributions of this research is the focus on using multiple datasets to evaluate the adaptability and performance of the IDS in different environments, addressing the need for enhanced flexibility and robustness in intrusion detection systems. Additionally, the development of a novel optimization method for feature selection and the implementation of the DL-based classification architecture highlight the innovative approach taken to improve the security of IoT networks and advance the field of IDS technology.

Application Area for Industry

This project can be utilized across various industrial sectors that rely on network security, such as banking and finance, healthcare, government, and critical infrastructure. By applying the proposed adaptive feature selection-based deep learning architecture, industries can overcome the challenge of dataset variations and effectively extract pertinent information during network training. The novel optimization algorithm schemes, which combine Yellow Saddle Goat fish algorithm (YSGA) and Particle Swarm Optimization (PSO), enable the IDS system to handle complexity and high dimensionality issues. The result is a more efficient and accurate detection of intrusions in IoT networks, enhancing overall cybersecurity measures in industries facing diverse and intricate network environments. Implementing this solution offers the benefit of improved detection rates and increased accuracy in identifying various modern attacks, ultimately bolstering network security and protecting sensitive information.

Furthermore, the development of a DL-based inverted funnel operated multilayer architecture specifically designed for classifying attacks within an IoT network offers a tailored approach to intrusion detection. By training this architecture on highly informative features selected through the feature selection phase, industries can classify intrusions with increased accuracy. The system's ability to adapt and respond effectively to different datasets and environments ensures its flexibility and reliability in various industrial domains. The project's focus on refining DL model architectures and optimizing feature selection algorithms addresses key challenges faced by industries in detecting and preventing network intrusions, making it a valuable tool in enhancing cybersecurity measures across different sectors.

Application Area for Academics

The proposed project offers significant contributions to academic research, education, and training in the field of network security and intrusion detection. By utilizing Machine Learning and Deep Learning techniques, the project aims to enhance the efficiency and security of IoT networks. The development of adaptive feature selection-based deep learning architecture, along with novel optimization algorithm schemes, addresses the challenge of handling dataset variations and improving IDS performance. The project's innovative approach in combining Yellow Saddle Goat fish algorithm (YSGA) and Particle Swarm Optimization (PSO) for feature selection, coupled with the use of Decision Tree (DT) classifier and IF-MN architecture for attack classification, provides a comprehensive solution to current IDS limitations. Researchers, MTech students, and PHD scholars can leverage the code and literature of this project to explore new research methods, simulations, and data analysis techniques within educational settings.

This project covers the technology domain of network security, specifically focusing on intrusion detection in IoT systems. The developed algorithms, YSGA, PSO, DT, and IF-MN architecture, offer a practical framework for conducting experiments, testing different datasets, and evaluating the effectiveness of the proposed IDS system. Future advancements in this area will contribute to the advancement of IDS technology and the enhancement of IoT network security. The potential applications of this project extend to researchers seeking to explore adaptive feature selection methods, optimization algorithms, and deep learning architectures in the context of network security. The hybrid IDS model proposed in this project can serve as a valuable tool for detecting and classifying intrusions in diverse network environments.

Moreover, the project sets the stage for further research and development in the field, opening up opportunities for future studies on improving intrusion detection systems and enhancing network security measures.

Algorithms Used

The project proposes an Intrusion Detection System (IDS) that addresses complexity and high dimensionality issues by utilizing novel optimization algorithms. The system combines Yellow Saddle Goat fish algorithm (YSGA) and Particle Swarm Optimization (PSO) to identify optimal features from training data. These features are then evaluated using a Decision Tree (DT) classifier to enhance detection rates. Additionally, the system incorporates a DL-based IF-MN architecture designed to classify different attacks in an IoT network. By selecting informative features and utilizing advanced algorithms, the proposed IDS aims to accurately detect and identify intrusions in network traffic.

The system is designed to be adaptable to different datasets and environments, improving its effectiveness in detecting various attacks. The project's main contributions include the development of a novel optimization method for feature selection and the implementation of the IF-MN architecture for intrusion determination.

Keywords

Machine Learning, Deep Learning, Intrusion Detection, Network Security, Internet of Things, Adaptive Models, Feature Selection Algorithms, Optimization Algorithms, Intricate Networks, Adaptive Feature Selection, Deep Learning Architecture, IDS Limitations, IoT Network Security, Hybrid Model, Feature Selection Phase, Network Traffic Patterns, Intelligent System, Dataset Variations, Decision Tree Classifier, Anomaly Detection, Cybersecurity, Malware Detection, Security Threats, Artificial Intelligence, Neural Network, Cyber Attack, Pattern Recognition, Data Preprocessing, Network Intrusion, Network Traffic Analysis.

SEO Tags

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