Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Sardroud, Asghar A. Asgharian
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Intrusion detection based on image transformations and data augmentation Abood, Nada Ali; Sardroud, Asghar A. Asgharian
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5594-5603

Abstract

The increasing growth of users and communication networks in different platforms has led to the emergence of various types of network attacks. intrusion detection systems (IDS) are one of the important solutions to cope with these problems. An IDS determines whether incoming traffic is intrusive or normal. IDSs often achieve high efficiency with methods based on deep neural networks. However, one of the shortcomings of these methods is the lack of sufficient attention to the spatial features in the data. This research presents an intrusion detection method based on image transformations and data augmentation is presented. In the proposed method, the intrusion detection process is performed by transforming the traffic vector into an image using a convolutional neural network (CNN). Also, we use data augmentation and dimension reduction techniques to increase accuracy and reduce complexity in the proposed method. Simulation results on network security laboratory - knowledge discovery and data mining (NSL-KDD) show that the proposed IDS can classify intrusion traffic with an accuracy of 97.58%.
Flow-guided long short-term memory with adaptive directional learning for robust distributed denial of service attack detection in software-defined networking Ibadi, Huda Mohammed; Sardroud, Asghar A. Asgharian
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5484-5496

Abstract

A software-defined networking (SDN) architecture is designed to improve network agility by decoupling the control and data planes, but while much more flexible, also makes networks more vulnerable to threats, such as distributed denial of service (DDoS) attacks. In this study we present a novel detection model, the flow-guided long short-term memory (LSTM) network with adaptive directional learning (ADL), for the mitigation of DDoS attacks in software defined networking (SDN) environments. While the methodology is based on a flow direction algorithm (FDA), which analyzes traffic patterns and detects anomalies from directional flow behavior. The proposed method integrates FDA in LSTM-based threat detection frameworks within internet of things (IoT) networks, thereby yielding enhanced detection accuracy, as well as a real-time security threat response. The experimental evaluation on two benchmark datasets, namely the InSDN dataset and a real-time dataset utilizing a Mininet and POX controller setup, shows that a detection rate of 99.85% and 99.72%, respectively, thereby showcasing the proposed model’s ability to differentiate between legitimate and malicious network traffic.
Co-Authors Abood, Nada Ali Ibadi, Huda Mohammed