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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. detection of hydrogen isotopes
32. distributed detection
33. early detection
34. early intrusion detection
35. energy detection
36. error detection
37. ESI-MS and IM detection
38. Event detection
39. extraction and detection methods
40. face detection
41. Face Manipulation Detection
42. failure detection
43. fall detection
44. fault detection
45. fault detection and diagnoses
46. fault detection and diagnosis
47. Fish detection
48. fouling detection
49. hepatitis C virus (HCV) detection
50. hubs detection
51. IED detection
52. indicident detection and response
53. indirect UV absorbance detection
54. intrusion detection
55. intrusion detection system (IDS)
56. intrusion detection systems
57. island detection
58. islanding detection
59. label-free detection
60. LED fluorescence detection
61. leg detection
62. light detection and ranging
63. Light Detection and Ranging (LiDAR) point cloud
64. limit of detection
65. lock-in detection
66. loop-closure detection
67. malware detection
68. marker detection
69. mobile malware detection
70. money laundering detection systems
71. monitoring and detection
72. multi-classed object detection
73. mutant detection
74. network anomaly detection
75. nodules detection
76. object detection
77. oil-spill detection
78. outlier’s detection methods
79. outliers detection
80. peak detection
81. plagiarism detection
82. position detection
83. protein detection
84. proton detection
85. radar detection
86. ramp event detection
87. recycled FPGA detection
88. robust detection
89. scene text detection
90. short-circuit detection
91. small molecule detection
92. speech activity detection
93. Suicidal ideation detection
94. synchronous detection
95. tyre detection
96. unmanned aerial vehicle detection systems
97. unsupervised outlier detection
98. UV detection
99. vehicle detection
100. vortex detection
101. 3D lane detection

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