Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1382 2024-02-28 10:12:04 |
2 layout Meta information modification 1382 2024-02-29 02:15:28 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Psychogyios, K.; Papadakis, A.; Bourou, S.; Nikolaou, N.; Maniatis, A.; Zahariadis, T. Deep Learning for IDSs in Time Series Data. Encyclopedia. Available online: https://encyclopedia.pub/entry/55638 (accessed on 21 April 2024).
Psychogyios K, Papadakis A, Bourou S, Nikolaou N, Maniatis A, Zahariadis T. Deep Learning for IDSs in Time Series Data. Encyclopedia. Available at: https://encyclopedia.pub/entry/55638. Accessed April 21, 2024.
Psychogyios, Konstantinos, Andreas Papadakis, Stavroula Bourou, Nikolaos Nikolaou, Apostolos Maniatis, Theodore Zahariadis. "Deep Learning for IDSs in Time Series Data" Encyclopedia, https://encyclopedia.pub/entry/55638 (accessed April 21, 2024).
Psychogyios, K., Papadakis, A., Bourou, S., Nikolaou, N., Maniatis, A., & Zahariadis, T. (2024, February 28). Deep Learning for IDSs in Time Series Data. In Encyclopedia. https://encyclopedia.pub/entry/55638
Psychogyios, Konstantinos, et al. "Deep Learning for IDSs in Time Series Data." Encyclopedia. Web. 28 February, 2024.
Deep Learning for IDSs in Time Series Data
Edit

Classification-based intrusion detection systems (IDSs) use machine learning algorithms to classify incoming data into different categories based on a set of features. Even though classification-based IDSs are effective in detecting known attacks, they can be less effective in identifying new and unknown attacks that have a small correlation with the training dataset. On the other hand, anomaly detection-based approaches use statistical models and machine learning algorithms to establish a baseline of normal behavior and identify deviations from that baseline.

intrusion detection system deep learning time series forecasting

1. Introduction

The universality of the internet and computer networks has revolutionized the way we interact with each other, enabling information sharing and collaboration at an unprecedented scale. However, this pervasive connectivity has also created new opportunities for malicious actors to exploit vulnerabilities and gain unauthorized access to sensitive information [1]. As a result, the importance of effective intrusion detection systems cannot be overstated, and the need for proactive notification is emerging [2]. IDS stands for a hardware device or software application used to monitor and detect suspicious network traffic and potential security breaches flagging malicious activity. This monitoring takes place at the packet level and, thus, such a system can distinguish malicious from benign packets. Traditionally, this component has been implemented as a firewall and later as a rule-based expert system. Due to the rise in ML in recent years [3][4][5][6], state-of-the-art approaches are based on ML technologies applied to data logs from IDSs, to classify packets as suspicious or not [7][8][9][10][11].
Moreover, multivariate time series prediction [12][13][14] is a sophisticated analytical approach that involves forecasting future values of multiple interrelated variables over time. Unlike univariate time series analysis, which focuses on a single variable, multivariate time series prediction considers the dynamic interactions and dependencies among several variables simultaneously. This method is particularly relevant in fields such as finance [15][16][17], weather forecasting [18], and industrial processes [19], where various factors influence the outcome of interest. The complexity lies in capturing the intricate relationships among different variables and understanding how changes in one can impact the others. Advanced machine learning techniques, including recurrent neural networks (RNNs) [20], LSTMs, and autoregressive integrated moving average (ARIMA) models [21], are commonly employed to handle the complexity of multivariate time series data. Accurate predictions in this context can provide valuable insights into informed decision-making, risk management, and optimizing processes in diverse domains.
Machine learning IDSs can be broadly categorized into two types: (i) classification-based [22][23][24] and (ii) anomaly-based [25]. Classification-based IDSs use machine learning algorithms to classify incoming data into different categories based on a set of features. Even though classification-based IDSs are effective in detecting known attacks, they can be less effective in identifying new and unknown attacks that have a small correlation with the training dataset. On the other hand, anomaly detection-based approaches use statistical models and machine learning algorithms to establish a baseline of normal behavior and identify deviations from that baseline. Unlike classification-based IDSs, anomaly-based IDSs can detect unknown or novel attacks that have not been previously seen. However apart from this advantage, these models cannot easily specify the type of attack and perform worse than classification approaches for known data types [26][27][28].

