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anomaly detection (võtmesõna)

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Kirjeid leitud 9
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ekspordi kõik päringu tulemused (9)
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artikkel kogumikus

AIS data analysis: reality in the Sea of EchosVisky, Gabor; Rohl, Alexander; Katsikas, Sokratis; Maennel, Olaf Manuel2024 IEEE 49th Conference on Local Computer Networks (LCN)2024 / 7 p https://doi.org/10.1109/LCN60385.2024.10639765

artikkel kogumikus
2

artikkel kogumikus

Anomalous File System Activity detection through Temporal Association rule miningReza, M.; Iman, H.; Chikul, Pavel; Jervan, Gert; Bahsi, Hayretdin; Ghasempouri, TaraProceedings of the 9th International Conference on Information Systems Security and Privacy ICISSP. Vol. 12023 / p. 733-740 https://doi.org//10.5220/0000168400003405

artikkel kogumikus
3

artikkel kogumikus

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

artikkel kogumikus
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
4

artikkel ajakirjas

Novel real-time in-step gait anomaly detection algorithmsRostovski, Jakob; Krivošei, Andrei; Kuusik, Alar; Le Moullec, Yannick; Alam, Muhammad Mahtabtechrxiv.org2024 https://doi.org/10.36227/techrxiv.171173236.66440239/v1

artikkel ajakirjas
5

artikkel kogumikus EST / artikkel kogumikus ENG

Real-time gait anomaly detection using 1D-CNN and LSTMRostovski, Jakob; Ahmadilivani, Mohammad Hasan; Krivošei, Andrei; Kuusik, Alar; Alam, Muhammad MahtabDigital Health and Wireless Solutions : First Nordic Conference, NCDHWS 2024, Oulu, Finland, May 7–8, 2024 : Proceedings, Part II2024 / p. 260-278 https://doi.org/10.1007/978-3-031-59091-7_17 Conference proceedings at Scopus Article at Scopus Article at WOS

artikkel kogumikus EST / artikkel kogumikus ENG
6

artikkel kogumikus

Real-time gait anomaly detection using SVM time series classificationRostovski, Jakob; Krivošei, Andrei; Kuusik, Alar; Alam, Muhammad Mahtab; Ahmadov, Ulvi2023 International Wireless Communications and Mobile Computing (IWCMC)2023 / p. 1389-1394 https://doi.org/10.1109/IWCMC58020.2023.10182666

artikkel kogumikus
7

artikkel kogumikus EST / artikkel kogumikus ENG

Signature-based approach to detecting malicious outgoing trafficPetliak, Nataliia; Klots, Yurii; Titova, Vira; Cheshun, Viktor; Boyarchuk, ArtemIntelITSIS 2023: Intelligent Information Technologies & Systems of Information Security 2023 : proceedings of the 4th International Workshop on Intelligent Information Technologies & Systems of Information Security : Khmelnytskyi, Ukraine, March 22-24, 20232023 / p. 486 - 506 https://ceur-ws.org/Vol-3373/paper33.pdf Conference Proceedings at Scopus Article at Scopus

artikkel kogumikus EST / artikkel kogumikus ENG
8

artikkel kogumikus

Unsupervised anomaly based botnet detection in IoT networksNõmm, Sven; Bahsi, Hayretdin17th IEEE International Conference on Machine Learning and Applications : ICMLA 2018, 17–20 December 2018, Orlando, Florida, USA : proceedings2018 / p. 1048-1053 https://doi.org/10.1109/ICMLA.2018.00171

artikkel kogumikus
9

artikkel kogumikus EST / artikkel kogumikus 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

artikkel kogumikus EST / artikkel kogumikus 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

Kirjeid leitud 9, kuvan 1 - 9

1. anomaly detection
2. anomaly detection for system logs
3. network anomaly detection
4. cerium anomaly europium anomaly
5. anomaly
6. Bouguer anomaly
7. Ce anomaly
8. cerium anomaly
9. europium anomaly
10. gravity anomaly
11. residual gravity anomaly
12. Residual Terrain Model (RTM) anomaly
13. residual terrain model anomaly
14. wind anomaly
15. amoxicillin detection
16. antibiotic detection
17. antibiotics detection
18. aspect detection
19. asynchronous fault detection
20. attack detection
21. barotrauma detection system
22. behaviour-based intrusion detection systems
23. body detection
24. botnet detection
25. capacitively coupled conductivity detection
26. change detection
27. chemical detection
28. community detection
29. compu ter-aided detection(CAD)
30. computer-aided detection
31. computer‐aided detection
32. contactless conductivity detection
33. contactless conductivity detection (C4D)
34. crack detection
35. crash detection
36. cyber intrusion detection
37. defect detection
38. detection
39. detection efficiency
40. detection engineering
41. detection methods
42. detection of anomalous end user nodes
43. detection of hydrogen isotopes
44. distributed detection
45. early detection
46. early intrusion detection
47. energy detection
48. error detection
49. ESI-MS and IM detection
50. Event detection
51. extraction and detection methods
52. face detection
53. Face Manipulation Detection
54. failure detection
55. fall detection
56. fault detection
57. fault detection and diagnoses
58. fault detection and diagnosis
59. Fish detection
60. fouling detection
61. harmonic detection
62. hepatitis C virus (HCV) detection
63. hubs detection
64. IED detection
65. image-based crack detection
66. indicident detection and response
67. indirect UV absorbance detection
68. intrusion detection
69. intrusion detection system
70. intrusion detection system (IDS)
71. intrusion detection systems
72. island detection
73. islanding detection
74. label-free detection
75. LED fluorescence detection
76. leg detection
77. light detection and ranging
78. light detection and ranging (LiDAR)
79. Light Detection and Ranging (LiDAR) point cloud
80. limit of detection
81. lock-in detection
82. loop-closure detection
83. malware detection
84. marker detection
85. mobile malware detection
86. money laundering detection systems
87. monitoring and detection
88. multi-classed object detection
89. mutant detection
90. network intrusion detection system
91. nodules detection
92. object detection
93. oil-spill detection
94. outlier’s detection methods
95. outliers detection
96. peak detection
97. phytoplankton detection methods
98. plagiarism detection
99. position detection
100. protein detection
101. proton detection
102. radar detection
103. ramp event detection
104. recycled FPGA detection
105. residual current detection
106. robust detection
107. scene text detection
108. short-circuit detection
109. small molecule detection
110. speech activity detection
111. Suicidal ideation detection
112. synchronous detection
113. tyre detection
114. unmanned aerial vehicle detection systems
115. unsupervised outlier detection
116. UV detection
117. vehicle detection
118. vortex detection
119. 3D lane detection

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