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Number of records 9
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book article

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

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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

book article
3

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
4

journal article

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

journal article
5

book article EST / book article 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

book article EST / book article ENG
6

book article

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

book article
7

book article EST / book article 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

book article EST / book article ENG
8

book article

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

book article
9

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 9, displaying 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 method
42. detection methods
43. detection of anomalous end user nodes
44. detection of hydrogen isotopes
45. distributed detection
46. early detection
47. early intrusion detection
48. energy detection
49. error detection
50. ESI-MS and IM detection
51. Event detection
52. extraction and detection methods
53. face detection
54. Face Manipulation Detection
55. failure detection
56. fall detection
57. fault detection
58. fault detection and diagnoses
59. fault detection and diagnosis
60. Fish detection
61. fouling detection
62. harmonic detection
63. hepatitis C virus (HCV) detection
64. hubs detection
65. IED detection
66. image-based crack detection
67. indicident detection and response
68. indirect UV absorbance detection
69. intrusion detection
70. intrusion detection system
71. intrusion detection system (IDS)
72. intrusion detection systems
73. island detection
74. islanding detection
75. label-free detection
76. LED fluorescence detection
77. leg detection
78. light detection and ranging
79. light detection and ranging (LiDAR)
80. Light Detection and Ranging (LiDAR) point cloud
81. limit of detection
82. lock-in detection
83. loop-closure detection
84. malware detection
85. marker detection
86. mobile malware detection
87. money laundering detection systems
88. monitoring and detection
89. multi-classed object detection
90. Multi-valued Simulation for Hazard Detection in Digital Circuits
91. mutant detection
92. network intrusion detection system
93. nodules detection
94. object detection
95. oil-spill detection
96. outlier’s detection methods
97. outliers detection
98. peak detection
99. phytoplankton detection methods
100. plagiarism detection
101. position detection
102. protein detection
103. proton detection
104. radar detection
105. ramp event detection
106. recycled FPGA detection
107. residual current detection
108. robust detection
109. scene text detection
110. short-circuit detection
111. small molecule detection
112. speech activity detection
113. Suicidal ideation detection
114. synchronous detection
115. tyre detection
116. unmanned aerial vehicle detection systems
117. unsupervised outlier detection
118. UV detection
119. vehicle detection
120. vortex detection
121. 3D lane detection

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