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anomaly detection (keyword)
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1
book article
MedBIoT : generation of an IoT Botnet Dataset in a medium-sized IoT network
Guerra Manzanares, Alejandro
;
Medina-Galindo, Jorge
;
Bahsi, Hayretdin
;
Nõmm, Sven
Proceedings of the 6th International Conference on Information Systems Security and Privacy, ICISSP : February 25-27, 2020, La Valletta, Malta. Vol. 1
2020
/
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
2
journal article
Novel real-time in-step gait anomaly detection algorithms
Rostovski, Jakob
;
Krivošei, Andrei
;
Kuusik, Alar
;
Le Moullec, Yannick
;
Alam, Muhammad Mahtab
techrxiv.org
2024
https://doi.org/10.36227/techrxiv.171173236.66440239/v1
journal article
3
book article
Real-time gait anomaly detection using 1D-CNN and LSTM
Rostovski, Jakob
;
Ahmadilivani, Mohammad Hasan
;
Krivošei, Andrei
;
Kuusik, Alar
;
Alam, Muhammad Mahtab
Digital Health and Wireless Solutions : First Nordic Conference, NCDHWS 2024, Oulu, Finland, May 7–8, 2024 : Proceedings, Part II
2024
/
p. 260-278
https://doi.org/10.1007/978-3-031-59091-7_17
book article
4
book article
Real-time gait anomaly detection using SVM time series classification
Rostovski, Jakob
;
Krivošei, Andrei
;
Kuusik, Alar
;
Alam, Muhammad Mahtab
;
Ahmadov, Ulvi
2023 International Wireless Communications and Mobile Computing (IWCMC)
2023
/
p. 1389-1394
https://doi.org/10.1109/IWCMC58020.2023.10182666
book article
5
book article
Unsupervised anomaly based botnet detection in IoT networks
Nõmm, Sven
;
Bahsi, Hayretdin
17th IEEE International Conference on Machine Learning and Applications : ICMLA 2018, 17–20 December 2018, Orlando, Florida, USA : proceedings
2018
/
p. 1048-1053
https://doi.org/10.1109/ICMLA.2018.00171
book article
6
book article EST
/
book article ENG
Using MedBIoT dataset to build effective machine learning-based IoT botnet detection systems
Guerra Manzanares, Alejandro
;
Medina-Galindo, Jorge
;
Bahsi, Hayretdin
;
Nõmm, Sven
Information Systems Security and Privacy : 6th International Conference, ICISSP 2020, Valletta, Malta, February 25–27, 2020 : revised selected papers
2022
/
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 6, displaying
1 - 6
keyword
102
1.
anomaly detection
2.
anomaly detection for system logs
3.
network anomaly detection
4.
anomaly
5.
Bouguer anomaly
6.
cerium anomaly
7.
europium anomaly
8.
gravity anomaly
9.
residual gravity anomaly
10.
Residual Terrain Model (RTM) anomaly
11.
residual terrain model anomaly
12.
wind anomaly
13.
amoxicillin detection
14.
antibiotic detection
15.
antibiotics 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.
crash detection
31.
cyber intrusion detection
32.
defect detection
33.
detection
34.
detection efficiency
35.
detection engineering
36.
detection of anomalous end user nodes
37.
distributed detection
38.
early detection
39.
early intrusion detection
40.
energy detection
41.
error detection
42.
ESI-MS and IM detection
43.
Event detection
44.
extraction and detection methods
45.
face detection
46.
Face Manipulation Detection
47.
failure detection
48.
fall detection
49.
fault detection
50.
fault detection and diagnoses
51.
fault detection and diagnosis
52.
Fish detection
53.
fouling detection
54.
hepatitis C virus (HCV) detection
55.
hubs detection
56.
IED detection
57.
indicident detection and response
58.
indirect UV absorbance detection
59.
intrusion detection
60.
intrusion detection systems
61.
island detection
62.
islanding detection
63.
label-free detection
64.
LED fluorescence detection
65.
leg detection
66.
light detection and ranging
67.
Light Detection and Ranging (LiDAR) point cloud
68.
limit of detection
69.
lock-in detection
70.
loop-closure detection
71.
malware detection
72.
marker detection
73.
mobile malware detection
74.
money laundering detection systems
75.
monitoring and detection
76.
multi-classed object detection
77.
mutant detection
78.
nodules detection
79.
object detection
80.
oil-spill detection
81.
outlier’s detection methods
82.
outliers detection
83.
plagiarism detection
84.
position detection
85.
protein detection
86.
proton detection
87.
radar detection
88.
ramp event detection
89.
recycled FPGA detection
90.
robust detection
91.
short-circuit detection
92.
small molecule detection
93.
speech activity detection
94.
Suicidal ideation detection
95.
synchronous detection
96.
tyre detection
97.
unmanned aerial vehicle detection systems
98.
unsupervised outlier detection
99.
UV detection
100.
vehicle detection
101.
vortex detection
102.
3D lane detection
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