An Artificial Intelligence-based Intrusion Detection System

Thani Almuhairi; Ahmad Almarri; Khalid Hokal

doi:https://doi.org/10.54216/JCIM.07.02.04

An Artificial Intelligence-based Intrusion Detection System

Thani Almuhairi ^{1
*} , Ahmad Almarri ² , Khalid Hokal ³

1 American University in the Emirates, Dubai, UAE - (171110066@aue.ae)

2 American University in the Emirates, Dubai, UAE - (171110025@aue.ae)

3 American University in the Emirates, Dubai, UAE - (181120029@aue.ae)

Doi: https://doi.org/10.54216/JCIM.07.02.04

Abstract

Intrusion detection systems have been used in many systems to avoid malicious attacks. Traditionally, these intrusion detection systems use signature-based classification to detect predefined attacks and monitor the network's overall traffic. These intrusion detection systems often fail when an unseen attack occurs, which does not match with predefined attack signatures, leaving the system hopeless and vulnerable. In addition, as new attacks emerge, we need to update the database of attack signatures, which contains the attack information. This raises concerns because it is almost impossible to define every attack in the database and make the process costly also. Recently, research in conjunction with artificial intelligence and network security has evolved. As a result, it created many possibilities to enable machine learning approaches to detect the new attacks in network traffic. Machine learning has already shown successful results in the domain of recommendation systems, speech recognition, and medical systems. So, in this paper, we utilize machine learning approaches to detect attacks and classify them. This paper uses the CSE-CIC-IDS dataset, which contains normal and malicious attacks samples. Multiple steps are performed to train the network traffic classifier. Finally, the model is deployed for testing on sample data.

Keywords :

Artificial Intelligence, Intrusion detection system, Machine learning, decision tree

References

[1] E. G. J. S. B. H. C. A. O. A. a. O. E. A. Dada, "Machine learning for email spam filtering: review, approaches and open research problems," Heliyon 5, no. 6, 2019.

[2] J. MacQueen, "Some methods for classification and analysis of multivariate observations," In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967.

[3] M. Dua, "Machine Learning Approach to IDS: A Comprehensive Review," In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 117-121. IEEE, 2019.

[4] Z. Liu, "A method of SVM with normalization in intrusion detection.," Procedia Environmental Sciences 11 , 2011.

[5] K. C. 1. Dataset. Machine Learning Library - s.-l. [. A. 2021].

[6] R. a. S. B. Panigrahi, "A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems."," International Journal of Engineering & Technology 7, no. 3.24 , 2018.

[7] P. Flach, "Machine learning: the art and science of algorithms that make sense of data.," Cambridge University Press, 2012.

[8] M. Maloof, " Machine learning and data mining for computer security: methods and applications.," Springer Science & Business Media, 2006.

[9] R. Y. A. S. C. J. K.-C. M. F. C. a. J. P. C. Choi, "Introduction to machine learning, neural networks, and deep learning," Translational Vision Science & Technology 9, no. 2, 2020.

[10] E. M. Ö. S. A. &. I. T. Karabulut, " A comparative study on the effect of feature selection on classification accuracy.," Procedia Technology, 1, 323-327., 2012.

[11] N. a. J. S. Moustafa, "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)."," In 2015 military communications and information systems conference (MilCIS, 2015.

[12] T. I. K. a. I. T. Shatovska, "New module of text classification for IDA system.," In 2009 10th International Conference-The Experience of Designing and Application of CAD Systems in Microelectronics, pp. 481-482. IEEE, , 2009.

[13] R. Y. A. S. C. J. K.-C. M. F. C. a. J. P. C. Choi, "Introduction to machine learning, neural networks, and deep learning," Translational Vision Science & Technology 9, no. 2, 2020.

Cite This Article As :

Almuhairi, Thani. , Almarri, Ahmad. , Hokal, Khalid. An Artificial Intelligence-based Intrusion Detection System. Journal of Cybersecurity and Information Management, vol. , no. , 2021, pp. 95-111. DOI: https://doi.org/10.54216/JCIM.07.02.04

Almuhairi, T. Almarri, A. Hokal, K. (2021). An Artificial Intelligence-based Intrusion Detection System. Journal of Cybersecurity and Information Management, (), 95-111. DOI: https://doi.org/10.54216/JCIM.07.02.04

Almuhairi, Thani. Almarri, Ahmad. Hokal, Khalid. An Artificial Intelligence-based Intrusion Detection System. Journal of Cybersecurity and Information Management , no. (2021): 95-111. DOI: https://doi.org/10.54216/JCIM.07.02.04

Almuhairi, T. , Almarri, A. , Hokal, K. (2021) . An Artificial Intelligence-based Intrusion Detection System. Journal of Cybersecurity and Information Management , () , 95-111 . DOI: https://doi.org/10.54216/JCIM.07.02.04

Almuhairi T. , Almarri A. , Hokal K. [2021]. An Artificial Intelligence-based Intrusion Detection System. Journal of Cybersecurity and Information Management. (): 95-111. DOI: https://doi.org/10.54216/JCIM.07.02.04

Almuhairi, T. Almarri, A. Hokal, K. "An Artificial Intelligence-based Intrusion Detection System," Journal of Cybersecurity and Information Management, vol. , no. , pp. 95-111, 2021. DOI: https://doi.org/10.54216/JCIM.07.02.04

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An Artificial Intelligence-based Intrusion Detection System

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References

[1] E. G. J. S. B. H. C. A. O. A. a. O. E. A. Dada, "Machine learning for email spam filtering: review, approaches and open research problems," Heliyon 5, no. 6, 2019.

[2] J. MacQueen, "Some methods for classification and analysis of multivariate observations," In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967.

[3] M. Dua, "Machine Learning Approach to IDS: A Comprehensive Review," In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 117-121. IEEE, 2019.

[4] Z. Liu, "A method of SVM with normalization in intrusion detection.," Procedia Environmental Sciences 11 , 2011.

[5] K. C. 1. Dataset. Machine Learning Library - s.-l. [. A. 2021].

[6] R. a. S. B. Panigrahi, "A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems."," International Journal of Engineering & Technology 7, no. 3.24 , 2018.

[7] P. Flach, "Machine learning: the art and science of algorithms that make sense of data.," Cambridge University Press, 2012.

[8] M. Maloof, " Machine learning and data mining for computer security: methods and applications.," Springer Science & Business Media, 2006.

[9] R. Y. A. S. C. J. K.-C. M. F. C. a. J. P. C. Choi, "Introduction to machine learning, neural networks, and deep learning," Translational Vision Science & Technology 9, no. 2, 2020.

[10] E. M. Ö. S. A. &. I. T. Karabulut, " A comparative study on the effect of feature selection on classification accuracy.," Procedia Technology, 1, 323-327., 2012.

[11] N. a. J. S. Moustafa, "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)."," In 2015 military communications and information systems conference (MilCIS, 2015.

[12] T. I. K. a. I. T. Shatovska, "New module of text classification for IDA system.," In 2009 10th International Conference-The Experience of Designing and Application of CAD Systems in Microelectronics, pp. 481-482. IEEE, , 2009.

[13] R. Y. A. S. C. J. K.-C. M. F. C. a. J. P. C. Choi, "Introduction to machine learning, neural networks, and deep learning," Translational Vision Science & Technology 9, no. 2, 2020.

Cite This Article As :

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