Ride the Storm: Navigating Through Unstable Periods / Katerina Rudko (Belka G...
Ardışık Topluluk Öğrenmesine Dayalı Gürbüz Anomali Tespiti
1. Ardışık Topluluk Ögrenmesine Dayalı
Gürbüz Anomali Tespiti
SIU 2020
Selim Firat Yilmaz
Suleyman Serdar Kozat
5-7 Ekim 2020
Bilkent Üniversitesi Elektrik ve Elektronik Mühendisliği
2. Anomali Tespiti
• Beklenen davranışa uymayan desenlerin bulunmasını amaçlar.
• Anomali: Verinin beklenen dagılımına aykırı davranan
örneklerdir1
.
• Kullanım alanları:
• Siber atakların tespiti2
• Videolarda anormal aktivite tespiti3
• Kredi kartı dolandırıcılıklarının tespiti4
• Gözetimsiz anomali tespiti
1Varun Chandola, Arindam Banerjee, and Vipin Kumar. “Anomaly detection: A survey”. In: ACM computing surveys (CSUR) 41.3 (2009), p. 15.
2Ahmad Javaid et al. “A deep learning approach for network intrusion detection system”. In: Proceedings of the 9th EAI International
Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ICST (Institute for Computer Sciences,
Social-Informatics and …. 2016, pp. 21–26.
3Waqas Sultani, Chen Chen, and Mubarak Shah. “Real-world anomaly detection in surveillance videos”. In: Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition. 2018, pp. 6479–6488.
4John Akhilomen. “Data mining application for cyber credit-card fraud detection system”. In: Industrial Conference on Data Mining.
Springer. 2013, pp. 218–228.
1
3. Literatür
• İzolasyon Ormanı (İO) (Isolation Forest)5
• Hem anomali hem normal örnekler olduğunda da iyi.
• k En Yakın Komşu (k-EYK) (kth
NN)6
• Sadece normal örnekler olduğunda iyi.
d3 d1
d2
SKORLA( ) = max(d1, d2, d3)
Ayrıştırılmış
Anomali
Normal
Hedef
d0
5Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. “Isolation forest”. In: 2008 Eighth IEEE International Conference on Data Mining. IEEE. 2008,
pp. 413–422.
6Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim. “Efficient algorithms for mining outliers from large data sets”. In: ACM Sigmod
Record. Vol. 29. 2. ACM. 2000, pp. 427–438.
2
4. Önerilen Model
• İzolasyon Ormanı Gözetimli k En Yakın Komşu modeli
• Veri etiketlemeye ihtiyaç duymaz.
• Anomalilere karşı gürbüz (robust)
• Kaynak kod: https://github.com/selimfirat/siu-sead
3
8. Parametre ve Anomali Oranı Analizi
0.0 0.2 0.4 0.6 0.8 1.0
Parametresi
0.2
0.4
0.6
0.8
1.0
OrtalamaKesinlik
Veri Kümesi
musk
pendigits
satellite
satimage-2
Figure 1: Önerilen modelin λ
parametresine karşılık ortalama
kesinlik metriğinin değişimi
0.0 0.2 0.4 0.6 0.8 1.0
Anomali Oran
0.0
0.2
0.4
0.6
0.8
1.0
OrtalamaKesinlik
Model
IO
k-EYK
Önerilen Model
TS-DVM
Figure 2: Satellite verisinin eğitim
kümesindeki anomali oranına göre
modellerin ortalama kesinlik
skorlarının değişimi
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9. Yeni Derin Öğrenme Modeli
• Unsupervised Anomaly Detection via Deep Metric Learning with
End-to-End Optimization
• https://arxiv.org/abs/2005.05865
• selimfirat.github.io
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11. Referanslar i
References
John Akhilomen. “Data mining application for cyber credit-card
fraud detection system”. In: Industrial Conference on Data
Mining. Springer. 2013, pp. 218–228.
Varun Chandola, Arindam Banerjee, and Vipin Kumar. “Anomaly
detection: A survey”. In: ACM computing surveys (CSUR) 41.3
(2009), p. 15.
10
12. Referanslar ii
Ahmad Javaid et al. “A deep learning approach for network
intrusion detection system”. In: Proceedings of the 9th EAI
International Conference on Bio-inspired Information and
Communications Technologies (formerly BIONETICS). ICST
(Institute for Computer Sciences, Social-Informatics and … 2016,
pp. 21–26.
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. “Isolation forest”.
In: 2008 Eighth IEEE International Conference on Data Mining.
IEEE. 2008, pp. 413–422.
Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim.
“Efficient algorithms for mining outliers from large data sets”. In:
ACM Sigmod Record. Vol. 29. 2. ACM. 2000, pp. 427–438.
11
13. Referanslar iii
Waqas Sultani, Chen Chen, and Mubarak Shah. “Real-world
anomaly detection in surveillance videos”. In: Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition.
2018, pp. 6479–6488.
Xiaodan Xu, Huawen Liu, and Minghai Yao. “Recent Progress of
Anomaly Detection”. In: Complexity 2019 (2019), pp. 1–11. doi:
10.1155/2019/2686378.
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