Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection using Hybrid Algorithms and Inverted Hour-Glass Model

Problem Definition

The increasing frequency and sophistication of cyber attacks pose a significant threat to the security and integrity of computer networks, making the development of effective Intrusion Detection Systems (IDS) a critical necessity. Current IDS face numerous challenges, including the need to improve accuracy in detecting intrusions, reduce false alarm rates, and handle the overwhelming amount of data produced by these systems. While some existing IDS have achieved high accuracy rates, they often do so at the expense of increased computational complexity and information loss, rendering them less effective in practice. Additionally, many IDS are limited by their reliance on a single dataset, which restricts their ability to detect new and emerging forms of cyber threats. Furthermore, the use of traditional machine learning algorithms in IDS development can lead to underfitting and overfitting issues, ultimately resulting in subpar performance of the system.

Addressing these limitations and challenges is crucial for enhancing the effectiveness and reliability of IDS in safeguarding computer networks against malicious intrusions.

Objective

The objective of this work is to address the challenges and limitations in existing Intrusion Detection Systems (IDS) by proposing a novel approach that combines feature selection algorithms, a modified optimization algorithm, and deep learning techniques. By incorporating multiple datasets and an inverted hour-glass based layered network architecture, the goal is to enhance accuracy, reduce processing time, and effectively detect intrusions in IoT networks. The use of state-of-the-art algorithms and techniques aims to overcome the limitations of current IDS systems and improve overall performance in safeguarding computer networks against cyber threats.

Proposed Work

In this work, the focus is on addressing the gaps and challenges in existing Intrusion Detection Systems (IDS) by proposing a novel approach that combines feature selection algorithms with a modified optimization algorithm and deep learning techniques. The literature survey revealed that current IDS systems struggle with reducing false alarm rates, limiting their accuracy, and often use only one dataset, limiting their ability to detect new attacks. By incorporating multiple datasets (KDD cup99, NSL-KDD, and UNSW-NB15) and an inverted hour-glass based layered network architecture, the proposed model aims to enhance accuracy while reducing processing time. To overcome the complexity of using multiple datasets, a hybrid of Yellow Saddle Goatfish (YSGA) and Particle Swarm Optimization (PSO) algorithms with a Decision Tree model is used to extract important features. This not only simplifies the model but also improves execution time.

Additionally, by incorporating an inverted hour-glass architecture, the model can effectively handle large volumes of data and categorize incoming traffic as normal or intrusive. By implementing this approach, the goal is to develop a highly accurate and efficient IDS that can effectively detect intrusions in IoT networks. The use of state-of-the-art algorithms and deep learning techniques within a specialized network architecture will enable the model to achieve high accuracy without compromising on execution time. By selecting only important features from multiple datasets and leveraging advanced optimization algorithms, the proposed approach aims to overcome the limitations of existing IDS systems and improve overall performance. This comprehensive strategy sets out to address the challenges identified in the literature survey and offers a promising solution for enhancing the security and integrity of computer networks through advanced intrusion detection capabilities.

Application Area for Industry

This project can be utilized in a wide range of industrial sectors including cybersecurity, information technology, finance, healthcare, and telecommunications. The proposed solutions can be applied within different industrial domains by addressing specific challenges such as reducing false alarm rates in intrusion detection systems, enhancing accuracy, and handling a large volume of data effectively. By using multiple datasets and a hybridized approach with YSGA, PSO algorithms, and Decision Tree models, the proposed layered network architecture can significantly improve the accuracy of intrusion detection while reducing computational complexity and processing time. Additionally, the ability to handle unbalanced datasets and categorize incoming data traffic into normal and intrusions effectively makes this project a valuable tool for industries where cybersecurity is a top priority. The benefits of implementing these solutions include heightened security, improved efficiency, and better protection against cyber threats, ultimately safeguarding critical data and networks in various industrial settings.

