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iChip01:24

iChip

The cultivation of environmental microorganisms has long been hindered by the inability to replicate complex native conditions in vitro. The isolation chip (iChip) addresses this limitation by facilitating the growth of previously uncultivable microorganisms through in situ incubation. Designed for high-throughput microbial cultivation, the iChip comprises hundreds of microchambers, each capable of housing a single microbial cell. These microchambers are loaded with a mixture of molten agar and...

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Binary Chimp Optimization Algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor

Mohammed Aljebreen1, Manal Abdullah Alohali2, Muhammad Kashif Saeed3

  • 1Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

A novel Binary Chimp Optimization Algorithm with Machine Learning based Intrusion Detection (BCOA-MLID) secures Internet of Things (IoT)-assisted Wireless Sensor Networks (WSNs). This method significantly improves intrusion detection accuracy, outperforming existing models.

Keywords:
chimp optimization algorithmfeature selectionintrusion detection systemmachine learningwireless sensor networks

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Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSNs) integrated with the Internet of Things (IoT) enhance data collection and analysis efficiency.
  • Security is a critical concern for interconnected IoT and WSN systems, requiring robust protection against diverse threats.
  • Existing intrusion detection methods may lack the efficiency and accuracy needed for complex IoT-WSN environments.

Purpose of the Study:

  • To introduce an advanced technique, the Binary Chimp Optimization Algorithm with Machine Learning based Intrusion Detection (BCOA-MLID), for securing IoT-WSN environments.
  • To enhance the effectiveness of intrusion detection by optimizing feature selection and employing a sophisticated classification model.
  • To demonstrate the superiority of the proposed BCOA-MLID technique in identifying and mitigating various network attacks.

Main Methods:

  • Data normalization is performed as a preliminary step in the BCOA-MLID technique.
  • The Binary Chimp Optimization Algorithm (BCOA) is utilized for optimal feature selection, boosting intrusion detection performance.
  • A class-specific cost-sensitive extreme learning machine (ELM) model, optimized with a sine cosine algorithm, is employed for intrusion classification.

Main Results:

  • The BCOA-MLID technique achieved a maximum accuracy of 99.36% on the Kaggle intrusion dataset.
  • The proposed method significantly outperformed benchmark models, including XGBoost (96.83% accuracy) and KNN-AOA (97.20% accuracy).
  • Experimental results validate the effectiveness of BCOA-MLID in accurately detecting intrusions within IoT-WSN systems.

Conclusions:

  • The BCOA-MLID technique offers a highly effective solution for enhancing the security of IoT-assisted Wireless Sensor Networks.
  • The optimized feature selection and advanced classification model contribute to superior intrusion detection accuracy.
  • This research provides a promising approach for securing critical IoT-WSN infrastructure against cyber threats.