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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Basarkod, Prabhugoud I.
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Computer Science & IT Engineering

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Enhancing intrusion detection in next-generation networks based on a multi-agent game-theoretic framework Lakshminarayana, Sai Krishna; Basarkod, Prabhugoud I.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4856-4868

Abstract

With cyber threats becoming increasingly sophisticated, existing intrusion detection systems (IDS) in next generation networks (NGNs) are subjected to more false-positives and struggles to offer robust security feature, highlighting a critical need for more adaptive and reliable threat detection mechanisms. This research introduces a novel IDS that leverages a dueling deep Q-network (DQN) a reinforcement learning algorithm within game-theoretic framework simulating a multi-agent adversarial learning scenario to address these challenges. By employing a customized OpenAI Gym environment for realistic threat simulation and advanced dueling DQN mechanisms for reduced overestimation bias, the proposed scheme significantly enhances the adaptability and accuracy of intrusion detection. Comparative analysis against current state-of-the-art methods reveals that the proposed system achieves superior performance, with accuracy and F1-score improvements to 95.02% and 94.68%, respectively. These results highlight the potential scope of the proposed adaptive IDS to provide a robust defense against the dynamic threat landscape in NGNs.
A novel deep anomaly detection approach for intrusion detection in futurisitic network Lakshminarayana, Sai Krishna; Basarkod, Prabhugoud I.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4895-4905

Abstract

In an era where networks are increasingly heterogeneous and multi-domain, establishing robust security models to protect data and network infrastructure is becoming ever more complex. Traditional intrusion detection systems (IDS) often struggle with novel or variant attacks that fall outside predefined rule sets, resulting in significant detection challenges. This paper proposes a methodologically refined approach leveraging data-driven insights and statistically robust feature selection to enhance the training dataset. The study presents a long short-term memory-autoencoder (LSTM-AE) based learning model designed for multi-class anomaly detection. The model's novelty lies in its application of distance metrics to define distinct thresholds for varied attack classifications, a strategy that significantly amplifies detection precision. Experimental results validate the superior performance of the proposed system, achieving 94.82% accuracy rate, outperforming similar existing works. The study also proactively addresses common issues of class imbalance and skewed data representation in benchmark datasets by strategically training the model on normal traffic, enhancing its capability to generalize and identify anomalies effectively.
Co-Authors Lakshminarayana, Sai Krishna