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Sunucuların Anomali Durumlarinin Yapay Zeka Metotları ile Tahmin Edilmesi

Year 2023, Volume: Vol:8 Issue: Issue:2, 57 - 65, 20.12.2023
https://doi.org/10.53070/bbd.1223978

Abstract

Sunucularda anormallik tespiti belirli bir metot ve uygulama yoluyla aykırı değerlerin diğerlerinden ayrıştırılarak analiz edilmesiyle oluşan ve genelde olağan dışı durumları tespit etmekte kullanılır. Anormalliklerin erkenden tespit edilmesi ön görülebilirlik kararlar vermeyi ve gerekirse savunma mekanizması geliştirilmesinde kullanılabilir. Önemli bir problem olarak bilinen Anormallik Tespiti birçok tarama ve uygulama sahasında araştırılmaktadır. Genelde araştırmacılar bu bahsi geçen probleme yapay zeka, makine öğrenimi ve durum makine modellemesi gibi teknikleri kullanarak çözüm arayışına girmişlerdir. Sunucuların anormallik testleri ve analizi yapılabilir ve bu yöntem-teknikler kullanılarak çıkarımlar yapılabilir. Sunuculardan alınan CPU, Network, Disk, Memory değerleri anomali testinde kullanılmak üzere veri analiz aşamalarından geçer ve teknikler uygulanarak modellemesi yapılır. Bu çalışmada toplanan veri kümesi kullanılarak YSA, Karar Ağacı, Rastgele Orman, K- En Yakın Komşu ve Ekstra Karar Ağacı algoritmalarının anormali tespit performansları test edilmiştir. Yapılan testlerde anormal durumlarının belirlenmesinde % 99.94 oranıyla YSA’nın başarılı olduğu görülmüştür. Önerilen yöntem, toplanan veri ve önerilen yöntemin diğer yöntemler ile karşılaştırmalı analizleri çalışma içerisinde sunulmuştur.

References

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  • Jiang, F., Leung, C. K., & Pazdor, A. G. M. (2016). Big data mining of social networks for friend recommendation. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. https://doi.org/10.1109/ASONAM.2016.7752349
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Prediction of Anomaly Conditions of Servers with Artificial Intelligence Methods

Year 2023, Volume: Vol:8 Issue: Issue:2, 57 - 65, 20.12.2023
https://doi.org/10.53070/bbd.1223978

Abstract

The objective of this paper is to investigate the efficacy of various techniques in detecting anomalies in server systems. Anomaly detection is a critical problem that involves identifying unusual situations by analyzing outliers and making predictions based on these observations. The study focuses on using artificial intelligence, machine learning, and state machine modeling to solve this problem. The data collected from the servers, including CPU, network, disk, and memory values, is analyzed and used for anomaly testing and modeling. The performance of five algorithms, including Artificial Neural Network (ANN), Decision Tree, Random Forest, K-Nearest Neighbor, and Extra Decision Tree, is evaluated using the collected data set. The results show that the ANN algorithm achieved a success rate of 99.94% in detecting abnormal conditions. The study presents a comprehensive analysis of the proposed method, the collected data, and a comparison of the proposed method with other methods. The findings of this study contribute to the ongoing efforts to improve the accuracy and efficiency of anomaly detection in server systems.

