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Optimizing neural networks using spider monkey optimization algorithm for intrusion detection system.

Deepshikha Kumari¹, Abhinav Sinha¹, Sandip Dutta¹

¹Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India.

Scientific Reports

|July 26, 2024

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This study introduces a novel Spider Monkey Optimization-Artificial Neural Network (SMO-ANN) model for enhanced cyber threat detection. The SMO-ANN model achieves high accuracy in identifying malicious network traffic, improving cybersecurity defenses.

Area of Science:

Cybersecurity and Artificial Intelligence
Network Intrusion Detection Systems

Background:

Cyber threats like hacking, phishing, and data breaches pose significant risks to individuals and organizations globally.
Intrusion detection systems are crucial for identifying abnormal network traffic and alerting against malicious activities in real-time.

Purpose of the Study:

To develop and evaluate an optimized Artificial Neural Network (ANN) model for detecting cyber attacks.
To enhance the accuracy and efficiency of intrusion detection systems through advanced optimization techniques.

Main Methods:

Optimization of Artificial Neural Network (ANN) layers using the Spider Monkey Optimization (SMO) algorithm.
Development of the SMO-ANN model for classifying network traffic as benign or malicious.
Evaluation of the SMO-ANN model using diverse datasets: Luflow, CIC-IDS 2017, UNR-IDD, and NSL-KDD.

Keywords:

Cyber security Deep learning Intrusion detection system Spider monkey optimization

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Main Results:

The SMO-ANN model demonstrated superior performance in classifying network traffic.
Achieved 100% accuracy on the binary Luflow dataset.
Attained 99% accuracy on the multiclass NSL-KDD dataset, indicating robust detection capabilities.

Conclusions:

The proposed SMO-ANN model offers a highly effective solution for real-time intrusion detection.
This research contributes to advancing cybersecurity by providing an accurate and efficient method for identifying cyber threats.