Applications of blockchain technology in clinical trials: review and open challenges

IA Omar, R Jayaraman, K Salah, I Yaqoob… - Arabian Journal for …, 2021 - Springer
Blockchain technology has disclosed unprecedented opportunities in the healthcare sector
by unlocking the true value of interoperability. Specifically, the striking features of blockchain …

A systematic literature review on machine learning and deep learning approaches for detecting DDoS attacks in software-defined networking

AA Bahashwan, M Anbar, S Manickam, TA Al-Amiedy… - Sensors, 2023 - mdpi.com
Software-defined networking (SDN) is a revolutionary innovation in network technology with
many desirable features, including flexibility and manageability. Despite those advantages …

A hybrid deep learning-driven SDN enabled mechanism for secure communication in Internet of Things (IoT)

D Javeed, T Gao, MT Khan, I Ahmad - Sensors, 2021 - mdpi.com
The Internet of Things (IoT) has emerged as a new technological world connecting billions of
devices. Despite providing several benefits, the heterogeneous nature and the extensive …

Obfuscated memory malware detection in resource-constrained IoT devices for smart city applications

SS Shafin, G Karmakar, I Mareels - Sensors, 2023 - mdpi.com
Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems,
including smart city applications, for its ability to evade detection through concealment …

[Retracted] An AI‐Driven Hybrid Framework for Intrusion Detection in IoT‐Enabled E‐Health

F Wahab, Y Zhao, D Javeed… - Computational …, 2022 - Wiley Online Library
E‐health has grown into a billion‐dollar industry in the last decade. Its device's high
throughput makes it an obvious target for cyberattacks, and these environments desperately …

Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation

F Ullah, S Ullah, MR Naeem, L Mostarda, S Rho… - Sensors, 2022 - mdpi.com
Currently, Android apps are easily targeted by malicious network traffic because of their
constant network access. These threats have the potential to steal vital information and …

Artificial intelligence, machine learning, and deep learning for cybersecurity solutions: a review of emerging technologies and applications

M Paramesha, NL Rane, J Rane - Partners Universal Multidisciplinary …, 2024 - pumrj.com
The increasing intricacy and advancement of online dangers have required the creation of
more advanced cybersecurity methods, with artificial intelligence (AI) becoming a crucial …

LIPAuth: Hand-dependent Light Intensity Patterns for Resilient User Authentication

H Cao, D Liu, H Jiang, R Wang, Z Chen… - ACM Transactions on …, 2023 - dl.acm.org
Authentication mechanisms deployed on access control systems undertake the
responsibility of judging user identity to prevent unauthorized individuals from illegally …

The convergence of deep learning and computer vision: smart city applications and research challenges

D Kothadiya, A Chaudhari, R Macwan… - … & Security (ICIIC …, 2021 - atlantis-press.com
In recent years, deep learning strategies started to outshine traditional machine learning
methods in a few fields, with Computer Vision being one of the most noticeable ones. The …

GRU-SVM Based Threat Detection in Cognitive Radio Network

JC Clement - Sensors, 2023 - mdpi.com
Cognitive radio networks are vulnerable to numerous threats during spectrum sensing.
Different approaches can be used to lessen these attacks as the malicious users degrade …