## 4 Experiment and Discussion
### Experimental Results  
Tables 3 and 4 report the link prediction results of the TBRm and baseline methods on the four IIoC data sets. On the data set in Table 3, the static KGE method lags far behind TA-DistMult or TTransE, because it cannot capture time dynamics. It can also be observed that the performance of all static KGE methods is usually better than that of HyTE. We believe that this is because HyTE slices the dynamic knowledge map with time into multiple static knowledge maps, which are represented independently on each static map, and lack coherence in time updates. Table 3 also shows that TBRm is significantly better than other baseline methods in BPLP and MOOC-Ub.  
We also observed in Table 4 that TBRm's performance on NFT and IE-IoTD is not always the best, especially on NFT. In fact, this is due to the excessive concentration of time information participating in the NFT data set. The excessive concentration of time information makes the time information data interval carried smaller, making it more difficult to distinguish triples. However, the Hyte method of slicing the time series knowledge  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**NFT**} & \multicolumn{4}{c}{**IE-IoTD**} \\ \cline{2-9}  & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline TransE & 33.67 & 17.26 & 48.87 & 66.31 & 28.42 & 20.52 & 59.67 & 45.87 \\ \hline DistMult & 32.53 & 17.64 & **49.63** & 64.41 & 25.68 & 19.43 & 61.36 & 42.94 \\ \hline TTransE & 49.57 & 32.98 & 35.65 & 70.69 & 30.26 & **25.20** & 48.47 & 50.64 \\ \hline HyTE & 45.39 & 26.35 & **49.63** & **75.64** & **22.63** & 24.12 & 42.13 & 52.12 \\ \hline TA-DistMult & 50.26 & **40.28** & 45.61 & 37.38 & 28.92 & 21.10 & 60.74 & 48.67 \\ \hline TBRm & **30.04** & 35.99 & 46.97 & 74.32 & 25.39 & 24.59 & **64.79** & **55.19** \\ \hline \hline \end{tabular}  
* The best results are shown in bold.  
\end{table}
Table 4: Results (percentages) on the NFT and IE-IoTD datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**BPLP**} & \multicolumn{4}{c}{**MOOC-Ub**} \\ \cline{2-9}  & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline TransE & 16.47 & 4.62 & 25.84 & 34.78 & 17.54 & 5.62 & 33.04 & 45.69 \\ \hline DistMult & 23.54 & 5.66 & 9.64 & 39.16 & 18.95 & 14.23 & 25.43 & 37.42 \\ \hline TTransE & 10.63 & 13.14 & 24.93 & 25.64 & 9.63 & 5.46 & 9.65 & 17.56 \\ \hline HyTE & 19.35 & 21.04 & 28.43 & 40.62 & 21.34 & 16.38 & 25.63 & 36.66 \\ \hline TA-DistMult & 9.61 & 7.25 & 8.96 & 15.34 & 12.57 & 8.94 & 15.82 & 23.13 \\ \hline TBRm & **8.72** & **22.63** & **29.97** & **45.49** & **8.35** & **20.32** & **40.98** & **46.49** \\ \hline \hline \end{tabular}  
* The best results are shown in bold.  
\end{table}
Table 3: Results (percentages) on the BPLP and MOOC-Ub datasets.  
map by time has a good performance on this data set. Although TBRm performs better on other data sets with a more balanced distribution of time information, how to solve this shortcoming of TBRm is a meaningful direction for further research.  
Table 5 shows the percentage of TBRm's four performance indicators on the Edge-IIoTset dataset. We can see that in addition to Hits@1, TBRm has achieved relatively high scores in the tests of the other three performance indicators. Analyzing the reasons, it was found that in the Edge-IIoTset data set, the number of subject entities of the network attacker is too large, however, there are only 14 kinds of attacks that act as predicates, and the gap between subject entity set and predicate relation set is too obvious. Therefore, in the process of link prediction, the probability of successful prediction for the first time will become very small, and the percentage value of Hits@1 will decrease accordingly.