Results 71 to 80 of about 27,602 (172)

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

Enabling pandemic‐resilient healthcare: Narrowband Internet of Things and edge intelligence for real‐time monitoring

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract The Internet of Things (IoT) in deploying robotic sprayers for pandemic‐associated disinfection and monitoring has garnered significant attention in recent research. The authors introduce a novel architectural framework designed to interconnect smart monitoring robotic devices within healthcare facilities using narrowband Internet of Things ...
Md Motaharul Islam, Mohammad Kamrul Hasan, Shayla Islam, Mohammed Balfaqih, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Nurhizam Safie, Zaheed Ahmed Bhuiyan, Rahul Thakkar, Taher M. Ghazal   +9 more
wiley   +1 more source

A Probability‐Aware AI Framework for Reliable Anti‐Jamming Communication

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Adversarial jamming attacks have increased on communication systems, causing distortion and threatening transmissions. Typical attacks rely on traditional, well‐defined cryptographic protocols and frequency‐hopping techniques. Nevertheless, these techniques become vulnerable when facing intelligent jammers.
Tawfeeq Shawly, Ahmed A. Alsheikhy
wiley   +1 more source

Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme

Applied Sciences, 2019
Computer networks are facing threats of ever-increasing frequency and sophistication. Encryption is becoming the norm in both legitimate and malicious network traffic.
Muhammad Tahir, Mingchu Li, Naeem Ayoub, Muhammad Aamir   +3 more
doaj   +1 more source

Multiterminal High‐Voltage Direct Current Projects: A Comprehensive Assessment and Future Prospects

High Voltage, EarlyView.
ABSTRACT Multiterminal high‐voltage direct current (MT‐HVDC) systems are an important part of modern power systems, addressing the need for bulk power delivery and efficient renewable energy integration. This paper provides a comprehensive overview of recent advances in MT‐HVDC technology, including launched projects and ongoing initiatives.
Mohammad Hossein Mousavi, Hassan Moradi, Kumars Rouzbehi, Vijay K. Sood   +3 more
wiley   +1 more source

Enmob: Unveil the Behavior with Multi-flow Analysis of Encrypted App Traffic

Cybersecurity
In the contemporary digital landscape, mobile applications have become the predominant conduit for internet connectivity and daily tasks. Simultaneously, the advent of application encryption technology has safeguarded users’ privacy.
Ge Mengmeng, Feng Ruitao, Liu Likun, Yu Xiangzhan, Sachidananda Vinay, Xie Xiaofei, Liu Yang   +6 more
doaj   +1 more source

A Multi-Feature Semantic Fusion Machine Learning Architecture for Detecting Encrypted Malicious Traffic

Journal of Cybersecurity and Privacy
With the increasing sophistication of network attacks, machine learning (ML)-based methods have showcased promising performance in attack detection. However, ML-based methods often suffer from high false rates when tackling encrypted malicious traffic ...
Shiyu Tang, Fei Du, Zulong Diao, Wenjun Fan   +3 more
doaj   +1 more source

$Deep-Full-Range$ : A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework

IEEE Access, 2019
With the rapid evolution of network traffic diversity, the understanding of network traffic has become more pivotal and more formidable. Previously, traffic classification and intrusion detection require a burdensome analyzing of various traffic features
Yi Zeng, Huaxi Gu, Wenting Wei, Yantao Guo   +3 more
doaj   +1 more source

TransGraphNet: robust detection of malicious encrypted network traffic via transformer and graph neural models [PDF]

PeerJ Computer Science
As encrypted network traffic becomes more prevalent, cybercriminals increasingly conceal their malicious activities within encrypted session contents. Traditional methods for detecting malicious encrypted traffic focus on inspecting the plaintext payload
Qiang Shi, Dacheng Shi, Liukun He, Qingjun Xiao, Liang Zheng, Jun Ma, Jingdong He, Qifan Zhang   +7 more
doaj   +2 more sources

Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic Feature Extraction. [PDF]

Comput Intell Neurosci, 2023
Long G, Zhang Z.
europepmc   +1 more source