Machine learning cryptography methods for IoT in healthcare

Table 1 Model performance comparison of accuracy for different LWC algorithms and ML models on 16 KB-2048 KB test message

File Size	Algorithm	Decision Tree	Random Forest	SVM	MLP	KNN
16 KB	AES	0.91	0.96	0.97	0.94	0.98
	PRESENT	0.88	0.92	0.89	0.91	0.92
	SIMON	0.87	0.92	0.88	0.91	0.92
	XTEA	0.89	0.95	0.94	0.93	0.96
	PRINCE	0.86	0.90	0.86	0.88	0.89
	MSEA	0.87	0.92	0.88	0.91	0.92
	LEA	0.88	0.92	0.89	0.91	0.92
	RECTANGLE	0.89	0.95	0.94	0.93	0.96
64 KB	AES	0.982	0.986	0.991	0.983	0.962
	PRESENT	0.890	0.929	0.987	0.954	0.917
	SIMON	0.875	0.908	0.983	0.935	0.898
	XTEA	0.978	0.983	0.991	0.984	0.968
	PRINCE	0.852	0.897	0.985	0.905	0.853
	MSEA	0.920	0.957	0.987	0.947	0.907
	LEA	0.983	0.986	0.992	0.985	0.964
	RECTANGLE	0.978	0.986	0.992	0.985	0.965
256 KB	AES	0.95	0.96	0.97	0.98	0.92
	PRESENT	0.88	0.91	0.92	0.93	0.83
	SIMON	0.89	0.91	0.92	0.94	0.84
	XTEA	0.87	0.89	0.90	0.92	0.82
	PRINCE	0.90	0.92	0.93	0.94	0.85
	MSEA	0.86	0.87	0.88	0.89	0.80
	LEA	0.91	0.93	0.94	0.95	0.87
	RECTANGLE	0.92	0.94	0.95	0.96	0.88
512 KB	AES	0.892	0.956	0.945	0.942	0.921
	PRESENT	0.827	0.918	0.904	0.894	0.867
	SIMON	0.874	0.942	0.930	0.925	0.905
	XTEA	0.761	0.886	0.858	0.844	0.824
	PRINCE	0.803	0.903	0.879	0.863	0.848
	MSEA	0.709	0.819	0.801	0.788	0.760
	LEA	0.769	0.851	0.836	0.824	0.787
	RECTANGLE	0.821	0.902	0.883	0.878	0.849
1024 KB	AES	0.845	0.943	0.932	0.928	0.896
	PRESENT	0.783	0.911	0.899	0.887	0.853
	SIMON	0.832	0.932	0.921	0.918	0.884
	XTEA	0.705	0.868	0.832	0.815	0.794
	PRINCE	0.746	0.879	0.834	0.817	0.806
	MSEA	0.652	0.784	0.764	0.745	0.707
	LEA	0.713	0.816	0.802	0.791	0.739
	RECTANGLE	0.785	0.875	0.853	0.848	0.821
2048 KB	AES	0.827	0.938	0.923	0.919	0.891
	PRESENT	0.769	0.896	0.881	0.870	0.838
	SIMON	0.814	0.924	0.909	0.903	0.879
	XTEA	0.681	0.853	0.826	0.808	0.782
	PRINCE	0.726	0.865	0.836	0.819	0.804
	MSEA	0.624	0.767	0.749	0.732	0.698
	LEA	0.691	0.801	0.789	0.776	0.728
	RECTANGLE	0.761	0.867	0.842	0.838	0.807

ISSN: 1472-6947