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Journals / Chaos Theory and Applications / 2023 / Cilt: 5 Sayı: 3

Anomaly Detection in Cyber Security with Graph-Based LSTM in Log Analysis

Yusuf ALACA
Yuksel CELIK
Sanjay GOEL
Pages
188–197
Publication Date
2023-11-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results on the selected datasets demonstrate high levels of success.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Intrusion detection systems utilize the analysis of log data to effectively detect anomalies.
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Key Findings & Data:Initially, the complex and diverse log data is transformed into graph data using Node2Vec, enabling efficient and rapid analysis on the artificial intelligence model.
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Conclusion & Impact:Experimental results on the selected datasets demonstrate high levels of success.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Anomaly Detection · Graph · Node2Vec

Abstract

Intrusion detection systems utilize the analysis of log data to effectively detect anomalies. However, detecting anomalies quickly and effectively in large and heterogeneous log data can be challenging. To address this difficulty, this study proposes the GLSTM (Graph-based Long Short-Term Memory) framework, a graph-based deep learning model that analyzes log data to detect cyber-attacks rapidly and effectively. The framework involves standardizing the complex and diverse log data, training this data on an artificial intelligence model, and detecting anomalies. Initially, the complex and diverse log data is transformed into graph data using Node2Vec, enabling efficient and rapid analysis on the artificial intelligence model. Subsequently, these graph data are trained using LSTM (Long Short-Term Memory), Bi-LSTM, and GRU(Gated Recurrent Unit) deep learning algorithms. The proposed framework is tested using Hadoop’s HDFS dataset, collected from different systems and heterogeneous sources, as well as the BGL and IMDB datasets. Experimental results on the selected datasets demonstrate high levels of success.

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Cite This Article

ALACA, Y., CELIK, Y., GOEL, S. (2023). Anomaly Detection in Cyber Security with Graph-Based LSTM in Log Analysis. Chaos Theory and Applications, Cilt: 5 Sayı: 3, 188–197. https://ajindex.com/articles/1019993

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References (11)

  1. Ahmed, M., A. N. Mahmood, and M. R. Islam, 2016 A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems 55: 278–288.
  2. Alaca, Y. and Y. Çelik, 2023 Cyber attack detection with qr code images using lightweight deep learning models. Computers & Security 126: 103065. Church, K. W., 2017 Word2Vec. Natural Language Engineering 23: 155–162. CSIRO’s Data61, 2018 StellarGraph Machine Learning Library. Demeester, T., T. Rocktäschel, and S. Riedel, 2016 Lifted rule injection for relation embeddings. EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings pp. 1389–1399.
  3. Du, M., F. Li, G. Zheng, and V. Srikumar, 2017 DeepLog: Anomaly detection and diagnosis from system logs through deep learning. Proceedings of the ACM Conference on Computer and Communications Security pp. 1285–1298.
  4. Elbasani, E. and J. D. Kim, 2021 LLAD: Life-Log Anomaly Detection Based on Recurrent Neural Network LSTM. Journal of Healthcare Engineering 2021.
  5. Farzad, A. and T. A. Gulliver, 2019 Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU pp. 1–28.
  6. Gogoi, P., D. K. Bhattacharyya, B. Borah, and J. K. Kalita, 2011 A survey of outlier detection methods in network anomaly identification. The Computer Journal 54: 570–588.
  7. Grover, A. and J. Leskovec, 2016 node2vec: Scalable Feature Learning for Networks. Guo, H., S. Yuan, and X.Wu, 2021 Logbert: Log anomaly detection via bert. In 2021 international joint conference on neural networks (IJCNN), pp. 1–8,
  8. IEEE. He, S., P. He, Z. Chen, T. Yang, Y. Su, et al., 2020 A Survey on Automated Log Analysis for Reliability Engineering. arXiv preprint arXiv:2009.07237. Hochreiter, S. and J. Schmidhuber, 1997 Long Short-Term Memory. Neural Computation 9: 1735–1780.
  9. Kipf, T. N. and M. Welling, 2016 SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS. Technical report. Li, H. and Y. Li, 2020 LogSpy: System Log Anomaly Detection for Distributed Systems. Proceedings - 2020 International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2020 pp. 347–352.
  10. Li, Y., Y. Zheng, H. Zhang, and L. Chen, 2015 Traffic prediction in a bike-sharing system. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10.
  11. Lu, S., X. Wei, Y. Li, and L. Wang, 2018 Detecting anomaly in big data system logs using convolutional neural network. Proceedings - IEEE 16th International Conference on Dependa