Öz
The primary theme of this paper revolves around the detection of anomalies and measurement of device health, using a Key Performance Indicator (KPI) dataset spanning twenty-one days. The study aims to improve the accuracy of models through the utilization of machine learning (ML) methods. The accuracy of each model was measured using a confusion matrix, and the results indicate that deep learning methods outperform classification methods across all models. Overall, this study provides valuable insights into the use of ML methods for improving the accuracy of anomaly detection and device health measurement in KPI datasets, with potential applications in various fields.
Anahtar Kelimeler
Anomaly detection Machine learning (ML) Key Performance Indicator (KPI)
Destekleyen Kurum
FEN BİLİMLERİ ENSTİTÜSÜ, CU
Proje Numarası
750785
Teşekkür
I would like to thank my supervisor, Prof. Dr. Mehmet Fatih AKAY, Also, like to thank all my professors at Çukurova University-Adana.
Kaynakça
- [1]Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla, (2019). '' ANOMALY DETECTION USING ONE-CLASS NEURAL NETWORKS .
- [2] Evelyn Fix, Joseph Hodges L., (2020). " Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties ".USAF School of Aviation Medicine, Randolph Field, Texas. Archived (PDF) from the original on 26, September.
- [3] Sara A. Althubiti , Eric Marcell Jones Jr, Kaushik Roy, (2018)." LSTM for Anomaly-Based Network Intrusion Detection ".978-1-5386-7177-1/18, IEEE
- [4] Felix A. Gers, Jurgen Schmidhuber, Fred Cummins, (2000)." Learning to Forget: Continual Prediction with LSTM". Neu-ral Computation 12, 2451–2471 °c 2000 Massachusetts Institute of Technology.
- [5] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, (2017)." ImageNet Classification with Deep Convolutional Neural Networks", communıcatıons of the acm, june, vol. 60, no. 6.
- [6] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, et. al., (2015)."ImageNet Large Scale Visual Recognition Challenge ", Int J Comput Vis 115:211–252 DOI 10.1007/s11263-015-0816-y,
- [7] Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, Fuad E. Alsaadi, (2017). "A survey of deep neural netwo Felix A. Gers rk architectures and their applications",Neurocomputing 234, 11–26.
- [8] Taejoon Kim, Sang C. Suh, Hyunjoo Kim, Jonghyun Kim, Jinoh Kim, (2018). " An Encoding Technique for CNN-based Network Anomaly Detection", 978-1-5386-5035-6/18 ©IEEE.
- [9] Ralf C. Staudemeyer, (2015). “Applying long short-term memory recurrent neural networks to intrusion detection”, SACJ No. 56.
- [10] M. A. Ambusaidi, X. He, P. Nanda, and Z. Tan, (2016). “Building an intrusion detection system using a filter-based feature selection algorithm,” IEEE Transactions on Computers, vol. 65, no. 10, pp. 2986–2998.
- [11] Annie Gilda Roselin et.al., (2021). ''Intelligent Anomaly Detection for Large Network Traffic With Optimized Deep Clus-tering (ODC) Algorithm'', 10.1109/ACCESS.2021.3068172. [12] Guanglu Wei et.al., (2021). ''Adoption and realization of deep learning in network traffic anomaly detection device design''. Soft computing 25:1147–1158.
Öz
Proje Numarası
750785
Kaynakça
- [1]Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla, (2019). '' ANOMALY DETECTION USING ONE-CLASS NEURAL NETWORKS .
- [2] Evelyn Fix, Joseph Hodges L., (2020). " Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties ".USAF School of Aviation Medicine, Randolph Field, Texas. Archived (PDF) from the original on 26, September.
- [3] Sara A. Althubiti , Eric Marcell Jones Jr, Kaushik Roy, (2018)." LSTM for Anomaly-Based Network Intrusion Detection ".978-1-5386-7177-1/18, IEEE
- [4] Felix A. Gers, Jurgen Schmidhuber, Fred Cummins, (2000)." Learning to Forget: Continual Prediction with LSTM". Neu-ral Computation 12, 2451–2471 °c 2000 Massachusetts Institute of Technology.
- [5] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, (2017)." ImageNet Classification with Deep Convolutional Neural Networks", communıcatıons of the acm, june, vol. 60, no. 6.
- [6] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, et. al., (2015)."ImageNet Large Scale Visual Recognition Challenge ", Int J Comput Vis 115:211–252 DOI 10.1007/s11263-015-0816-y,
- [7] Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, Fuad E. Alsaadi, (2017). "A survey of deep neural netwo Felix A. Gers rk architectures and their applications",Neurocomputing 234, 11–26.
- [8] Taejoon Kim, Sang C. Suh, Hyunjoo Kim, Jonghyun Kim, Jinoh Kim, (2018). " An Encoding Technique for CNN-based Network Anomaly Detection", 978-1-5386-5035-6/18 ©IEEE.
- [9] Ralf C. Staudemeyer, (2015). “Applying long short-term memory recurrent neural networks to intrusion detection”, SACJ No. 56.
- [10] M. A. Ambusaidi, X. He, P. Nanda, and Z. Tan, (2016). “Building an intrusion detection system using a filter-based feature selection algorithm,” IEEE Transactions on Computers, vol. 65, no. 10, pp. 2986–2998.
- [11] Annie Gilda Roselin et.al., (2021). ''Intelligent Anomaly Detection for Large Network Traffic With Optimized Deep Clus-tering (ODC) Algorithm'', 10.1109/ACCESS.2021.3068172. [12] Guanglu Wei et.al., (2021). ''Adoption and realization of deep learning in network traffic anomaly detection device design''. Soft computing 25:1147–1158.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Yazılım Kalitesi, Süreçler ve Metrikler, Yazılım Mimarisi, Yazılım Testi, Doğrulama ve Validasyon |
| Bölüm | Research Articles |
| Yazarlar | |
| Proje Numarası | 750785 |
| Erken Görünüm Tarihi | 18 Aralık 2023 |
| Yayımlanma Tarihi | 31 Aralık 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 2 Sayı: 3 |