Security Issues and Privacy Concerns in Industry 4.0 Applications. Группа авторов

no. of received packets (M.N:30%) [PDR: 20%].Figure 6.19 False alarm rate vs. no. of received packets (20%) [PDR: 30%].Figure 6.20 False alarm rate vs. no. of received packets (M.N:20%) [PDR: 20%].Figure 6.21 False alarm rate vs. no. of received packets (M.N:10%) [PDR:30%].Figure 6.22 False alarm rate vs. no. of received packets (M.N:10%) [PDR: 20%].

      7 Chapter 7Figure 7.1 Proposed architecture.Figure 7.2 Working model of the system.Figure 7.3 Drone in track.Figure 7.4 Track with garbage.Figure 7.5 Open CV with drone to scan.Figure 7.6 Working of rover in track.Figure 7.7 Collection of garbage.Figure 7.8 Spraying of sanitary lotion cleaned the railway track.

      8 Chapter 8Fig. 8.1 Blockchain technology.Fig. 8.2 Works timeline of blockchain technology.Fig. 8.3 Blockchain architecture’s basic components.Fig. 8.4 Blockchain architecture.Fig. 8.5 Blockchain key features.Fig. 8.6 Blockchain components.Fig. 8.7 How blockchain cryptography works.Fig. 8.8 Working of smart contracts.Fig. 8.9 Blockchain applications.Fig. 8.10 Blockchain financial and non-financial applications.Fig. 8.11 Benefits of using blockchain technology.Fig. 8.12 Limitations of implementing blockchain.Fig. 8.13 Industries that global executives believe are most advanced in blockch...Fig. 8.14 Blockchain adoption rate is gradually increasing.Fig. 8.15 Sectors currently using blockchain technology.Fig. 8.16 Barriers to large-scale adoption of blockchain.Fig. 8.17 Barriers for blockchain adoptions by 2020.

      9 Chapter 9Figure 9.1 Work flow of ML-based prediction.Figure 9.2 Framework architecture.Figure 9.3 Product and former details in web portal.Figure 9.4 Web interface of proposed system.Figure 9.5 Price prediction using linear regression.Figure 9.6 Price prediction using random forest.

      10 Chapter 10Fig. 10.1 resnet50 model architecture.Fig. 10.2 CNN model architecture.Fig. 10.3 Model accuracy vs. epoch.Fig. 10.4 Model loss vs. epoch.

      11 Chapter 11Figure 11.1 Architecture of MAS.Figure 11.2 Dynamic nature of agents in MAS.Figure 11.3 Use case diagram for case study.Figure 11.4 Sequence diagram for the case study.Figure 11.5 Deployment diagram for case study.Figure 11.6 Arrival rate of requests versus workload on Interface Agent.Figure 11.7 Arrival time of requests versus workload on Information Agent.Figure 11.8 Arrival rate of requests versus workload on Work Agent.Figure 11.9 Arrival time of requests versus Response time.Figure 11.10 Sensitivity analysis for Response Time.Figure 11.11 Sample screen shots for Proposed Algorithm.Figure 11.12 Sample screen shots for Random Selection Algorithm.Figure 11.13 Average response time comparison using normal distribution.Figure 11.14 Average waiting time comparison using normal distribution.Figure 11.15 Average utilization comparison using normal distribution.Figure 11.16 Average response time comparison using poisson distribution.Figure 11.17 Average waiting time comparison using poisson distribution.Figure 11.18 Average utilization comparison using poisson distribution.Figure 11.19 Average response time comparison using exponential distribution.Figure 11.20 Average waiting time comparison using exponential distribution.Figure 11.21 Average utilization time comparison using exponential distribution.

      List of Tables

      1 Chapter 1Table 1.1 Research on IoT-based SWMS.

      2 Chapter 2Table 2.1 Network forensics architecture conceptual block of the model.

      3 Chapter 3Table 3.1 List of open source datasets for Tamil language.Table 3.2 Performance of ASR systems using various extraction and classification...

      4 Chapter 4Table 4.1 Data analysis on correlation.Table 4.2 Serial test.Table 4.3 Avalanche effect: change in session key.Table 4.4 Comparison between proposed algorithm and standard algorithms.Table 4.5 Comparison between some existing algorithm and proposed algorithm.Table 4.6 Different IoT attacks.

      5 Chapter 5Table 5.1 Initial features from profiles.Table 5.2 Confusion matrix for fake profile detection testing data set.Table 5.3 Performance analysis of Random Forest, Optimized Naive Bayes and SVM.Table 5.4 Evaluation metrics (Precision, Recall and F-Score) of Random Forest, O...

      6 Chapter 6Table 6.1 Hardware and software components.

      7 Chapter 8Table 8.1 Shows the blockchain pros and cons [12].Table 8.2 A comparison between private, public and consortium blockchain.Table 8.3 Factors affecting the implementation of blockchain technology.Table 8.4 Sector-wide uses and application areas of blockchain technology.

      8 Chapter 11Table 11.1 Sample data from simulations using first-come first-serve method.Table 11.2 Sample data from simulations using the Proposed Algorithm.

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