2. Machine Learning Intrusion Detection Systems

The field of IDSs using machine learning has seen extensive research with new methods and datasets emerging frequently [29][30]. Predicting attacks through IDS log analysis can serve as a proactive security notification feed for an organization, enhancing complementary analysis and the assessment of vulnerabilities, as pursued in [31]. Maseer, Z.K et al. [32] evaluated many machine learning classification methods on the CIC-IDS2017 [33] dataset. Regarding the pre-processing steps, they conducted one-hot encoding and normalization. They also employed parameter tuning and k-fold cross-validation for the training phase. The methods employed were the standard classification approaches, such as random forest, support vector machines, convolutional neural networks, etc., for the binary classification task. They measured accuracy (with binary accuracy, F1 score, precision, etc.) and training/testing times. The results showed that KNN, random forest, and naive Bayes achieve excellent results for these metrics.
Imran, M. et al. [34] evaluated custom autoencoder-based models against KDD-99 [35] for the multiclass classification problem. They developed a non-symmetric deep autoencoder, which was either used as a single model (NDAE) or in a stacked manner (S-NDAE). The term non-symmetric refers to the architectures of the encoder and decoder models, which in this case are not similar (symmetric). They evaluated the performance of these models with common metrics, namely accuracy, precision, etc. The results showed that this method achieves better metrics compared to different state-of-the-art approaches. Saba, T. et al. [36] developed an intrusion detection model that was tested with BoT-IoT [37] and NID datasets (https://www.kaggle.com/datasets/sampadab17/network-intrusion-detection 23 November 2023). The proposed model was a convolutional neural network. The BoT-IoT dataset was used for the multiclass classification task, whereas NID was used for binary classification. The results showed that the proposed model was able to classify the packets with an accuracy of 95%.
Pranto, M.B et al. [38] tested many classification methods using the KDD-99 dataset. Regarding the pre-processing steps, they emphasize feature selection using famous techniques (selecting K-Best) to achieve better accuracy in the classification task. From the tested methods, random forest performed the best, reaching an accuracy of 99% for the binary classification task.
Tahri, R. et al. [39] compared many articles that proposed IDSs based on the UNSW-NB15 dataset, and more specifically, on the Kaggle 100.000 sample version. In their survey, they found that random forest was the best-performing model in most of the studies, reaching an accuracy of up to 98%, specificity of up to 98%, and sensitivity of 94% for the binary classification task.
Regarding approaches that address this problem as a time series instance, Duque A. S. et al. [40] proposed the use of machine learning-based intrusion detection techniques or analyzing industrial time series data. The paper evaluated three different algorithms, namely matrix profiles, seasonal autoregressive-integrated moving average (SARIMA), and LSTM-based [41] neural networks, using an industrial dataset based on the Modbus/TCP protocol. The paper demonstrated that the matrix profile algorithm outperformed the other models in detecting anomalies in industrial time series data, requiring minimal parameterization effort.

