Results 221 to 230 of about 67,029 (331)
Dataset of SCADA traffic captures from a medical waste incinerator with injected cyberattacks. [PDF]
Al-Duwairi B +3 more
europepmc +1 more source
Design of False Data Injection Attacks in a Cyber-Physical System Using Gaussian Distribution [PDF]
Sushree Padhan, Ashok Kumar Turuk
openalex +1 more source
Mission Aware Cyber‐Physical Security
ABSTRACT Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets.
Georgios Bakirtzis +3 more
wiley +1 more source
Secure TPMS Data Transmission in Real-Time IoV Environments: A Study on 5G and LoRa Networks. [PDF]
Niranjan DK, Supriya M, Tiberti W.
europepmc +1 more source
Effect of ‘combing’ on intramuscular sedation in pigs in a clinical setting—A randomised trial
Abstract Background Pigs pose distinct challenges to the anaesthetist due to their temperament, the limitations of manual restraint and species‐specific anatomical features. Despite the importance of minimising stress during clinical procedures, gentle handling techniques tailored for pigs in a clinical setting have yet to be clearly described or ...
Kaitlynn C. Ban +5 more
wiley +1 more source
Graph neural network‐based attack prediction for communication‐based train control systems
Abstract The Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions.
Junyi Zhao +3 more
wiley +1 more source
A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis. [PDF]
Wijethilaka RWKS, Yapa K, Siriwardena D.
europepmc +1 more source
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 +4 more
wiley +1 more source

