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Year 2023, Volume: 7 Issue: 2, 90 - 96, 19.12.2023

Gulsum Akkuzukaya Mehmet Yıldız

Abstract

References

  • [1] Damien, A., Fumey, M., Alata, E., Kaâniche, M., & Nicomette, V. (2018, November). Anomaly based intrusion detection for an avionic embedded system. In Aerospace Systems and Technology Conference (ASTC-2018).
  • [2] Biesecker, C. (2017). Boeing 757 testing shows airplanes vulnerable to hacking, DHS says. Avionics International, Nov.
  • [3] Schellekens, M. (2016). Car hacking: Navigating the regulatory landscape. Computer law & security review, 32(2), 307-315.
  • [4] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.
  • [5] Esmaeili, F., Cassie, E., Nguyen, H. P. T., Plank, N. O., Unsworth, C. P., & Wang, A. (2023). Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks. Bioengineering, 10(4), 405
  • [6] Ezeme, M., Azim, A., & Mahmoud, Q. H. (2017, December). An imputation-based augmented anomaly detection from large traces of operating system events. In Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (pp. 43-52).
  • [7] Lippmann, R. P., Fried, D. J., Graf, I., Haines, J. W., Kendall, K. R., McClung, D., ... & Zissman, M. A. (2000, January). Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation. In Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00 (Vol. 2, pp. 12-26). IEEE.
  • [8] Boukerche, A., Zheng, L., & Alfandi, O. (2020). Outlier detection: Methods, models, and classification. ACM Computing Surveys (CSUR), 53(3), 1-37.
  • [9] Creech, G., & Hu, J. (2013). A semantic approach to host-based intrusion detection systems using contiguousand discontiguous system call patterns. IEEE Transactions on Computers, 63(4), 807-819.
  • [10] Meena, G., & Choudhary, R. R. (2017, July). A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA. In 2017 International Conference on Computer, Communications and Electronics (Comptelix) (pp. 553-558). IEEE.
  • [11] Hafeez, I., Antikainen, M., Ding, A. Y., & Tarkoma, S. (2020). IoT-KEEPER: Detecting malicious IoT network activity using online traffic analysis at the edge. IEEE Transactions on Network and Service Management, 17(1), 45-59.
  • [12] Ring IV, J. H., Van Oort, C. M., Durst, S., White, V., Near, J. P., & Skalka, C. (2021). Methods for host-based intrusion detection with deep learning. Digital Threats: Research and Practice (DTRAP), 2(4), 1-29.
  • [13] Ezeme, O. M., Mahmoud, Q. H., Azim, A., & Michael, L. (2019). SysCall dataset: A dataset for context modeling and anomaly detection using system calls.
  • [14] Ezeme, O. M., Lescisin, M., Mahmoud, Q. H., & Azim, A. (2019). Deepanom: An ensemble deep framework for anomaly detection in system processes. In Advances in Artificial Intelligence: 32nd Canadian Conference on Artificial Intelligence, Canadian AI 2019, Kingston, ON, Canada, May 28–31, 2019, Proceedings 32 (pp. 549-555). Springer International Publishing.
  • [15] Duan, G., Fu, Y., Cai, M., Chen, H., & Sun, J. (2023). DongTing: A large-scale dataset for anomaly detection of the Linux kernel. Journal of Systems and Software, 111745.
  • [16] Mvula, P. K., Branco, P., Jourdan, G. V., & Viktor, H. L. (2023). Evaluating Word Embedding Feature Extraction Techniques for Host-Based Intrusion Detection Systems. Discover Data, 1(1), 2.
  • [17] Terbuch, A., O’Leary, P., Khalili-Motlagh-Kasmaei, N., Auer, P., Zöhrer, A., & Winter, V. (2023). Detecting Anomalous Multivariate Time-Series via Hybrid Machine Learning. IEEE Transactions on Instrumentation and Measurement.
  • [18] Kim, J., Kang, H., & Kang, P. (2023). Time-series anomaly detection with stacked Transformer representations and 1D convolutional network. Engineering Applications of Artificial Intelligence, 120, 105964.
  • [19] Ma, Y., Xie, Z., Chen, S., Qiao, F., & Li, Z. (2023). Real-time detection of abnormal driving behavior based on long short-term memory network and regression residuals. Transportation research part C: emerging technologies, 146, 103983
  • [20] Aggarwal, S. (2023). LSTM based Anomaly Detection in Time Series for United States exports and imports.
  • [21] Ezeme, Okwudili; Mahmoud, Qusay; Azim, Akramul; Lescisin, Michael (2019), “SysCall Dataset: A Dataset for Context Modeling and Anomaly Detection using System Calls”, Mendeley Data, V2, doi: 10.17632/vfvw7g8s8h.2