3. Multivariate Time Series Prediction

When reviewing related work on multivariate time series prediction, researchers have explored various methodologies to enhance forecasting accuracy and address the complexities inherent in analyzing multiple interrelated variables over time [42][43][44]. Additionally, existing literature has investigated diverse applications of multivariate time series prediction, ranging from financial markets to healthcare, contributing valuable insights into the challenges and advancements within this interdisciplinary field.
Bloemheuvel, S. et al. [45] introduced a novel graph neural network (GNN)-based architecture, TISER-GCN, for multivariate time series regression, in the context of sensor networks. It addresses the limitations of existing deep learning techniques that focus solely on time series data, neglecting spatial relations among geographically distributed sensors. The proposed model was evaluated using high-frequency seismic data, demonstrating its effectiveness when compared to baseline models and traditional machine learning methods, with contributions including the development of a flexible architecture for various use cases, a thorough evaluation of diverse seismic datasets, and a systematic analysis of the model’s capabilities through extensive experimentation.
Gorbett, M. et al. [46] proposed an extension of the lottery ticket hypothesis to time series Transformers, demonstrating that pruning and binarizing the weights of the model maintains accuracy similar to that of a dense Transformer. Employing the Biprop algorithm, a technique proven on complex datasets, the combination of weight binarization and pruning was applied to achieve computational advantages, reducing non-zero floating-point operations (FLOPs) and storage sizes. The approach was specifically tested on multivariate time series modeling, showcasing its effectiveness in tasks like classification, anomaly detection, and forecasting, with potential applications in resource-constrained environments such as IoT devices, engines, and spacecraft.
Wang, D. et al. [47] addressed the importance of accurate predictions in various applications of multivariate time series, such as stock prices, traffic prediction, and COVID-19 spread forecasts. They introduced the challenges faced in capturing both temporal relationships and variable dependencies in existing forecasting methods, emphasizing the need for a comprehensive understanding of underlying patterns. The work proposed a spatiotemporal self-attention-based LSTNet model, integrating spatial and temporal self-attention mechanisms to capture relationships among variables and historical observations. The contributions included the effectiveness of the proposed model in capturing spatiotemporal relationships, a novel objective function to address imbalanced errors among variables, and extensive experiments demonstrating the efficiency of LSTM-based methods in multivariate time series forecasting.

