Web: http://arxiv.org/abs/2108.12732

Nov. 24, 2022, 2:10 a.m. | Mohanad Sarhan, Siamak Layeghy, Marius Portmann

cs.CR updates on arXiv.org arxiv.org

Internet of Things (IoT) networks have become an increasingly attractive
target of cyberattacks. Powerful Machine Learning (ML) models have recently
been adopted to implement network intrusion detection systems to protect IoT
networks. For the successful training of such ML models, selecting the right
data features is crucial, maximising the detection accuracy and computational
efficiency. This paper comprehensively analyses feature sets' importance and
predictive power for detecting network attacks. Three feature selection
algorithms: chi-square, information gain and correlation, have been utilised …

analysis detection intrusion intrusion detection iot machine machine learning

Senior Cloud Security Engineer

@ HelloFresh | Berlin, Germany

Senior Security Engineer

@ Reverb | Remote, US

I.S. Security Analyst

@ YVFWC | Yakima, WA

Territory Account Manager - Cybersecurity - Little Rock

@ Optiv | Little Rock, AR

Cybersecurity Network Engineer

@ Bitcoin Depot | Remote

Senior Solutions Architect, Prisma Cloud - Visibility, Compliance, and Security (EMEA)

@ Palo Alto Networks | Manchester, United Kingdom

Cloud Security Engineer

@ Snow Software | Solna, Sweden

Senior Security Engineer - 12 month contract - Outside IR35 - Northampton Area

@ Eurofins | Northampton, United Kingdom

Penetration Tester

@ Family Zone | Melbourne, Australia

Senior Consultant - II - Fortinet

@ Optiv | Bengaluru, Karnataka

Snr Professional Services Consultant - XSIAM

@ Palo Alto Networks | Madrid, Spain

Data Governor and Security Specialist

@ Dynatrace | Milan, Italy