Results 41 to 50 of about 1,235 (177)

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

IEEE Access
Nowadays, the Internet of Things (IoT) has become a rapid development; it can be employed by cyber threats in IoT devices. A correct system to recognize malicious attacks at IoT platforms became of major importance to minimize security threats in IoT ...
Louai A. Maghrabi, Sahar Shabanah, Turki Althaqafi, Dheyaaldin Alsalman, Sultan Algarni, Abdullah Al-Malaise Al-Ghamdi, Mahmoud Ragab   +6 more
doaj   +1 more source

Graph‐Based Generative Adversarial Network for Adaptive IoT Intrusion Detection

Engineering Reports, Volume 8, Issue 8, August 2026.
The study introduces a hybrid graph‐based generative adversarial network (G‐GAN) that integrates adversarial learning with graph attention mechanisms to enhance intrusion detection in IoT environments. G‐GAN significantly improves accuracy and adaptability by reducing false alarms and detecting emerging threats in dynamic, heterogeneous IoT networks ...
Meshari H. Alanazi, Aabid A. Mir, Asma A. Alhashmi, Faris Alanazi, Mohammad N. Alanazi, Majid Ali Nawaz, Yahia Said, Abdulbasit A. Darem   +7 more
wiley   +1 more source

Neutrosophic Set and Machine Learning Model for Identifying Botnet Attacks on IoT Effectively [PDF]

Neutrosophic Sets and Systems
Botnet attacks, in which attackers utilize reciprocal communications between IoT devices to undertake extensive harmful actions, are one of the most significant risks in WSNs.
Wasal S AL-Bash AL-Azzawi, Hassan W. Hilou, Nawfal H. warush, Hasan Meslmani, Ahmed A El-Douh, Ahmed Abdelhafeez   +5 more
doaj   +1 more source

Safeguarding Online Research in Eating Disorders Against Fraud: Increasing Risks and Practical Recommendations

International Journal of Eating Disorders, Volume 59, Issue 7, Page 1451-1468, July 2026.
ABSTRACT Objective Recent growth of online research has been accompanied by an increase in reports of fraudulent participants, which can significantly comprise research validity. Drawing from our experience using Qualtrics with open recruitment, existing literature, and emerging studies in eating disorders (ED), we outline the risk and provide simple ...
Jamie‐Lee Pennesi, Mia L. Pellizzer, Tracey D. Wade   +2 more
wiley   +1 more source

A Comprehensive Systematic Review of Cyber‐Physical Systems Security: Threats, Challenges, and Defense in ICS/OT Environments

Engineering Reports, Volume 8, Issue 7, July 2026.
Overview of the paper organization, illustrating the hierarchical structure of cybersecurity domains in ICS and CPS, including attack analysis, security approaches, offensive tactics, career guidance, and concluding discussions. ABSTRACT The convergence of operational technology (OT) with IP‐based information systems has exposed industrial control ...
M. A. Khalifa, Ahmad Taher Azar, Walid El‐Shafai   +2 more
wiley   +1 more source

Attack Model and Detection Scheme for Botnet on 6LoWPAN [PDF]

, 2009
Recently, Botnet has been used to launch spam-mail, key-logging, and DDoS attacks. Botnet is a network of bots which are controlled by attacker. A lot of detection mechanisms have been proposed to detect Botnet on wired network. However, in IP based sensor network environment, there is no detection mechanism for Botnet attacks.
Eung Jun Cho, Jin Ho Kim, Choong Seon Hong   +2 more
openaire   +1 more source

Graph–Time IoT IDS: Requirement‐Aligned Impact Evaluation

Engineering Reports, Volume 8, Issue 7, July 2026.
A multi‐view intrusion detection framework (IMPACT‐MVG) combines temporal behavior modeling and graph‐based interaction analysis to detect IoT network attacks. Impact‐centric evaluation using the ICSec score shows that the approach reduces operational damage from intrusions while maintaining efficient, explainable, and privacy‐aware security monitoring.
Kumkum Dubey, Isha Batra, Abhishek Bajpai, Arun Malik, Mohammed Ali Alshara, Tariqullah Jan, Naveed Ahmad, Ruhul Amin Khalil   +7 more
wiley   +1 more source

GA‐ANN: An Efficient Hybrid Deep Learning Scheme for Network Intrusion Detection in IoT

SECURITY AND PRIVACY, Volume 9, Issue 4, July/August 2026.
ABSTRACT Intrusion detection systems (IDS) are critical to the security of the dynamic internet of things (IoT) environment. The integration of Artificial Intelligence (AI) into IDS has substantially improved network security. Particularly, deep learning techniques have shown strong potential in addressing IoT security challenges.
Naveed Ahmed, M. Siraj Rathore, Azhar Mahmood, Muhammad Daniyal, Md Asri Ngadi   +4 more
wiley   +1 more source

Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment

IEEE Access
Cyber attacks are growing with the rapid development and wide use of internet technology. Botnet attack emerged as one of the most harmful attacks. Botnet identification is becoming challenging due to the numerous attack vectors and the ongoing evolution
Mudasir Ali, Mobeen Shahroz, Muhammad Faheem Mushtaq, Sultan Alfarhood, Mejdl Safran, Imran Ashraf   +5 more
doaj   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source