References

  1. Alshamrani, A.; Myneni, S.; Chowdhary, A.; Huang, D. A survey on advanced persistent threats: Techniques, solutions, challenges, and research opportunities. IEEE Commun. Surv. Tutor. 2019, 21, 1851–1877.
  2. Khraisat, A.; Gondal, I.; Vamplew, P.; Kamruzzaman, J. Survey of intrusion detection systems: Techniques, datasets and challenges. Cybersecurity 2019, 2, 1–22.
  3. Dou, B.; Zhu, Z.; Merkurjev, E.; Ke, L.; Chen, L.; Jiang, J.; Zhu, Y.; Liu, J.; Zhang, B.; Wei, G.W. Machine learning methods for small data challenges in molecular science. Chem. Rev. 2023, 123, 8736–8780.
  4. Psychogyios, K.; Ilias, L.; Ntanos, C.; Askounis, D. Missing value imputation methods for electronic health records. IEEE Access 2023, 11, 21562–21574.
  5. Psychogyios, K.; Ilias, L.; Askounis, D. Comparison of Missing Data Imputation Methods using the Framingham Heart study dataset. In Proceedings of the 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece, 27–30 September 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 1–5.
  6. Psychogyios, K.; Leligou, H.C.; Melissari, F.; Bourou, S.; Anastasakis, Z.; Zahariadis, T. SAMStyler: Enhancing Visual Creativity with Neural Style Transfer and Segment Anything Model (SAM). IEEE Access 2023, 13, 100256–100267.
  7. Halbouni, A.; Gunawan, T.S.; Habaebi, M.H.; Halbouni, M.; Kartiwi, M.; Ahmad, R. Machine learning and deep learning approaches for cybersecuriy: A review. IEEE Access 2022, 10, 19572–19585.
  8. Zhu, J.J.; Yang, M.; Ren, Z.J. Machine learning in environmental research: Common pitfalls and best practices. Environ. Sci. Technol. 2023, 57, 17671–17689.
  9. He, K.; Kim, D.D.; Asghar, M.R. Adversarial machine learning for network intrusion detection systems: A comprehensive survey. IEEE Commun. Surv. Tutor. 2023, 25, 538–566.
  10. Hariharan, S.; Rejimol Robinson, R.R.; Prasad, R.R.; Thomas, C.; Balakrishnan, N. XAI for intrusion detection system: Comparing explanations based on global and local scope. J. Comput. Virol. Hacking Tech. 2023, 19, 217–239.
  11. Al-Shareeda, M.A.; Manickam, S.; Ali, M. DDoS attacks detection using machine learning and deep learning techniques: Analysis and comparison. Bull. Electr. Eng. Inform. 2023, 12, 930–939.
  12. Wang, X.; Liu, H.; Du, J.; Dong, X.; Yang, Z. A long-term multivariate time series forecasting network combining series decomposition and convolutional neural networks. Appl. Soft Comput. 2023, 139, 110214.
  13. Wang, J.; Lin, L.; Gao, S.; Zhang, Z. Deep generation network for multivariate spatio-temporal data based on separated attention. Inf. Sci. 2023, 633, 85–103.
  14. Wang, K.; Li, K.; Zhou, L.; Hu, Y.; Cheng, Z.; Liu, J.; Chen, C. Multiple convolutional neural networks for multivariate time series prediction. Neurocomputing 2019, 360, 107–119.
  15. Tsay, R.S. Multivariate Time Series Analysis: With R and Financial Applications; John Wiley & Sons: Hoboken, NJ, USA, 2013.
  16. Yang, Y.; Lu, J. Foreformer: An enhanced transformer-based framework for multivariate time series forecasting. Appl. Intell. 2023, 53, 12521–12540.
  17. Hossin, M.S. Interest rate deregulation, financial development and economic growth: Evidence from Bangladesh. Glob. Bus. Rev. 2023, 24, 690–703.
  18. Sanhudo, L.; Rodrigues, J.; Vasconcelos Filho, E. Multivariate time series clustering and forecasting for building energy analysis: Application to weather data quality control. J. Build. Eng. 2021, 35, 101996.
  19. Yao, Y.; Yang, M.; Wang, J.; Xie, M. Multivariate Time-Series Prediction in Industrial Processes via a Deep Hybrid Network Under Data Uncertainty. IEEE Trans. Ind. Inform. 2022, 19, 1977–1987.
  20. Medsker, L.R.; Jain, L.C. Recurrent neural networks. Des. Appl. 2001, 5, 2.
  21. Shumway, R.H.; Stoffer, D.S.; Shumway, R.H.; Stoffer, D.S. ARIMA models. In Time Series Analysis and Its Applications: With R Examples; Springer: Berlin/Heidelberg, Germany, 2017; pp. 75–163.
  22. Le, T.T.H.; Oktian, Y.E.; Kim, H. XGBoost for imbalanced multiclass classification-based industrial internet of things intrusion detection systems. Sustainability 2022, 14, 8707.
  23. Ahakonye, L.A.C.; Nwakanma, C.I.; Lee, J.M.; Kim, D.S. Agnostic CH-DT technique for SCADA network high-dimensional data-aware intrusion detection system. IEEE Internet Things J. 2023, 10, 10344–10356.
  24. Rabhi, S.; Abbes, T.; Zarai, F. IoT routing attacks detection using machine learning algorithms. Wirel. Pers. Commun. 2023, 128, 1839–1857.
