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Deep learning-based detection of cyberattacks in software-defined networksMirsadeghi, Seyed Mohammad Hadi; Bahsi, Hayretdin; Inbouli, WissemDigital forensics and cyber crime : 13th EAI international conference, ICDF2C 2022, Boston, MA, November 16-18, 2022 : proceedings2023 / p. 341-354 https://doi.org/10.1007/978-3-031-36574-4_20

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Dimensionality reduction for machine learning based IoT botnet detectionBahsi, Hayretdin; Nõmm, Sven15th International Conference on Control, Automation, Robotics and Vision (ICARCV 2018) : Singapore, November 18-21, 20182018 / p. 1857-1862 : ill https://doi.org/10.1109/ICARCV.2018.8581205

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journal article EST / journal article ENG

In-depth feature selection for the statistical machine learning-based botnet detection in IoT networksKalakoti, Rajesh; Nõmm, Sven; Bahsi, HayretdinIEEE Access2022 / p. 94518-94535 https://doi.org/10.1109/ACCESS.2022.3204001 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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4

book article

MedBIoT : generation of an IoT Botnet Dataset in a medium-sized IoT networkGuerra Manzanares, Alejandro; Medina-Galindo, Jorge; Bahsi, Hayretdin; Nõmm, SvenProceedings of the 6th International Conference on Information Systems Security and Privacy, ICISSP : February 25-27, 2020, La Valletta, Malta. Vol. 12020 / p. 207-218 : ill https://doi.org/10.5220/0009187802070218

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
5

book article EST / book article ENG

Using MedBIoT dataset to build effective machine learning-based IoT botnet detection systemsGuerra Manzanares, Alejandro; Medina-Galindo, Jorge; Bahsi, Hayretdin; Nõmm, SvenInformation Systems Security and Privacy : 6th International Conference, ICISSP 2020, Valletta, Malta, February 25–27, 2020 : revised selected papers2022 / p. 222–243 https://doi.org/10.1007/978-3-030-94900-6_11 Conference proceedings at Scopus Article at Scopus Article at WOS

book article EST / book 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

Number of records 5, displaying 1 - 5

1. cyber intrusion detection
2. early intrusion detection
3. intrusion detection
4. intrusion detection system (IDS)
5. intrusion detection systems
6. air intrusion
7. intrusion
8. saline intrusion blockage
9. amoxicillin detection
10. anomaly detection
11. anomaly detection for system logs
12. antibiotic detection
13. antibiotics detection
14. aspect detection
15. asynchronous fault detection
16. attack detection
17. barotrauma detection system
18. body detection
19. botnet detection
20. capacitively coupled conductivity detection
21. change detection
22. chemical detection
23. community detection
24. compu ter-aided detection(CAD)
25. computer-aided detection
26. computer‐aided detection
27. contactless conductivity detection
28. contactless conductivity detection (C4D)
29. crash detection
30. defect detection
31. detection
32. detection efficiency
33. detection engineering
34. detection of anomalous end user nodes
35. detection of hydrogen isotopes
36. distributed detection
37. early detection
38. energy detection
39. error detection
40. ESI-MS and IM detection
41. Event detection
42. extraction and detection methods
43. face detection
44. Face Manipulation Detection
45. failure detection
46. fall detection
47. fault detection
48. fault detection and diagnoses
49. fault detection and diagnosis
50. Fish detection
51. fouling detection
52. hepatitis C virus (HCV) detection
53. hubs detection
54. IED detection
55. indicident detection and response
56. indirect UV absorbance detection
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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