Application Area for Academics

The proposed project on developing a novel Intrusion Detection System (IDS) using multiple datasets and an inverted hour-glass based layered network architecture has significant potential to enrich academic research, education, and training in the field of cybersecurity. This project addresses the challenges faced by traditional IDS models in reducing false alarm rates and increasing accuracy in detecting intrusions in computer networks. By incorporating multiple datasets and hybridizing algorithms like YSGA, PSO, and Decision Tree along with Deep learning, the proposed model aims to achieve higher accuracy while reducing computational complexity and execution time. Academically, this project can contribute to innovative research methods in IDS by incorporating a layered network architecture and utilizing multiple datasets for training and testing. Researchers, MTech students, and PHD scholars in the field of cybersecurity can use the code and literature of this project to enhance their understanding of intrusion detection techniques and apply the proposed model in their own research.

By exploring the effectiveness of hybridized algorithms and network architectures, academic institutions can introduce advanced concepts in data analysis, simulations, and machine learning to students pursuing education in cybersecurity. The relevance of this project extends to practical applications in detecting intrusions in IoT networks, securing computer systems, and improving the overall cybersecurity posture of organizations. The use of advanced algorithms like YSGA, PSO, and RF, combined with deep learning techniques, allows for the accurate categorization of incoming data traffic into normal and intrusion classes. This not only enhances the security of computer networks but also provides a platform for future research in optimizing IDS performance and adapting to evolving cyber threats. In conclusion, the proposed project on developing a novel IDS system using multiple datasets and advanced algorithms has the potential to enrich academic research, education, and training in the field of cybersecurity.

By addressing the limitations of existing IDS models and introducing innovative techniques for intrusion detection, this project opens up new avenues for research, data analysis, and simulation in educational settings. The future scope of this project includes further enhancing the accuracy and efficiency of the proposed model, exploring new algorithms and architectures for intrusion detection, and collaborating with industry partners to implement the developed IDS in real-world cybersecurity scenarios.

Algorithms Used

YSGA and PSO algorithms are used in the project to address the complexity and processing time concerns associated with using multiple datasets. These algorithms are utilized to extract and select only important features from the datasets, contributing to an enhanced accuracy of the intrusion detection model. They help in reducing the complexity of the system and improving its efficiency by only focusing on crucial data points. Additionally, the Random Forest (RF) algorithm is employed in the project to further refine the feature selection process and improve the overall performance of the model. The Deep Learning algorithm (RESNET) is incorporated into the proposed inverted hour-glass based layered network architecture to effectively categorize incoming data traffic into normal and intrusion categories.

This architecture is specifically designed to handle large volumes of data and enhance the accuracy of intrusion detection. By utilizing deep learning techniques, the model is able to overcome shortcomings of existing intrusion detection models and achieve superior results in terms of accuracy and efficiency.

Keywords

Intrusion Detection System, IDS, Network security, Deep learning, Neural network, Machine learning, Anomaly detection, Cybersecurity, Network traffic analysis, Data preprocessing, Feature extraction, Pattern recognition, Network intrusion, Malware detection, Security threats, Cyber attack, Artificial intelligence, KDD cup99, NSL-KDD, UNSW-NB15, Yellow Saddle Goatfish, Particle Swarm Optimization, Decision Tree.

SEO Tags

Intrusion Detection System, IDS, Network security, Deep learning, Deep learning architecture, Neural network, Machine learning, Anomaly detection, Cybersecurity, Network traffic analysis, Data preprocessing, Feature extraction, Pattern recognition, Network intrusion, Malware detection, Security threats, Cyber attack, Artificial intelligence, KDD cup99 dataset, NSL-KDD dataset, UNSW-NB15 dataset, Yellow Saddle Goatfish algorithm, Particle Swarm Optimization algorithm, Decision Tree model, Intrusion detection model, Layered network architecture, False alarm rates, Accuracy of system, Cyber attacks, Computer networks, Literature survey, PHD, MTech student, Research scholar.