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EURASIP Journal on Wireless Communications and Networking
Fig. 5 | EURASIP Journal on Wireless Communications and Networking

Fig. 5

From: Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set

Fig. 5

Binary Classification Confusion Matrices. a–c are of RF with mean, multiple and regression imputation, d–f are of SVM with mean, multiple and regression imputation and g–i are of ANN with mean, multiple and regression imputation. In Cji, C is class, i is the label which is 0 or 1 and j shows that 0 is normal(N) and 1 is malicious(M) packet. In RF and SVM, there is very less % of false alarms but in ANN that % is slightly higher which is mostly due to the malicious traffic being classified as normal

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