## 6 Results and Discussion  
Internal threats: the likelihood of internal threats mainly depends on the frequency of security attacks and data breaches caused by insider threats [59; 60; 61; 62; 63; 64; 65; 66; 67]. According to a report produced by Nucleus Cyber in 2019 [31], 60% of the surveyed organizations have experienced one or more insider attacks within the last 12 months in 2019. In addition, according to a threat report produced by Proof point [32], around 50% percent of the reported attacks analyzed were caused by criminal or malicious insiders in 2020. Therefore, the likelihood for threat T1 can be considered to be moderate. Algorithm 2 describes the process of encryption and hashing techniques. We carried out the experimental setup based on the proposed framework and algorithm. The simulations were carried out using the hyperledger fabric tool. In order to implement the proposed algorithm and smart contracts we used chain code for blockchain transactions. The parameters used for analysis include block creation, encryption time, decryption time, number of transactions, number of iterations, and number of nodes. Figure 9 shows the comparative analysis between the writing Merkle tree root and writing contribution data using hybrid deep-learning techniques using consortium blockchain in terms of number of tests carried out and execution time in seconds. We carried out up to 100 tests and the execution time was noted as up to 100. Figure 10 simulations were carried out on the experimental results from the number of devices and the service execution time. We provided a comparative analysis through the experiment based on a simulation of the proposed model and the benchmark model. From Figure 7, it is very obvious that our proposed model takes significantly less process and execution time as compared to the benchmark models. As presented in Figure 8, we carried out the simulations results based on training time based on the proposed hybrid deep-learning protocol (BLSTM + CNN) and the number of transactions. From the simulations results, it is very clear that the number of transactions is higher as compared to the benchmark models based on the training time. The simulation result in Figure 8 explains that our proposed system is more intelligent, and it transfers the blocks according to the requirements and quick access to the participants in the system, thus, it is time effective as well. As presented in Figure 9, we carried out simulation results based on number of records and the execution time. In Figure 11, we compare our proposed model with the benchmark model such as [12; 13; 14]. Using hybrid deep-learning techniques and choosing the blocks according to the requirements, our proposed model takes significantly less time as compared to the benchmark model. Thus, Figure 9 justifies that our proposed framework is efficient and intelligent. In Figure 10, we present an experimental analysis based on the number of rounds and the number of transactions through the proposed model. We tested our proposed model based on different nodes and we started from 20 nodes up to a maximum of 140 nodes. The number of rounds taken into account were 300 and the number of transactions counted was up to 5000. Figure 10: Latency evaluation through number of rounds versus execution time. Figure 9: Performance analysis of the proposed system versus benchmark model. As presented in Figure 11, we carried out simulations based on two parameters including privacy parameter and test accuracy. We carried out the simulation results for two rounds and then we analyzed the test accuracy. We validated our simulations results through Figure 11, and we found that there is significantly less difference in the accuracy, showing the validation of our method for security and privacy. Figures 12 and 13 represent the simulations results based on the number of domains and the local epoch. The value epoch represents the training of the datasets in batch form.