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Updated: Jun 30, 2026

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

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

Published on: December 15, 2023

Software defined network intrusion system to detect malicious attacks in computer Internet of Things security using

Muhammad Mujahid¹, Abeer Rashad Mirdad¹, Faten S Alamri²

¹Artificial Intelligence & Data Analytics Lab, CCIS, Prince Sultan University, Riyadh, Saudi Arabia.

Peerj. Computer Science

|September 24, 2025

Summary

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This study introduces a lightweight intrusion detection system for Software-Defined Networking (SDN) using Long Short-Term Memory (LSTM) and Supervised Random Forest (SRF). The method achieves high accuracy in detecting various network attacks, enhancing SDN security.

Area of Science:

Computer Science
Network Security
Machine Learning

Background:

Software-Defined Networking (SDN) offers potential cost and service delivery benefits over traditional networks.
SDN architectures introduce unique security vulnerabilities, including a single point of failure.
Effective intrusion detection systems (IDS) are crucial for mitigating threats like DDoS, DoS, web attacks, and Bot-NET in SDN environments.

Purpose of the Study:

To propose a novel, lightweight method for detecting diverse network attacks within SDN environments.
To address the limitations of existing machine learning methods in terms of flexibility and accuracy for SDN intrusion detection.

Main Methods:

Developed a lightweight intrusion detection method utilizing a Long Short-Term Memory (LSTM) network to analyze temporal patterns in SDN data.

Keywords:

Deep extractor SRF Distributed DoS Intrusion detection IoT Network security Security risks

Related Experiment Videos

Last Updated: Jun 30, 2026

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

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

Published on: December 15, 2023

Employed Supervised Random Forest (SRF) for accurate attack prediction based on extracted features.

Utilized a preprocessed dataset comprising 207,146 rows and 84 features for training and validation.

Main Results:

Achieved 99.93% accuracy and a loss of 0.0090 for attack detection on the primary dataset.
Validated the method's effectiveness on a separate SDN dataset, yielding 99.43% accuracy for multi-class attack detection.
Demonstrated that the SRF model enhances overall efficiency by reducing computational complexity.

Conclusions:

The proposed lightweight LSTM and SRF method is highly effective for detecting various SDN attacks.
The approach offers improved accuracy and flexibility compared to existing machine learning techniques.
This solution enhances the security posture of SDN architectures.