Research Article
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Anomaly Detection In Network Traffic Using Machine Learning

Year 2023, Volume: 2 Issue: 3, 5 - 12, 31.12.2023

Abstract

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.

Supporting Institution

FEN BİLİMLERİ ENSTİTÜSÜ, CU

Project Number

750785

Thanks

I would like to thank my supervisor, Prof. Dr. Mehmet Fatih AKAY, Also, like to thank all my professors at Çukurova University-Adana.

References

  • [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.
Year 2023, Volume: 2 Issue: 3, 5 - 12, 31.12.2023

Abstract

Project Number

750785

References

  • [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.
There are 11 citations in total.

Details

Primary Language English
Subjects Software Quality, Processes and Metrics, Software Architecture, Software Testing, Verification and Validation
Journal Section Research Articles
Authors

Roaa Mohammed 0009-0001-2973-2470

Fatih Akay 0000-0003-0780-0679

Project Number 750785
Early Pub Date December 18, 2023
Publication Date December 31, 2023
Published in Issue Year 2023 Volume: 2 Issue: 3

Cite

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. December 2023;2(3):5-12.
Chicago Mohammed, Roaa, and Fatih Akay. “Anomaly Detection In Network Traffic Using Machine Learning”. Cukurova University Journal of Natural and Applied Sciences 2, no. 3 (December 2023): 5-12.
EndNote Mohammed R, Akay F (December 1, 2023) Anomaly Detection In Network Traffic Using Machine Learning. Cukurova University Journal of Natural and Applied Sciences 2 3 5–12.
IEEE R. Mohammed and F. Akay, “Anomaly Detection In Network Traffic Using Machine Learning”, Cukurova University Journal of Natural and Applied Sciences, vol. 2, no. 3, pp. 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 (December 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 and Fatih Akay. “Anomaly Detection In Network Traffic Using Machine Learning”. Cukurova University Journal of Natural and Applied Sciences, vol. 2, no. 3, 2023, pp. 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.