Evading Botnet Detectors Based on Flows and Random Forest with Adversarial Samples
Giovanni Apruzzese, Michele Colajanni
openalex +2 more sources
Correction: Okey et al. BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning. <i>Sensors</i> 2022, <i>22</i>, 7409. [PDF]
Okey OD +6 more
europepmc +1 more source
FORT-IDS: a federated, optimized, robust and trustworthy intrusion detection system for IIoT security. [PDF]
Mazroa AA.
europepmc +1 more source
Scalable architecture for autonomous malware detection and defense in software-defined networks using federated learning approaches. [PDF]
Ranpara R +3 more
europepmc +1 more source
Enhanced SqueezeNet model for detecting IoT-Bot attacks: A comprehensive approach. [PDF]
Bojarajulu B, Tanwar S, Singh TP.
europepmc +1 more source
eMUD: Enhanced Manufacturer Usage Description for IoT Botnets Prevention on Home WiFi Routers [PDF]
Syed Muhammad Sajjad +3 more
openalex +1 more source
Botnet detection in internet of things using stacked ensemble learning model. [PDF]
Ali M +6 more
europepmc +1 more source
Hyperparameter optimization of XGBoost and hybrid CnnSVM for cyber threat detection using modified Harris hawks algorithm. [PDF]
Elwahsh H +7 more
europepmc +1 more source
Simulation-based study of botnets and defense mechanisms against them
Alexey Konovalov +2 more
openalex +2 more sources
Generative Adversarial Networks for Intrusion Detection Systems: A Comprehensive Survey of Applications, Challenges, and Research Directions. [PDF]
Alauthman M +4 more
europepmc +1 more source

