point of view.Figure 1.5 Flowchart of inbox.Figure 1.6 Storage bucket from manager’s point of view.Figure 1.7 Flowchart of storage bucket.Figure 1.8 Manage employees page from manager point of view.Figure 1.9 Flowchart of employee database.Figure 1.10 Announcement page from manager point of view.Figure 1.11 Flowchart of creating announcement.Figure 1.12 Group access control modifier by manager.Figure 1.13 Before compression of data (raw data).Figure 1.14 After compression of data (compressed data).2 Chapter 3Figure 3.1 Linux.Figure 3.2 Linux.Figure 3.3 GUI hashing tool for windows: hash tool.Figure 3.4 Linux terminal.Figure 3.5 PE-bear.Figure 3.6 Snipped image of the result produced by dumpbin (Windows).Figure 3.7 Dependencies (Windows only).Figure 3.8 Virtualbox network port configuration.Figure 3.9 Guest addition.Figure 3.10 VBoxService.exe file.Figure 3.11 Process Monitor (Windows only).Figure 3.12 Process explorer (Windows).Figure 3.13 AccessEnum (Windows).Figure 3.14 Wireshark display structure (Windows/Linux).Figure 3.15 Flowchart.Figure 3.16 PEiD tool (Windows only).Figure 3.17 Portable executable dissected.Figure 3.18 Ghidra (Windows and Linux).Figure 3.19 WinDbg (Windows only).
3 Chapter 4Figure 4.1 Block diagram of block-chain secured online shopping.Figure 4.2 Traditional supply chain mechanism.Figure 4.3 Supply chain using blockchain technology.
4 Chapter 5Figure 5.1 Identity management system architecture.Figure 5.2 Blockchain architecture.Figure 5.3 Merkle tree [Source: Cointral.com].
5 Chapter 6Figure 6.1 An example of extraction of image feature in convolution neural netwo...Figure 6.2 Training model with MINST database.Figure 6.3 (a) Fully connected RNN and (b) simple, partially connected RNN.Figure 6.4 Abstraction and the detailed process of recurrent neural network.Figure 6.5 Process of RNN forward propagation.Figure 6.6 Basic LSTM structure for RNN.Figure 6.7 Back propagation in neural networks.Figure 6.8 Basic architecture for generative steganography using neural networks...Figure 6.9 Illustration of gradient descent optimization technique in neural net...Figure 6.10 Differential process of (a) stochastic gradient descent and (b) grad...Figure 6.11 Convergence techniques in SGD with momentum.
6 Chapter 7Figure 7.1 Minimization of privacy loss using barrier function.Figure 7.2 Illustrative mechanism for smart health management system.Figure 7.3 Portion of cancer patients’ dataset used in our study.Figure 7.4 Comparative performance between most promising privacy preservation m...
7 Chapter 8Figure 8.1 Various compression techniques.Figure 8.2 PSNR vs. threshold for image at decomposition level = 16.Figure 8.3 PSNR vs. threshold for image at decomposition level = 4.
8 Chapter 9Figure 9.1 Cellular infrastructure revenue forecast, Worldwide, 2018–2021 (Milli...Figure 9.2 Key enabling techniques for 5G.Figure 9.3 Components of 5G green network (5G-GN).Figure 9.4 Overview of 5G security architecture (source: 5G America White paper ...
9 Chapter 10Figure 10.1 Virtual architecture of the proposed architecture.Figure 10.2 Entire framework price in INR taking in upholding for first year bet...
10 Chapter 11Figure 11.1 Big data processing.Figure 11.2 10 V’s of big data.Figure 11.3 Big data technology process.Figure 11.4 Architecture of big data.Figure 11.5 Big data access control.Figure 11.6 Classifications of big data.Figure 11.7 Performance factors.Figure 11.8 Big data security and privacy areas.Figure 11.9 Big data security and privacy issue.Figure 11.10 Big data security threats.Figure 11.11 Process flow of the proposed work.Figure 11.12 Comparative analysis of time taken.Figure 11.13 Comparative analysis of error rate.Figure 11.14 Comparative analysis of packet size.Figure 11.15 Comparative analysis of security mechanism.
Guide
1 Cover
6 Preface
10 Index
Pages
1 vii
2 ii
3 iii
4 iv
5 v
6 xv
7 xvi
8 xvii
9 xviii
10 xix
11 xx
