[HTML][HTML] Ensemble machine learning approach for classification of IoT devices in smart home
The emergence of the Internet of Things (IoT) concept as a new direction of technological
development raises new problems such as valid and timely identification of such devices …
development raises new problems such as valid and timely identification of such devices …
Detection of distributed denial of service (DDoS) attacks in IOT based monitoring system of banking sector using machine learning models
Cyberattacks can trigger power outages, military equipment problems, and breaches of
confidential information, ie, medical records could be stolen if they get into the wrong hands …
confidential information, ie, medical records could be stolen if they get into the wrong hands …
Evidence-based clinical engineering: Machine learning algorithms for prediction of defibrillator performance
Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk of
performance accuracy and safety deviations effecting the clinical accuracy and efficiency of …
performance accuracy and safety deviations effecting the clinical accuracy and efficiency of …
DDoS attack detection with feature engineering and machine learning: the framework and performance evaluation
M Aamir, SMA Zaidi - International Journal of Information Security, 2019 - Springer
This paper applies an organized flow of feature engineering and machine learning to detect
distributed denial-of-service (DDoS) attacks. Feature engineering has a focus to obtain the …
distributed denial-of-service (DDoS) attacks. Feature engineering has a focus to obtain the …
An overview of distributed denial of service traffic detection approaches
Sažetak The availability of information and communication (IC) resources is a growing
problem caused by the increase in the number of users, IC services, and the capacity …
problem caused by the increase in the number of users, IC services, and the capacity …
Detection of DDoS attack and classification using a hybrid approach
In the area of cloud security, detection of DDoS attack is a challenging task such that
legitimate users use the cloud resources properly. So in this paper, detection and …
legitimate users use the cloud resources properly. So in this paper, detection and …
Bot detection using unsupervised machine learning
W Wu, J Alvarez, C Liu, HM Sun - Microsystem Technologies, 2018 - Springer
This research focuses on bot detection through implementation of techniques such as traffic
analysis, unsupervised machine learning, and similarity analysis between benign traffic data …
analysis, unsupervised machine learning, and similarity analysis between benign traffic data …
An efficient feature selection approach for intrusion detection system using decision tree
The intrusion detection system has been widely studied and deployed by researchers for
providing better security to computer networks. The increasing volume of attacks, com-bined …
providing better security to computer networks. The increasing volume of attacks, com-bined …
Cyber attacks detection through machine learning in banking
MA GILL, N AHMAD, M KHAN, F ASGHAR… - Bulletin of Business …, 2023 - bbejournal.com
Cyberattacks may cause a wide range of problems, from power outages to broken military
equipment to the loss of vital information like patient medical records. Due to the huge …
equipment to the loss of vital information like patient medical records. Due to the huge …
Feature selection for intrusion detection system in a cluster-based heterogeneous wireless sensor network
Wireless sensor network (WSN) has become one of the most promising networking solutions
with exciting new applications for the near future. Notwithstanding the resource constrain of …
with exciting new applications for the near future. Notwithstanding the resource constrain of …