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Related Experiment Video

Updated: Jul 5, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

CyberDetect MLP a big data enabled optimized deep learning framework for scalable cyberattack detection in IoT

Talluri Upender1, M Neelakantappa2, C Prakasa Rao3

  • 1Department of Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India. talluri.upender@gmail.com.

Scientific Reports
|November 19, 2025
PubMed
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CyberDetect-MLP offers a scalable, explainable deep learning framework for detecting Internet of Things (IoT) cyberattacks. It achieves high accuracy, addressing limitations of traditional systems in big data environments.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Big Data Analytics

Background:

  • The proliferation of Internet of Things (IoT) ecosystems generates vast, multidimensional data, creating significant attack surfaces for cyber threats.
  • Traditional intrusion detection systems (IDS) and many machine learning (ML) models struggle with scalability, interpretability, and high-dimensional data streams, limiting their effectiveness in large-scale IoT applications.

Purpose of the Study:

  • To propose CyberDetect-MLP, a scalable, explainable, big data-enabled, and optimized deep learning framework for IoT cyberattack detection.
  • To address the limitations of existing IDS and ML models in handling the complexities of IoT security.

Main Methods:

  • Utilized Apache Spark for distributed data ingestion and preprocessing.
  • Implemented Mutual Information-based feature selection.
Keywords:
Big data analyticsCyberattack detectionDeep learningIntrusion detection systemIoT security

Related Experiment Videos

Last Updated: Jul 5, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

  • Employed a multi-layer perceptron (MLP) with batch normalization, dropout, and cosine annealing scheduling.
  • Integrated an optional explainable AI (XAI) module using Grad-CAM and SHAP for transparency.

    • Main Results:

    • CyberDetect-MLP achieved 98.87% accuracy and 99.10% ROC-AUC on the TON_IoT dataset, outperforming baseline models like Random Forest, XGBoost, and vanilla MLP.
    • Ablation studies and explainability evaluations confirmed the framework's robustness and trustworthiness.

    Conclusions:

    • The proposed CyberDetect-MLP framework effectively bridges the gap between big data analytics and interpretable deep learning for cybersecurity.
    • It provides an end-to-end IDS solution suitable for real-time applications in smart cities, industrial IoT, and critical infrastructure.
    • The framework's public availability promotes reproducibility and transparency in IoT security research.