An Enhanced Feature Selection and Hybrid IDS Model for IoT Network Security with Trust-Based Routing and ACOTSA Algorithm

Problem Definition

The current state of intrusion detection systems (IDS) reveals a significant challenge in the form of limitations of rule-based IDS. These systems rely on a set of predefined rules stored in a knowledge base to detect known attack types, which poses a problem when it comes to dynamically updating the rule database and detecting variations of attacks. To address these shortcomings, AI-based approaches such as Machine Learning (ML) and Deep Learning (DL) are increasingly being utilized in IDS to focus on learning-based detection of novel attacks. While these AI techniques have shown success in detecting specific types of attacks, they too encounter limitations, particularly in detecting low-frequency attacks and during the complex learning phase. Additionally, the use of large datasets in ML-based IDS can lead to the "Curse of Dimensionality," making the learning process resource-intensive and complex.

These challenges highlight the need for a more efficient and effective intrusion detection approach that can address the limitations of both rule-based and AI-based systems.

Objective

The objective of the proposed project is to develop an advanced Intrusion Detection System (IDS) model that overcomes the limitations of traditional rule-based systems by incorporating machine learning techniques for improved detection rates and reduced False Alarm rates in wireless sensor networks. By employing feature selection algorithms and classifiers like ANN, KNN, and DT, the model aims to enhance accuracy in identifying novel attacks while minimizing resource-intensive learning processes. The project also introduces a hybrid IDS model based on DT and KNN, along with a secure trust-based routing mechanism using the ACOTSA algorithm, to provide a comprehensive and secure communication pathway in wireless sensor networks. Through the systematic implementation of these phases, the project seeks to contribute to the advancement of intrusion detection and secure routing frameworks, addressing the challenges faced by current IDS systems.

Proposed Work

In order to address the limitations of rule-based Intrusion Detection Systems (IDS) and enhance the accuracy of detection rates while minimizing False Alarm rates (FAR), a proposed advanced IDS model aims to provide a secure routing framework for wireless sensor networks. This three-phase approach includes a focus on feature selection to reduce complexity and improve detection rates by applying the EIFS and ECRFS algorithms on pre-processed datasets, followed by classification using classifiers like ANN, KNN, and DT, where DT and KNN proved to be most accurate. The second phase involves the development of a hybrid IDS model based on DT and KNN to achieve high-accuracy results, while the third phase introduces a secure trust-based routing mechanism that evaluates network characteristics, traffic, and trust factors for node selection, in addition to considering factors like node density, delay, energy consumption, and more. An optimized hybrid routing algorithm, ACOTSA, which combines ACO and TSA, is proposed to determine the best routing path based on node parameters. The proposed project aims to overcome the shortcomings of traditional rule-based IDS by incorporating machine learning techniques for comprehensive intrusion detection and secure routing in wireless sensor networks.

By utilizing advanced algorithms for feature selection and classification, the model is designed to improve detection rates while reducing False Alarm rates and providing a secure communication pathway. The use of hybrid approaches and trust-based routing mechanisms adds a layer of complexity and sophistication to the system, allowing for a more accurate and efficient detection process. By combining different classifiers and proposing an optimized routing algorithm, the project seeks to achieve a holistic and robust solution for intrusion detection and secure communication in wireless sensor networks, thus addressing the existing challenges and limitations in current IDS systems. Through systematic phases and a methodical approach, the project aims to contribute to the advancement of intrusion detection and secure routing frameworks in the context of wireless sensor networks.

Application Area for Industry

The proposed project can be implemented in various industrial sectors such as Information Technology, Cybersecurity, Telecommunications, and Automotive industries. In the Information Technology sector, the advanced intrusion detection model can enhance the security of sensitive data stored in networks. In the Cybersecurity domain, the hybrid IDS model can improve the accuracy of detecting intrusions while reducing false alarm rates. Within the Telecommunications industry, the project can help in securing communication channels in wireless sensor networks. Additionally, in the Automotive sector, the secure trust-based routing framework can ensure a safe and reliable transfer of data between vehicles and infrastructure.

