A Novel Deep Learning Architecture for Cyber Threat Detection in Smart Industrial Big Data Environment

Computer Science

1. Intrusion Detection 100%
2. Deep Learning Method 100%
3. Big Data Environment 100%
4. Big Data 100%
5. Threat Detection 100%
6. Internet-Of-Things 77%
7. Machine Learning 72%
8. Network Traffic 66%
9. Deep Learning Model 55%
10. Cyber Attack 55%
11. Intrusion Detection System 50%
12. Cybersecurity 44%
13. Industry 4.0 33%
14. Malicious Activity 27%
15. Smart Environment 27%
16. Research Project 27%
17. Industrial Application 27%
18. Botnets 22%
19. Smart City 22%
20. Leaning Parameter 16%
21. Security Solution 16%
22. Malware 16%
23. Essential Feature 16%
24. Support Vector Machine 16%
25. Security Architecture 16%
26. Industrial Environment 16%
27. Internet of Medical Things 16%
28. Learning System 11%
29. Traffic Characteristic 11%
30. Data Preprocessing 11%
31. Detection Accuracy 11%
32. Testbed 11%
33. Detection Rate 11%
34. Sybil Attack 11%
35. Map-Reduce 11%
36. Clustering Algorithm 11%
37. Time Complexity 11%
38. classification result 11%
39. Feature Selection 11%
40. Training Process 11%
41. Computer Network 11%
42. Grey Wolf Optimization 11%
43. Data Partitioning 11%
44. Feature Extraction 11%
45. Evaluation Models 11%
46. Sampling Data 11%
47. Trust Management 11%
48. Smart Device 11%
49. Research Direction 11%
50. Data Partition 5%
51. Data Processing 5%
52. Denial-of-Service Attack 5%
53. Statistical Approach 5%
54. Evaluation Measure 5%
55. Data Preparation 5%
56. Network Forensics 5%
57. Information Study 5%
58. Hierarchical Clustering 5%
59. Class Imbalance 5%
60. Preprocessing Phase 5%
61. Benchmarking 5%
62. Simulation Environment 5%
63. Fair Comparison 5%
64. Experimental Result 5%
65. Quality of Service 5%
66. Learning Process 5%
67. Management Tool 5%
68. Virtual Machine 5%
69. Parameter Value 5%
70. Final Decision 5%
71. Machine Learning Algorithm 5%
72. Discriminative Power 5%
73. Learning Technique 5%
74. Learning Algorithm 5%
75. Web of Science 5%
76. Interface System 5%
77. Industrial Internet of Things 5%
78. Training Dataset 5%
79. Human Intervention 5%
80. Data Transformation 5%
81. Model Complexity 5%
82. Threat Intelligence 5%
83. Successful Completion 5%
84. Process Optimization 5%
85. Ensemble Learning 5%
86. Membership Function 5%
87. Cybersecurity Framework 5%
88. Evaluation Result 5%
89. Stratified Random 5%
90. Behavioral Feature 5%
91. Clustered Data 5%
92. Anomaly Detection 5%
93. Detection Process 5%
94. Sensor Network 5%
95. Optimization Problem 5%
96. Security Vulnerability 5%
97. Attack Prevention 5%
98. Future Generation 5%
99. Fuzzy Mathematics 5%
100. System Implementation 5%
101. Attack Surface 5%
102. Learning Network 5%
103. Network Estimation 5%
104. Data Mining 5%
105. Deep Autoencoder 5%
106. Long Short-Term Memory Network 5%
107. Random Decision Forest 5%
108. Objective Optimization 5%
109. Cybercriminals 5%
110. Extreme Gradient Boosting 5%
111. Massive Amount 5%
112. Autoencoder 5%
113. Hybrid Approach 5%
114. Generalization Ability 5%
115. Online Transaction 5%
116. Computer System 5%
117. Parallel Processing 5%
118. Normal Traffic 5%
119. Class Imbalance Problem 5%
120. Ensemble Approach 5%
121. Use Case 5%
122. Variational Autoencoder 5%
123. Legitimate Traffic 5%
124. Data Analytics 5%
125. Deep Neural Network 5%
126. Computational Complexity 5%
127. Malicious Attack 5%
128. United States of America 5%
129. Literature Review 5%
130. Machine Learning Model 5%