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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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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
5. intrusion detection system (IDS)
6. intrusion detection systems
7. air intrusion
8. intrusion
9. saline intrusion blockage
10. amoxicillin detection
11. anomaly detection
12. anomaly detection for system logs
13. antibiotic detection
14. antibiotics detection
15. aspect detection
16. asynchronous fault detection
17. attack detection
18. barotrauma detection system
19. body detection
20. botnet detection
21. capacitively coupled conductivity detection
22. change detection
23. chemical detection
24. community detection
25. compu ter-aided detection(CAD)
26. computer-aided detection
27. computer‐aided detection
28. contactless conductivity detection
29. contactless conductivity detection (C4D)
30. crack detection
31. crash detection
32. defect detection
33. detection
34. detection efficiency
35. detection engineering
36. detection methods
37. detection of anomalous end user nodes
38. detection of hydrogen isotopes
39. distributed detection
40. early detection
41. energy detection
42. error detection
43. ESI-MS and IM detection
44. Event detection
45. extraction and detection methods
46. face detection
47. Face Manipulation Detection
48. failure detection
49. fall detection
50. fault detection
51. fault detection and diagnoses
52. fault detection and diagnosis
53. Fish detection
54. fouling detection
55. harmonic detection
56. hepatitis C virus (HCV) detection
57. hubs detection
58. IED detection
59. image-based crack detection
60. indicident detection and response
61. indirect UV absorbance detection
62. island detection
63. islanding detection
64. label-free detection
65. LED fluorescence detection
66. leg detection
67. light detection and ranging
68. Light Detection and Ranging (LiDAR) point cloud
69. limit of detection
70. lock-in detection
71. loop-closure detection
72. malware detection
73. marker detection
74. mobile malware detection
75. money laundering detection systems
76. monitoring and detection
77. multi-classed object detection
78. mutant detection
79. network anomaly detection
80. nodules detection
81. object detection
82. oil-spill detection
83. outlier’s detection methods
84. outliers detection
85. peak detection
86. plagiarism detection
87. position detection
88. protein detection
89. proton detection
90. radar detection
91. ramp event detection
92. recycled FPGA detection
93. residual current detection
94. robust detection
95. scene text detection
96. short-circuit detection
97. small molecule detection
98. speech activity detection
99. Suicidal ideation detection
100. synchronous detection
101. tyre detection
102. unmanned aerial vehicle detection systems
103. unsupervised outlier detection
104. UV detection
105. vehicle detection
106. vortex detection
107. 3D lane detection

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