[HTML][HTML] Ensemble machine learning approach for classification of IoT devices in smart home

I Cvitić, D Peraković, M Periša, B Gupta - International Journal of Machine …, 2021 - Springer
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 …

Detection of distributed denial of service (DDoS) attacks in IOT based monitoring system of banking sector using machine learning models

U Islam, A Muhammad, R Mansoor, MS Hossain… - Sustainability, 2022 - mdpi.com
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 …

Evidence-based clinical engineering: Machine learning algorithms for prediction of defibrillator performance

A Badnjević, LG Pokvić, M Hasičić, L Bandić… - … Signal Processing and …, 2019 - Elsevier
Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk 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 …

An overview of distributed denial of service traffic detection approaches

I Cvitić, D Peraković, M Periša… - Promet …, 2019 - hrcak.srce.hr
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 …

Detection of DDoS attack and classification using a hybrid approach

S Nandi, S Phadikar, K Majumder - 2020 Third ISEA …, 2020 - ieeexplore.ieee.org
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 …

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 …

An efficient feature selection approach for intrusion detection system using decision tree

A Das, BS Sunitha - International Journal of Advanced …, 2022 - search.proquest.com
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 …

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 …

Feature selection for intrusion detection system in a cluster-based heterogeneous wireless sensor network

O Osanaiye, O Ogundile, F Aina… - … Series: Electronics and …, 2019 - casopisi.junis.ni.ac.rs
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 …