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Anomaly Detection In Network Traffic Using Machine Learning

Yıl 2023, Cilt: 2 Sayı: 3, 5 - 12, 31.12.2023

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.

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.
Yıl 2023, Cilt: 2 Sayı: 3, 5 - 12, 31.12.2023

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.
Toplam 11 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Kalitesi, Süreçler ve Metrikler, Yazılım Mimarisi, Yazılım Testi, Doğrulama ve Validasyon
Bölüm Research Articles
Yazarlar

Roaa Mohammed 0009-0001-2973-2470

Fatih Akay 0000-0003-0780-0679

Proje Numarası 750785
Erken Görünüm Tarihi 18 Aralık 2023
Yayımlanma Tarihi 31 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 2 Sayı: 3

Kaynak Göster

APA Mohammed, R., & Akay, F. (2023). Anomaly Detection In Network Traffic Using Machine Learning. Cukurova University Journal of Natural and Applied Sciences, 2(3), 5-12.
AMA Mohammed R, Akay F. Anomaly Detection In Network Traffic Using Machine Learning. Cukurova University Journal of Natural and Applied Sciences. Aralık 2023;2(3):5-12.
Chicago Mohammed, Roaa, ve Fatih Akay. “Anomaly Detection In Network Traffic Using Machine Learning”. Cukurova University Journal of Natural and Applied Sciences 2, sy. 3 (Aralık 2023): 5-12.
EndNote Mohammed R, Akay F (01 Aralık 2023) Anomaly Detection In Network Traffic Using Machine Learning. Cukurova University Journal of Natural and Applied Sciences 2 3 5–12.
IEEE R. Mohammed ve F. Akay, “Anomaly Detection In Network Traffic Using Machine Learning”, Cukurova University Journal of Natural and Applied Sciences, c. 2, sy. 3, ss. 5–12, 2023.
ISNAD Mohammed, Roaa - Akay, Fatih. “Anomaly Detection In Network Traffic Using Machine Learning”. Cukurova University Journal of Natural and Applied Sciences 2/3 (Aralık 2023), 5-12.
JAMA Mohammed R, Akay F. Anomaly Detection In Network Traffic Using Machine Learning. Cukurova University Journal of Natural and Applied Sciences. 2023;2:5–12.
MLA Mohammed, Roaa ve Fatih Akay. “Anomaly Detection In Network Traffic Using Machine Learning”. Cukurova University Journal of Natural and Applied Sciences, c. 2, sy. 3, 2023, ss. 5-12.
Vancouver Mohammed R, Akay F. Anomaly Detection In Network Traffic Using Machine Learning. Cukurova University Journal of Natural and Applied Sciences. 2023;2(3):5-12.