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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. crack detection
33. crash detection
34. cyber intrusion detection
35. defect detection
36. detection
37. detection efficiency
38. detection engineering
39. detection methods
40. detection of anomalous end user nodes
41. detection of hydrogen isotopes
42. distributed detection
43. early detection
44. early intrusion detection
45. energy detection
46. error detection
47. ESI-MS and IM detection
48. Event detection
49. extraction and detection methods
50. face detection
51. Face Manipulation Detection
52. failure detection
53. fall detection
54. fault detection
55. fault detection and diagnoses
56. fault detection and diagnosis
57. Fish detection
58. fouling detection
59. harmonic detection
60. hepatitis C virus (HCV) detection
61. hubs detection
62. IED detection
63. image-based crack detection
64. indicident detection and response
65. indirect UV absorbance detection
66. intrusion detection
67. intrusion detection system
68. intrusion detection system (IDS)
69. intrusion detection systems
70. island detection
71. islanding detection
72. label-free detection
73. LED fluorescence detection
74. leg detection
75. light detection and ranging
76. Light Detection and Ranging (LiDAR) point cloud
77. limit of detection
78. lock-in detection
79. loop-closure detection
80. malware detection
81. marker detection
82. mobile malware detection
83. money laundering detection systems
84. monitoring and detection
85. multi-classed object detection
86. mutant detection
87. nodules detection
88. object detection
89. oil-spill detection
90. outlier’s detection methods
91. outliers detection
92. peak detection
93. plagiarism detection
94. position detection
95. protein detection
96. proton detection
97. radar detection
98. ramp event detection
99. recycled FPGA detection
100. residual current detection
101. robust detection
102. scene text detection
103. short-circuit detection
104. small molecule detection
105. speech activity detection
106. Suicidal ideation detection
107. synchronous detection
108. tyre detection
109. unmanned aerial vehicle detection systems
110. unsupervised outlier detection
111. UV detection
112. vehicle detection
113. vortex detection
114. 3D lane detection

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