all InfoSec news
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection. (arXiv:2111.13149v2 [cs.CR] CROSS LISTED)
cs.CR updates on arXiv.org arxiv.org
The digital transformation faces tremendous security challenges. In
particular, the growing number of cyber-attacks targeting Internet of Things
(IoT) systems restates the need for a reliable detection of malicious network
activity. This paper presents a comparative analysis of supervised,
unsupervised and reinforcement learning techniques on nine malware captures of
the IoT-23 dataset, considering both binary and multi-class classification
scenarios. The developed models consisted of Support Vector Machine (SVM),
Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine
(LightGBM), Isolation Forest (iForest), …
analysis detection intrusion intrusion detection iot machine machine learning