The implementation of the proposed solutions in these industrial sectors addresses specific challenges such as the poor detection rate for low-frequency attacks, the curse of dimensionality in learning processes, and the need for secure routing paths during communication. By using advanced feature selection techniques and hybrid IDS models, the project offers benefits such as increased accuracy in intrusion detection, reduced false alarm rates, and improved communication security. Moreover, the integration of AI-based algorithms and trust-based routing mechanisms can optimize the performance of classifiers and enhance the efficiency of routing algorithms in various industrial domains.

Application Area for Academics

The proposed project can greatly enrich academic research, education, and training by introducing a novel approach to enhancing intrusion detection in wireless sensor networks. This research is highly relevant in the field of cybersecurity and network security, providing a practical solution to the limitations of rule-based and traditional machine learning-based intrusion detection systems. By integrating advanced feature selection techniques and a hybrid IDS model based on Decision Trees (DT) and K-nearest neighbors (KNN), the project aims to improve detection rates while reducing False Alarm rates (FAR). This research has the potential to be applied in various educational settings for teaching and training purposes. Students pursuing research in the field of cybersecurity, network security, and artificial intelligence can benefit from the code and literature of this project.

MTech students and PhD scholars can use the proposed algorithms and methodologies for their own research work, exploring innovative approaches to intrusion detection and secure routing in wireless sensor networks. The use of advanced algorithms such as Ant Colony Optimization (ACO) and Tabu Search Algorithm (TSA) in developing a secure trust-based routing framework further enhances the practical applications of this project. Researchers and students can explore the implications of these algorithms in optimizing routing paths based on factors like energy consumption, network traffic, and trust levels of nodes. In conclusion, this project has the potential to contribute significantly to academic research in the domains of cybersecurity and network security. The novel methodologies and algorithms introduced through this research can advance the understanding of intrusion detection and secure routing in wireless sensor networks, offering valuable insights for future studies in the field.

The integration of AI-based approaches with traditional IDS systems opens up new avenues for innovation and exploration in cybersecurity. Future Scope: The future scope of this research includes exploring the application of blockchain technology for secure communication in wireless sensor networks, integrating machine learning models with IDS for adaptive learning capabilities, and evaluating the scalability of the proposed hybrid IDS model in larger network environments. Additionally, the project could be extended to investigate the impact of edge computing and IoT devices on intrusion detection mechanisms, further enhancing the security of interconnected systems.

Algorithms Used

The algorithms used in this project are ACO (Ant Colony Optimization) and TSA (Tabu Search Algorithm). ACO is utilized in the proposed hybrid routing algorithm, ACOTSA, to find the optimal route in the wireless sensor network based on parameters such as remaining energy in a node and previous behavior of a node. TSA is combined with ACO to further enhance the routing efficiency and security of the network. The ACOTSA algorithm plays a crucial role in ensuring secure communication by selecting the best routing path for data transmission within the network. It improves the overall efficiency and reliability of the network by taking into account various factors such as energy consumption and node behavior.

By integrating ACO and TSA, the algorithm aids in achieving the project's objectives of providing a secure routing path during communication in the wireless sensor network.

Keywords

SEO-optimized keywords: Signature-based intrusion detection, Rule-based expert systems, Machine learning IDS, Deep learning IDS, Feature selection technique, Hybrid IDS model, False Alarm rates, Secure routing path, Wireless sensor network security, KDDCUP99 dataset, NSL-KDD dataset, Entropy-based feature selection, Eigenvector centrality, Ranking FS algorithm, Artificial Neural Network, K-nearest neighbour, Decision Tree classifier, Performance evaluation, Trust-based routing framework, Network characteristics, Novelty detection, Node evaluation, Packet Delivery Ratio, Packet Loss Ratio, Energy consumption, Hybrid routing algorithm, Ant Colony Optimization, Tabu Search Algorithm, Network performance optimization, Data transmission security, Energy-efficient routing, Sensor node security, Artificial intelligence intrusion detection.

SEO Tags

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