Authors : Khandakar Rabbi Ahmed, Md Anisur Rahman Chowdhury, Md Razaul Karim, Md Sayham Khan, Md Afjal Hosien, Shah Tawkir Nesar, Ronny Bazan-Antequera
Publication date : 2025/7/31
Conference : 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)
Pages : 1-6
Publisher : IEEE
Description : Internet of Things (IoT) security challenges grow stronger as the network expands and more devices join the network, even though many devices contain built-in security flaws. Traditional intrusion detection systems (IDS) based on signature detection struggle to identify new cyber threats, requiring advanced solutions. The proposed research develops a Deep Neural Network (DNN)-based IDS, which addresses IoT security through deep learning methods that enhance anomaly detection. This model harnesses network traffic data examination to detect observed and unidentified attack series because of its robust feature exploration functionality. The benchmark IoT dataset evaluation shows that the model attains superior accuracy rates for different attack types, and the model demonstrates its greatest strength in detecting wormhole attacks because it reaches a detection with an accuracy of 95.3 %, precision of 94.7 …
Total citations : Cited by 26
Scholar articles :
KR Ahmed, MAR Chowdhury, MR Karim, MS Khan… – 2025 International Conference on Quantum Photonics …, 2025