Time Series Anomaly Detection Embedded Systems By Using LSTM

Year 2023, Volume: 7 Issue: 2, 90 - 96, 19.12.2023

Gulsum Akkuzukaya Mehmet Yıldız

Abstract

İnsansız Hava Araçları (İHA) için anomali tespiti önemli bir araştırma alanı olmuştur. Anormallikleri tespit etme tekniklerinden biri, geleneksel Makine Öğrenimi (ML) algoritmalarını uygulamaktır, ancak geleneksel ML yaklaşımları, özellikle uzun vadeli bağımlı noktalardaki anormallikleri tespit edemez. Bu çalışma, İHA sistem çağrılarının zaman serisindeki anormallikleri tespit etmek için Uzun Kısa Süreli Bellek (LSTM) yöntemini kullanır. Bunu yapmak için, LSTM ağı, bir İHA sistemindeki olayların zaman aralıklarındaki verilerin uzun vadeli bağımlılıklarını öğrenmek için birbiriyle çalışan birden fazla LSTM hücresinden oluşur. Bu makalede kullanılan veri seti, sistem çağrılarının sırasını ve türünü, sistem çağrısı olaylarının zaman damgalarını, işlem kimliklerini ve isteğe bağlı argümanları içeren bir İHA'dan sistem çağrısı olaylarından toplanmıştır. LSTM tekniği ile derinlemesine modern bir siber tehdit analizi sağlamayı amaçladığımız için veri seti bu çalışmanın amacına uygun bir veri setidir. Deneysel sonuçlar, LSTM tekniğinin sistem çağrılarının zaman serisindeki anormallikleri tespit etmedeki üstün performansını kanıtlamıştır.

