Blockchain-based federated learning for securing internet of things: A comprehensive survey

W Issa, N Moustafa, B Turnbull, N Sohrabi… - ACM Computing …, 2023 - dl.acm.org
The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

Internet of things (IoT) security dataset evolution: Challenges and future directions

B Kaur, S Dadkhah, F Shoeleh, ECP Neto, P Xiong… - Internet of Things, 2023 - Elsevier
The evolution of mobile technologies has introduced smarter and more connected objects
into our day-to-day lives. This trend, known as the Internet of Things (IoT), has applications …

[HTML][HTML] HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system

MA Khan - Processes, 2021 - mdpi.com
Nowadays, network attacks are the most crucial problem of modern society. All networks,
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …

[PDF][PDF] Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection.

DS Khafaga, FK Karim, AA Abdelhamid… - … , Materials & Continua, 2023 - academia.edu
Managing physical objects in the network's periphery is made possible by the Internet of
Things (IoT), revolutionizing human life. Open attacks and unauthorized access are possible …

IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities

J Ashraf, M Keshk, N Moustafa, M Abdel-Basset… - Sustainable Cities and …, 2021 - Elsevier
The rapid proliferation of the Internet of Things (IoT) systems, has enabled transforming
urban areas into smart cities. Smart cities' paradigm has resulted in improved quality of life …

Deep learning for phishing detection: Taxonomy, current challenges and future directions

NQ Do, A Selamat, O Krejcar, E Herrera-Viedma… - Ieee …, 2022 - ieeexplore.ieee.org
Phishing has become an increasing concern and captured the attention of end-users as well
as security experts. Existing phishing detection techniques still suffer from the deficiency in …

[HTML][HTML] An explainable deep learning-enabled intrusion detection framework in IoT networks

M Keshk, N Koroniotis, N Pham, N Moustafa… - Information …, 2023 - Elsevier
Although the field of eXplainable Artificial Intelligence (XAI) has a significant interest these
days, its implementation within cyber security applications still needs further investigation to …

[HTML][HTML] A review of plant phenotypic image recognition technology based on deep learning

J Xiong, D Yu, S Liu, L Shu, X Wang, Z Liu - Electronics, 2021 - mdpi.com
Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In
recent years, deep learning has achieved significant breakthroughs in image recognition …

Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review

SW Lee, M Mohammadi, S Rashidi… - Journal of Network and …, 2021 - Elsevier
Providing a high-performance Intrusion Detection System (IDS) can be very effective in
controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative …

On the performance of machine learning models for anomaly-based intelligent intrusion detection systems for the internet of things

G Abdelmoumin, DB Rawat… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Anomaly-based machine learning-enabled intrusion detection systems (AML-IDSs) show
low performance and prediction accuracy while detecting intrusions in the Internet of Things …