2025, Vol. 7, Issue 5, Part B

Author(s): Arshad Husain and Ruchin Jain

Abstract: The increasing sophistication and frequency of cyberattacks, enhancing the accuracy of network attack detection has become a critical priority for securing digital infrastructures. Traditional detection methods often struggle to identify complex and evolving threats, necessitating more intelligent approaches. This paper explores the application of deep learning optimization techniques to improve the precision and reliability of network attack detection systems. Deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Belief Networks (DBNs) are leveraged to automatically extract and learn complex patterns from large-scale network traffic data. To maximize performance, various optimization strategies—including hyperparameter tuning, genetic algorithms, and regularization methods—are employed to refine model architectures and training processes. These optimizations help address challenges like overfitting, training time, and false positive rates, thereby enhancing the model’s generalization and real-time detection capabilities. Experimental results demonstrate that the optimized deep learning models significantly outperform traditional approaches, achieving higher precision, recall, and overall accuracy in identifying network intrusions. This study highlights the potential of integrating advanced optimization techniques with deep learning to build robust, scalable, and adaptive security solutions that can keep pace with rapidly evolving cyber threats. The findings provide valuable insights for researchers and practitioners aiming to develop next-generation intrusion detection systems with improved detection accuracy and operational efficiency.

DOI: 10.33545/27068919.2025.v7.i5b.1463

Pages: 109-114 | Views: 145 | Downloads: 35

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