Results 111 to 120 of about 17,788 (216)

Liquid neural networks for adaptive DDoS detection in IoT and SDN environments. [PDF]

Sci Rep
Raza M, Sattar MA, Jahangir Z, Billah M.
europepmc   +1 more source

An overview of DDoS attacks

, 2014
© The Author(s) 2014. In this chapter, we firstly review the short history of denial of service (DoS) and distributed denial of service (DDoS) attacks.
S Yu (7291433)
core  

Intelligent DDoS Attack Detection in Software-Defined Networks Using Explainable Machine Learning. [PDF]

Sensors (Basel)
Malik JA, Naz NS, Saleem M, Khan MA.
europepmc   +1 more source

Optimization-driven machine learning intrusion detection systems for resource-constrained internet of things challenges: a systematic review and future directions. [PDF]

PeerJ Comput Sci
Al-Matarneh FM, Al-Hameli BA.
europepmc   +1 more source

TSD-DDoS: A time-series dataset for TCP flooding attack detection and severity assessment for server health monitoring. [PDF]

Data Brief
Kumar SR, Vatsavayi VK.
europepmc   +1 more source

Sequential submodular feature-sample selection for lightweight and update-efficient IoT intrusion detection. [PDF]

Sci Rep
Amr MN, Elliethy A, Mekkawy T, Mahran A.
europepmc   +1 more source

DDoS: Ett evolverande fenomen / DDoS: An evolving phenomenon

, 2012
Internetfenomenet ”Distributed Denial of Service”, förkortat DDoS, beskrivs ofta som ett av destörsta hoten mot Internet idag. Genom att utnyttja den grundläggande strukturen i kommunikationmellan nätverk och datorer kan kriminella blockera och stänga ute webbplatser och -tjänster frånanvändare, samtidigt som det är mycket svårt för offret och ...
openaire   +1 more source

Privacy-preserving intrusion detection in IoT smart homes using a federated hybrid 1D-CNN-LSTM model with explainable AI. [PDF]

Sci Rep
Abdelhady G, Hussein KW, Gad IAA.
europepmc   +1 more source

Методи протидії DDoS-атакам [PDF]

У сучасних умовах цифровізації DDoS-атаки становлять одну з найсерйозніших загроз кібербезпеці, здатних паралізувати роботу інформаційних систем, завдати значних фінансових збитків та підірвати довіру користувачів.
Чорненька, С. В., Лашко, О. О.   +1 more
core  

LITH: A Lightweight Behavioral Anomaly-to-Hunt-Lead Framework for IoT Sensor Ecosystems. [PDF]

Sensors (Basel)
Bamashmoos F, Khairullah E.
europepmc   +1 more source