Results 21 to 30 of about 6,155,073 (227)
Symmetry, 2022 Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS)
Mona A. Alduailij +5 more
semanticscholar +1 more source
Federated Deep Learning for Zero-Day Botnet Attack Detection in IoT-Edge Devices
IEEE Internet of Things Journal, 2021 Deep learning (DL) has been widely proposed for botnet attack detection in Internet of Things (IoT) networks. However, the traditional centralized DL (CDL) method cannot be used to detect the previously unknown (zero-day) botnet attack without breaching ...
S. Popoola +5 more
semanticscholar +1 more source
Plenoptic Face Presentation Attack Detection [PDF]
IEEE Access, 2020 The vulnerability of current face recognition systems to presentation attacks significantly limits their application in biometrics. Herein, we present a passive presentation attack detection method based on a complete plenoptic imaging system which can derive the complete plenoptic function of light rays using a single detector.
Shuaishuai Zhu +5 more
openaire +3 more sources
Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs
Cybersecurity, 2023 Early attack detection is essential to ensure the security of complex networks, especially those in critical infrastructures. This is particularly crucial in networks with multi-stage attacks, where multiple nodes are connected to external sources ...
Armita Kazeminajafabadi, Mahdi Imani
semanticscholar +1 more source
Machine Learning Methods for Attack Detection in the Smart Grid [PDF]
, 2015 Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings.
Esnaola, Inaki +4 more
core +2 more sources
Hybrid Deep Learning for Botnet Attack Detection in the Internet-of-Things Networks
IEEE Internet of Things Journal, 2021 Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large.
S. Popoola +4 more
semanticscholar +1 more source
Applied Computing and Informatics, 2019 The internet network is mostly victimized to the Distributed Denial of Service (DDOS) attack, which is one that intentionally occupies the computing resources and bandwidth in order to deny that services to potential users.
Indraneel Sreeram +1 more
doaj +1 more source
Necessary detection efficiencies for secure quantum key distribution and bound randomness [PDF]
, 2016 In recent years, several hacking attacks have broken the security of quantum cryptography implementations by exploiting the presence of losses and the ability of the eavesdropper to tune detection efficiencies.
Acín, Antonio +4 more
core +3 more sources
ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks
J. Sens. Actuator Networks, 2022 Due to the prompt expansion and development of intelligent systems and autonomous, energy-aware sensing devices, the Internet of Things (IoT) has remarkably grown and obstructed nearly all applications in our daily life.
Q. A. Al-Haija, Mu'awya Al-Dala'ien
semanticscholar +1 more source
TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection [PDF]
IEEE Signal Processing Letters, 2021 3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks. Recently, remote photoplethysmography (rPPG) has been developed as an intrinsic liveness clue for 3D mask PAD without ...
Zitong Yu +3 more
semanticscholar +1 more source