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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

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

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

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