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Abstract : The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyber-attacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By leveraging advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. The model is trained and tested using a comprehensive dataset that includes a diverse range of cyber-attack types, allowing it to generalize effectively across various threat scenarios. The experimental results demonstrate that the optimized SVM model outperforms traditional methods in terms of detection accuracy, false positive rate, and computational efficiency. Additionally, the model's adaptability to new and unforeseen attack patterns highlights its potential as a critical component in modern cybersecurity infrastructures. This study contributes to the field by offering a scalable and effective solution to the pressing challenge of intrusion detection in an increasingly complex digital environment. Future work will explore the integration of real-time data processing and the application of deep learning techniques to further enhance the model's capabilities.
Keyword Intrusion Detection System (IDS), Support Vector Machine (SVM), Cybersecurity, Feature Selection, Optimization Techniques
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