  25. Hajisalem, V.; Babaie, S. A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. Comput. Netw. 2018, 136, 37–50.
  26. Pang, G.; Shen, C.; Cao, L.; Hengel, A.V.D. Deep learning for anomaly detection: A review. ACM Comput. Surv. CSUR 2021, 54, 1–38.
  27. Yao, W.; Shi, H.; Zhao, H. Scalable anomaly-based intrusion detection for secure Internet of Things using generative adversarial networks in fog environment. J. Netw. Comput. Appl. 2023, 214, 103622.
  28. Xiao, J.; Yang, L.; Zhong, F.; Chen, H.; Li, X. Robust anomaly-based intrusion detection system for in-vehicle network by graph neural network framework. Appl. Intell. 2023, 53, 3183–3206.
  29. Thakkar, A.; Lohiya, R. A review on machine learning and deep learning perspectives of IDS for IoT: Recent updates, security issues, and challenges. Arch. Comput. Methods Eng. 2021, 28, 3211–3243.
  30. Saranya, T.; Sridevi, S.; Deisy, C.; Chung, T.D.; Khan, M.A. Performance analysis of machine learning algorithms in intrusion detection system: A review. Procedia Comput. Sci. 2020, 171, 1251–1260.
  31. Nikolaou, N.; Papadakis, A.; Psychogyios, K.; Zahariadis, T. Vulnerability Identification and Assessment for Critical Infrastructures in the Energy Sector. Electronics 2023, 12, 3185.
  32. Maseer, Z.K.; Yusof, R.; Mostafa, S.A.; Bahaman, N.; Musa, O.; Al-rimy, B.A.S. DeepIoT. IDS: Hybrid deep learning for enhancing IoT network intrusion detection. Computers. Mater. Contin. 2021, 69, 3945–3966.
  33. Sharafaldin, I.; Lashkari, A.H.; Ghorbani, A.A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. In Proceedings of the 4th International Conference on Information Systems Security and Privacy, Madeira, Portugal, 22–24 January 2018; pp. 108–116.
  34. Imran, M.; Haider, N.; Shoaib, M.; Razzak, I. An intelligent and efficient network intrusion detection system using deep learning. Comput. Electr. Eng. 2022, 69, 107764.
  35. Bay, S.D.; Kibler, D.; Pazzani, M.J.; Smyth, P. The UCI KDD archive of large data sets for data mining research and experimentation. Acm Sigkdd Explor. Newsl. 2000, 2, 81–85.
  36. Saba, T.; Rehman, A.; Sadad, T.; Koliv, H.; Bahaj, S.A. Anomaly-based intrusion detection system for IoT networks through deep learning model. Comput. Electr. Eng. 2022, 99, 107810.
  37. Koroniotis, N.; Moustafa, N.; Sitnikova, E.; Turnbull, B. Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Gener. Comput. Syst. 2019, 100, 779–796.
  38. Pranto, M.B.; Ratul, M.H.A.; Rahman, M.M.; Diya, I.J.; Zahir, Z.B. Performance of machine learning techniques in anomaly detection with basic feature selection strategy—A network intrusion detection system. J. Adv. Inf. Technol. 2022, 13, 36–44.
  39. Tahri, R.; Jarrar, A.; Lasbahani, A.; Balouki, Y. A comparative study of Machine learning Algorithms on the UNSW-NB 15 Dataset. In Proceedings of the ITM Web of Conferences, Craiova, Romania, 29 June–2 July 2022; Volume 48, p. 03002.
  40. Anton, S.D.; Ahrens, L.; Fraunholz, D.; Schotten, H.D. Time is of the essence: Machine learning-based intrusion detection in industrial time series data. In Proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW), Singapore, 17–20 November 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6.
  41. Hochreiter, S.; Schmidhuber, J. Long Short-term Memory. Neural Comput. 1997, 9, 1735–1780.
  42. Luo, Y.; Cai, X.; Zhang, Y.; Xu, J. Multivariate time series imputation with generative adversarial networks. Adv. Neural Inf. Process. Syst. 2018, 31, 1596–1607.
  43. Su, Y.; Zhao, Y.; Niu, C.; Liu, R.; Sun, W.; Pei, D. Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4–8 August 2019; pp. 2828–2837.
  44. Jones, S.S.; Evans, R.S.; Allen, T.L.; Thomas, A.; Haug, P.J.; Welch, S.J.; Snow, G.L. A multivariate time series approach to modeling and forecasting demand in the emergency department. J. Biomed. Inform. 2009, 42, 123–139.
  45. Bloemheuvel, S.; van den Hoogen, J.; Jozinović, D.; Michelini, A.; Atzmueller, M. Graph neural networks for multivariate time series regression with application to seismic data. Int. J. Data Sci. Anal. 2023, 16, 317–332.
  46. Gorbett, M.; Shirazi, H.; l Ray, I. Sparse Binary Transformers for Multivariate Time Series Modeling. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, USA, 6–10 August 2023; pp. 544–556.
  47. Wang, D.; Chen, C. Spatiotemporal Self-Attention-Based LSTNet for Multivariate Time Series Prediction. Int. J. Intell. Syst. 2023, 2023, 9523230.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , , , ,
View Times: 44
Revisions: 2 times (View History)
Update Date: 29 Feb 2024
1000/1000