Keywords

LSTM Anomaly Detectıon UAV System Calls Deep Learning

References

  • [1] Damien, A., Fumey, M., Alata, E., Kaâniche, M., & Nicomette, V. (2018, November). Anomaly based intrusion detection for an avionic embedded system. In Aerospace Systems and Technology Conference (ASTC-2018).
  • [2] Biesecker, C. (2017). Boeing 757 testing shows airplanes vulnerable to hacking, DHS says. Avionics International, Nov.
  • [3] Schellekens, M. (2016). Car hacking: Navigating the regulatory landscape. Computer law & security review, 32(2), 307-315.
  • [4] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.
  • [5] Esmaeili, F., Cassie, E., Nguyen, H. P. T., Plank, N. O., Unsworth, C. P., & Wang, A. (2023). Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks. Bioengineering, 10(4), 405
  • [6] Ezeme, M., Azim, A., & Mahmoud, Q. H. (2017, December). An imputation-based augmented anomaly detection from large traces of operating system events. In Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (pp. 43-52).
  • [7] Lippmann, R. P., Fried, D. J., Graf, I., Haines, J. W., Kendall, K. R., McClung, D., ... & Zissman, M. A. (2000, January). Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation. In Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00 (Vol. 2, pp. 12-26). IEEE.
  • [8] Boukerche, A., Zheng, L., & Alfandi, O. (2020). Outlier detection: Methods, models, and classification. ACM Computing Surveys (CSUR), 53(3), 1-37.
  • [9] Creech, G., & Hu, J. (2013). A semantic approach to host-based intrusion detection systems using contiguousand discontiguous system call patterns. IEEE Transactions on Computers, 63(4), 807-819.
  • [10] Meena, G., & Choudhary, R. R. (2017, July). A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA. In 2017 International Conference on Computer, Communications and Electronics (Comptelix) (pp. 553-558). IEEE.
  • [11] Hafeez, I., Antikainen, M., Ding, A. Y., & Tarkoma, S. (2020). IoT-KEEPER: Detecting malicious IoT network activity using online traffic analysis at the edge. IEEE Transactions on Network and Service Management, 17(1), 45-59.
  • [12] Ring IV, J. H., Van Oort, C. M., Durst, S., White, V., Near, J. P., & Skalka, C. (2021). Methods for host-based intrusion detection with deep learning. Digital Threats: Research and Practice (DTRAP), 2(4), 1-29.
  • [13] Ezeme, O. M., Mahmoud, Q. H., Azim, A., & Michael, L. (2019). SysCall dataset: A dataset for context modeling and anomaly detection using system calls.
  • [14] Ezeme, O. M., Lescisin, M., Mahmoud, Q. H., & Azim, A. (2019). Deepanom: An ensemble deep framework for anomaly detection in system processes. In Advances in Artificial Intelligence: 32nd Canadian Conference on Artificial Intelligence, Canadian AI 2019, Kingston, ON, Canada, May 28–31, 2019, Proceedings 32 (pp. 549-555). Springer International Publishing.
  • [15] Duan, G., Fu, Y., Cai, M., Chen, H., & Sun, J. (2023). DongTing: A large-scale dataset for anomaly detection of the Linux kernel. Journal of Systems and Software, 111745.
  • [16] Mvula, P. K., Branco, P., Jourdan, G. V., & Viktor, H. L. (2023). Evaluating Word Embedding Feature Extraction Techniques for Host-Based Intrusion Detection Systems. Discover Data, 1(1), 2.
  • [17] Terbuch, A., O’Leary, P., Khalili-Motlagh-Kasmaei, N., Auer, P., Zöhrer, A., & Winter, V. (2023). Detecting Anomalous Multivariate Time-Series via Hybrid Machine Learning. IEEE Transactions on Instrumentation and Measurement.
  • [18] Kim, J., Kang, H., & Kang, P. (2023). Time-series anomaly detection with stacked Transformer representations and 1D convolutional network. Engineering Applications of Artificial Intelligence, 120, 105964.
  • [19] Ma, Y., Xie, Z., Chen, S., Qiao, F., & Li, Z. (2023). Real-time detection of abnormal driving behavior based on long short-term memory network and regression residuals. Transportation research part C: emerging technologies, 146, 103983
  • [20] Aggarwal, S. (2023). LSTM based Anomaly Detection in Time Series for United States exports and imports.
  • [21] Ezeme, Okwudili; Mahmoud, Qusay; Azim, Akramul; Lescisin, Michael (2019), “SysCall Dataset: A Dataset for Context Modeling and Anomaly Detection using System Calls”, Mendeley Data, V2, doi: 10.17632/vfvw7g8s8h.2
There are 21 citations in total.

Details

Primary Language Turkish
Subjects Deep Learning, Machine Learning (Other), System and Network Security
Journal Section Articles
Authors

Gulsum Akkuzukaya 0000-0003-1806-7759

Mehmet Yıldız This is me

Early Pub Date December 19, 2023
Publication Date December 19, 2023
Submission Date November 21, 2023
Acceptance Date December 19, 2023
Published in Issue Year 2023 Volume: 7 Issue: 2

Cite

IEEE G. Akkuzukaya and M. Yıldız, “Time Series Anomaly Detection Embedded Systems By Using LSTM”, IJMSIT, vol. 7, no. 2, pp. 90–96, 2023.
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