References

  • Agarwala, S., Alegre, F., Schwan, K., & Mehalingham, J. (2007). E2EProf: Automated end-to-end performance management for enterprise systems. Proceedings of the International Conference on Dependable Systems and Networks. https://doi.org/10.1109/DSN.2007.38
  • Agarwala, S., & Schwan, K. (2006). SysProf: Online distributed behavior diagnosis through fine-grain system monitoring. Proceedings - International Conference on Distributed Computing Systems, 2006. https://doi.org/10.1109/ICDCS.2006.81
  • Aggarwal, C. C. (2013). Mining sensor data streams. In Managing and Mining Sensor Data (Vol. 9781461463092). https://doi.org/10.1007/978-1-4614-6309-2_6
  • Aguilera, M. K., Mogul, J. C., Wiener, J. L., Reynolds, P., & Muthitacharoen, A. (2003). Performance debugging for distributed systems of black boxes. Operating Systems Review (ACM), 37(5). https://doi.org/10.1145/1165389.945454
  • Bahl, P., Chandra, R., Greenberg, A., Kandula, S., Maltz, D. A., & Zhang, M. (2007). Towards highly reliable enterprise network services via inference of multi-level dependencies. Computer Communication Review, 37(4). https://doi.org/10.1145/1282427.1282383
  • Dastjerdi, A. V., Bakar, K. A., & Hassan Tabatabaei, S. G. (2009). Distributed intrusion detection in clouds using mobile agents. 3rd International Conference on Advanced Engineering Computing and Applications in Sciences, ADVCOMP 2009. https://doi.org/10.1109/ADVCOMP.2009.34
  • Garfinkel, T., & Rosenblum, M. (2003). A Virtual Machine Introspection Based Architecture for Intrusion Detection. Proc. Network and Distributed Systems Security …, 1.
  • Guan, Q., Fu, S., de Bardeleben, N., & Blanchard, S. (2013). Exploring time and frequency domains for accurate and automated anomaly detection in cloud computing systems. Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC. https://doi.org/10.1109/PRDC.2013.40
  • Guan, Y., & Bao, J. (2009). A CP Intrusion Detection Strategy on Cloud Computing. 2009 International Symposium on Web Information Systems and Applications, Proceedings, 8.
  • Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques. https://doi.org/10.1016/C2009-0-61819-5
  • Huang, L., Nguyen, X. L., Garofalakis, M., Jordan, M. I., Joseph, A., & Taft, N. (2007). In-network PCA and anomaly detection. Advances in Neural Information Processing Systems. https://doi.org/10.7551/mitpress/7503.003.0082
  • Jiang, F., Leung, C. K., & Pazdor, A. G. M. (2016). Big data mining of social networks for friend recommendation. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. https://doi.org/10.1109/ASONAM.2016.7752349
  • Kang, H., Zhu, X., & Wong, J. L. (2012). DAPA: Diagnosing application performance anomalies for virtualized infrastructures. 2nd USENIX Workshop on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services, Hot-ICE 2012.
  • Kiciman, E., & Fox, A. (2005). Detecting application-level failures in component-based Internet services. IEEE Transactions on Neural Networks, 16(5). https://doi.org/10.1109/TNN.2005.853411
  • Lee, J. H., Park, M. W., Eom, J. H., & Chung, T. M. (2011). Multi-level intrusion detection system and log management in cloud computing. International Conference on Advanced Communication Technology, ICACT.
  • Lewis, S. (2015). Qualitative Inquiry and Research Design: Choosing Among Five Approaches. In Health Promotion Practice (Vol. 16, Issue 4). https://doi.org/10.1177/1524839915580941
  • MacQueen, J. B. (1967). Kmeans Some Methods for classification and Analysis of Multivariate Observations. 5th Berkeley Symposium on Mathematical Statistics and Probability 1967, 1(233), 281–297. https://doi.org/citeulike-article-id:6083430
  • Massie, M. L., Chun, B. N., & Culler, D. E. (2004). The ganglia distributed monitoring system: Design, implementation, and experience. Parallel Computing, 30(7). https://doi.org/10.1016/j.parco.2004.04.001
  • Muniyandi, A. P., Rajeswari, R., & Rajaram, R. (2012). Network anomaly detection by cascading k-Means clustering and C4.5 decision tree algorithm. Procedia Engineering, 30. https://doi.org/10.1016/j.proeng.2012.01.849
  • Sigelman, B. H., Andr, L., Burrows, M., Stephenson, P., Plakal, M., Beaver, D., Jaspan, S., & Shanbhag, C. (2010). Dapper , a Large-Scale Distributed Systems Tracing Infrastructure. Google Research, April.
  • Tan, Y., Nguyen, H., Shen, Z., Gu, X., Venkatramani, C., & Rajan, D. (2012). PREPARE: Predictive performance anomaly prevention for virtualized cloud systems. Proceedings - International Conference on Distributed Computing Systems. https://doi.org/10.1109/ICDCS.2012.65
  • Team, K. (n.d.). Keras Developer Guides. Retrieved August 24, 2022, from https://keras.io/guides/ Thudumu, S., Branch, P., Jin, J., & Singh, J. (Jack). (2020). Adaptive Clustering for Outlier Identification in High-Dimensional Data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11945 LNCS. https://doi.org/10.1007/978-3-030-38961-1_19 UCI Machine Learning Repository. (2015). KDD Cup 1999 Data. In 1999]. Http://Kdd. Ics. Uci. Edu/Databases/Kddcup99/Kddcup99. Html.
  • University of Waikato. (2016). Weka 3 - Data Mining with Open Source Machine Learning Software in Java. In The University of Waikato.
  • Wang, C., Viswanathan, K., Choudur, L., Talwar, V., Satterfield, W., & Schwan, K. (2011). Statistical techniques for online anomaly detection in data centers. Proceedings of the 12th IFIP/IEEE International Symposium on Integrated Network Management, IM 2011. https://doi.org/10.1109/INM.2011.5990537
  • Yalagandula, P., & Dahlin, M. (2004). A Scalable Distributed Information Management System ∗ Categories and Subject Descriptors. Conference on Applications, Technologies, Architectures and Protocols for Computer Communications.
  • Zhai, Y., Ong, Y. S., & Tsang, I. W. (2014). The emerging ?Big dimensionality? IEEE Computational Intelligence Magazine, 9(3). https://doi.org/10.1109/MCI.2014.2326099
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Artificial Intelligence
Journal Section PAPERS
Authors

Mehmet Fatih Savran This is me 0000-0002-2020-5789

Ahmet Anıl Müngen 0000-0002-5691-6507

Publication Date December 20, 2023
Submission Date December 24, 2022
Acceptance Date February 9, 2023
Published in Issue Year 2023 Volume: Vol:8 Issue: Issue:2

Cite

APA Savran, M. F., & Müngen, A. A. (2023). Sunucuların Anomali Durumlarinin Yapay Zeka Metotları ile Tahmin Edilmesi. Computer Science, Vol:8(Issue:2), 57-65. https://doi.org/10.53070/bbd.1223978

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