ArticleScientific reports2024
Optimizing neural networks using spider monkey optimization algorithm for intrusion detection system.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Metaheuristic-driven automatic ARIMA order selection using spider monkey optimization for agricultural production forecasting.MethodsX · 2026Article
- A new intrusion detection method using ensemble classification and feature selection.Scientific reports · 2025Article
- Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm.PeerJ. Computer science · 2025Article
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Authors and funding
4 authors.
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Abstract
The constantly changing nature of cyber threats presents unprecedented difficulties for people, institutions, and governments across the globe. Cyber threats are a major concern in today's digital world like hacking, phishing, malware, and data breaches. These can compromise anyone's personal information and harm the organizations. An intrusion detection system plays a vital responsibility to identifying abnormal network traffic and alerts the system in real time if any malicious activity is detected. In our present research work Artificial Neural Networks (ANN) layers are optimized with the execution of Spider Monkey Optimization (SMO) to detect attacks or intrusions in the system. The developed model SMO-ANN is examined using publicly available dataset Luflow, CIC-IDS 2017, UNR-IDD and NSL -KDD to classify the network traffic as benign or attack type. In the binary Luflow dataset and the multiclass NSL-KDD dataset, the proposed model SMO-ANN has the maximum accuracy, at 100% and 99%, respectively.
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