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journal article EST / journal article ENG

On the application of active learning for efficient and effective Iot botnet detectionGuerra Manzanares, Alejandro; Bahsi, HayretdinFuture Generation Computer Systems2023 / p. 40-53 : ill https://doi.org/10.1016/j.future.2022.10.024 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

journal article EST / journal article ENG
Seotud publikatsioonid 1
- Machine learning-based detection and characterization of evolving threats in mobile and IoT Systems = Masinõppepõhine arenevate ohtude tuvastamine ning kirjeldamine mobiilseadmete ja värkvõrkude jaoks
2

book article

Towards the integration of a post-hoc interpretation step into the machine learning workflow for IoT botnet detectionGuerra Manzanares, Alejandro; Nõmm, Sven; Bahsi, Hayretdin18th IEEE International Conference on Machine Learning and Applications : ICMLA 2019, 16–19 December 2019, Boca Raton, Florida, USA : proceedings2019 / p. 1162-1169 : ill https://doi.org/10.1109/ICMLA.2019.00193

book article
Seotud publikatsioonid 1
- Machine learning-based detection and characterization of evolving threats in mobile and IoT Systems = Masinõppepõhine arenevate ohtude tuvastamine ning kirjeldamine mobiilseadmete ja värkvõrkude jaoks

Number of records 2, displaying 1 - 2

1. botnet detection
2. botnet
3. botnet attack
4. iot botnet
5. amoxicillin detection
6. anomaly detection
7. anomaly detection for system logs
8. antibiotic detection
9. antibiotics detection
10. aspect detection
11. asynchronous fault detection
12. attack detection
13. barotrauma detection system
14. body detection
15. capacitively coupled conductivity detection
16. change detection
17. chemical detection
18. community detection
19. compu ter-aided detection(CAD)
20. computer-aided detection
21. computer‐aided detection
22. contactless conductivity detection
23. contactless conductivity detection (C4D)
24. crash detection
25. cyber intrusion detection
26. defect detection
27. detection
28. detection efficiency
29. detection engineering
30. detection of anomalous end user nodes
31. distributed detection
32. early detection
33. early intrusion detection
34. energy detection
35. error detection
36. ESI-MS and IM detection
37. Event detection
38. extraction and detection methods
39. face detection
40. Face Manipulation Detection
41. failure detection
42. fall detection
43. fault detection
44. fault detection and diagnoses
45. fault detection and diagnosis
46. Fish detection
47. fouling detection
48. hepatitis C virus (HCV) detection
49. hubs detection
50. IED detection
51. indicident detection and response
52. indirect UV absorbance detection
53. intrusion detection
54. intrusion detection system (IDS)
55. intrusion detection systems
56. island detection
57. islanding detection
58. label-free detection
59. LED fluorescence detection
60. leg detection
61. light detection and ranging
62. Light Detection and Ranging (LiDAR) point cloud
63. limit of detection
64. lock-in detection
65. loop-closure detection
66. malware detection
67. marker detection
68. mobile malware detection
69. money laundering detection systems
70. monitoring and detection
71. multi-classed object detection
72. mutant detection
73. network anomaly detection
74. nodules detection
75. object detection
76. oil-spill detection
77. outlier’s detection methods
78. outliers detection
79. plagiarism detection
80. position detection
81. protein detection
82. proton detection
83. radar detection
84. ramp event detection
85. recycled FPGA detection
86. robust detection
87. scene text detection
88. short-circuit detection
89. small molecule detection
90. speech activity detection
91. Suicidal ideation detection
92. synchronous detection
93. tyre detection
94. unmanned aerial vehicle detection systems
95. unsupervised outlier detection
96. UV detection
97. vehicle detection
98. vortex detection
99. 3D lane detection

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