Security Issues and Privacy Concerns in Industry 4.0 Applications

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.

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

2 Chapter 2 Table 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.

1 v

2 ii

3 iii

4 iv

5 xiii

6 1

7 2

8 3

9 4

10 5

11 6

12 7

13 8

14 9

15 10

16 11

17 12

18 13

19 14

20 15

21 16

22 17

23 18

24 19

25 20

26 21

27 22

28 23

29 24

30 25

31 26

32 27

33 28

34 29

35 30

36 31

37 32

38 33

39 35

40 36

41 37

42 38

43 39

44 40

45 41

46 42

47 43

48 44

49 45

50 46

51 47

52 48

53 49

54 50

55 51

56 52

57 53

58 55

59 56

60 57

61 58

62 59

63 60

64 61

65 62

66 63

67 64

68 65

69 66

70 67

71 68

72 69

73 70

74 71

75 72

76 73

77 74

78 75

79 76

80 77

81 78

82 79

83 80

84 81

85 82

86 83

87 84

88 85

89 86

90 87

91 88

92 89

93 90

94 91

95 92

96 93

97 94

98 95

99 96

100 97

101 98

102 99

103 100

104 101

105 102

106 103

107 104

108 105

109 106

110 107

111 108

112 109

113 110

114 111

115 112

116 113

117 114

118 115

119 116

120 117

121 118

122 119

123 120

124 121

125 122

126 123

127 124

128 125

129 126

130 127

131 128

132 129

133 130

134 131

135 132

136 133

137 134

138 135

139 136

140 137

141 138

142 139

143 140

144 141

145 142

146 143

147 144

148 145

149 146

150 147

151 148

152 149

153 150

154 151

155 152

156 153

157 154

158 155

159 156

160 157

161 158

162 159

163 160

164 161

165 162

166 163

167 164

168 165

169 166

170 167

171 168

172 169

173 170

174 171

175 172

176 173

177 174

178 175

179 176

180 177

181 178

182 179

183 180

184 181

185 182

186 183

187 184

188 185

189 186

190 187

191 188

192 189

193 190

194 191

195 192

196 193

197 194

198 195

199 196

200 197

201 198

202 199

203 200

204 201

205 202

206 203

207 204

208 205

209 207

210 208

211 209

212 210

213 211

214 212

215 213

216 214

217 215

218 216

219 217

220 218

221 219

222 220

223 221

224 222

225 223

226 224

227 225

228 226

229 227

230 229

231 230

232 231

233 232

234 233

235 234

236 235

237 236

238 237

239 238

240 239

241 240

242 241

243 242

244 243

245 244

246 245

247 246

248 247

249 249

250 250

251 251

252 252

253 253

254 254

Scrivener Publishing100 Cummings Center, Suite 541J Beverly, MA 01915-6106

Advances in Data Engineering and Machine Learning

Series Editor: M. Niranjanamurthy, PhD, Juanying XIE, PhD, and Ramiz Aliguliyev, PhD

Scope: Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise.

It is important to have business goals in line when working with data, especially for companies that handle large and complex datasets and databases. Data Engineering Contains DevOps, Data Science, and Machine Learning Engineering. DevOps (development and operations) is an enterprise software development phrase used to mean a type of agile relationship between development and IT operations. The goal of DevOps is to change and improve the relationship by advocating better communication and collaboration between these two business units. Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.

Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction. Machine learning engineering is the process of using software engineering principles, and analytical and data science knowledge, and combining both of those in order to take an ML model that’s created and making it available for use by the product or the consumers. “Advances in Data Engineering and Machine Learning Engineering” will reach a wide audience including data scientists, engineers, industry, researchers and students working in the field of Data Engineering and Machine Learning Engineering.