Development of a stacked hybrid Decision Tree model leveraging the NSL-KDD dataset

Edosa Osa; Patience Orukpe; Iruansi Usiholo

doi:10.59400/jam.v1i4.271

Development of a stacked hybrid Decision Tree model leveraging the NSL-KDD dataset

Edosa Osa Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Benin, P.M.B. 1154, Benin City, Nigeria
Patience Orukpe Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Benin, P.M.B. 1154, Benin City, Nigeria
Iruansi Usiholo Department of Computer Engineering, Faculty of Engineering, University of Benin, P.M.B. 1154, Benin City, Nigeria

Article ID: 271

2879 Views, 1299 PDF Downloads

DOI: https://doi.org/10.59400/jam.v1i4.271

Keywords: machine learning; decision tree; hybrid; intrusion; detection

Abstract

Intrusion detection in information technology as well as operational technology networks is highly required in modern day systems due to the increased spate of cyber-attacks in both number and complexity. Anomaly-based intrusion detection systems which have the capacity to detect novel or zero-day attacks are highly employed in this regard. One important component of anomaly-based intrusion detection systems which ensures their behaviour is artificial intelligence in general and machine learning in particular. The burden in modern day cybersecurity research is to investigate and develop models that can outperform existing ones. This paper is aimed at developing a hybrid decision tree model using the stacking ensemble approach. Performances were measured on the basis of recall, precision, accuracy, F1-score, receiver operating characteristics and confusion matrices. The hybrid model presented a precision of 97%, accuracy of 81%, F1-score of 80% and AUC score of 0.96, respectively.

References

[1]Mohajan HK. Third industrial revolution brings global development. Journal of Social Sciences and Humanities 2021; 7(4): 239–251.

[2]Orukpe PE, Erhiaguna TO, Agbontaen FO. Computer security and privacy in wireless local area network in Nigeria. International Journal of Engineering Research in Africa 2013; 9: 23–33. doi: 10.4028/www.scientific.net/jera.9.23

[3]Shruti M. Types of cyber attacks you should be aware of in 2023. Available online: https://www.simplilearn.com/tutorials/cyber-security-tutorial/types-of-cyber-attacks (accessed on 11 August 2023).

[4]FORTINET. Types of cyber attacks. Available online: https://www.fortinet.com/resources/cyberglossary/types-of-cyber-attacks (accessed on 11 August 2023).

[5]DNV. The three-pillar approach to cyber security: Data and information protection. Available online: https://www.dnv.com/article/the-three-pillar-approach-to-cyber-security-data-and-information-protection-165683 (accessed on 11 August 2023).

[6]James K. What are the 5 pillars of cybersecurity? Available online: https://cybersecurityforme.com/what-are-the-5-pillars-of-cybersecurity (accessed on 11 August 2023).

[7]Osa E. Cyber security terminologies and concepts. In: SMART-IEEE-ACity-ICTU-CRACC-ICTU-Foundations Series Book Chapter on Web of Deceit. Creative Research Publishers; 2022. pp. 231–236.

[8]Rama Devi R, Abualkibash M. Intrusion detection system classification using different machine learning algorithms on KDD-99 and NSL-KDD datasets—A review paper. International Journal of Computer Science and Information Technology 2019; 11(3): 65–80. doi: 10.5121/ijcsit.2019.11306

[9]Abuh A, Orukpe PE. Development of an integrated campus security alerting and access control system. In: Emerging Trends in Engineering Research and Technology Vol. 9. Book Publisher International; 2020. pp. 57–67.

[10]Chudasma P. Network Intrusion Detection System using Classification Techniques in Machine Learning [Master’s thesis]. Dublin Business School; 2020.

[11]Sangfor Technologies. What is an intrusion detection system and how does it work. Available online: https://www.sangfor.com/glossary/cybersecurity/what-is-intrusion-detection-system-and-how-does-it-work (accessed on 11 August 2023).

[12]Velimirovic A. What is an intrusion detection system? Available online: https://phoenixnap.com/blog/intrusion-detection-system (accessed on 11 August 2023).

[13]FORTINET. Intrusion Detection System (IDS). Available online: https://www.fortinet.com/resources/cyberglossary/intrusion-detection-system (accessed on 11 August 2023).

[14]Géron A. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media, Inc.; 2019.

[15]Alladi SK. Effectively Improving the Efficiency and Performance of an Intrusion Detection System Using Hybrid Machine Learning Models [Master’s thesis]. National College of Ireland; 2020.

[16]Sapre S, Ahmadi P, Islam K. A robust comparison of the KDDCup99 and NSL-KDD IoT network intrusion detection datasets through various machine learning algorithms. arXiv 2019; arXiv:1912.13204. doi: 10.48550/arXiv.1912.13204

[17]Vinayakumar R, Alazab M, Soman KP, et al. Deep learning approach for intelligent intrusion detection system. IEEE Access 2019; 7: 41525–41550. doi: 10.1109/access.2019.2895334

[18]Li Y, Gao J, Li Q, Fan W. Ensemble learning. In: Data Classification Algorithms and Applications’ Data Mining and Knowledge Discovery Series. CRC Press; 2015. pp. 498–500.

[19]Bhandari A. Guide to AUC ROC Curve in machine learning: What is specificity? Available online: www.analyticsvidhya.com/blog/2020/06/auc-roc-curve-machine-learning/ (accessed on 8 June 2023).

Published

2023-12-21

How to Cite

Osa, E., Orukpe, P., & Usiholo, I. (2023). Development of a stacked hybrid Decision Tree model leveraging the NSL-KDD dataset. Journal of AppliedMath, 1(4), 271. https://doi.org/10.59400/jam.v1i4.271

Download Citation

Issue

Vol. 1 No. 4 (2023)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

Editor-in-Chief

Assoc. Prof. Ahmad Zeeshan

International Islamic University, Pakistan

eISSN

2972-4805

Publication Frequency

Bi-monthly

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

more

Volume Arrangement

2024

2023

Featured Articles

Octagonal-square tessellation model for masting GSM network: A case study of MTN Kumasi-East, Ghana

Masting in GSM network design is one of the most challenging problems in cell planning. The effect of uniform design pattern has been proven geographically to be hexagonal using uniform cell range. In this paper, we present a new uniform greedy semi-regular tessellation model called the octagonal square tessellation model (OSTM) to address the problem of global minimum overlap difference and area. Data from MTN Kumasi-East Ghana was collected and analyzed using the developed model. The original layout for the 0.6 km cell range accounted for an overlap difference of 937.66 m and a total area of 21.41 km² for 50 GSM mosts whereas the OSTM model accounted for an overlap difference of 1316.95 m with an area of 34.23 km². This is a 59.87% reduction of the original total area. Our solution is shown to be optimal in overlap difference and area for non-uniform cell range.

Computation of topological indices of linear chains of perylene and coronene using M-polynomial

The M-polynomial yields degree based topological indices that anticipate different physical and chemical properties of material being scrutinized. In this work, M- polynomial of linear chains of perylene and coronene are acquired. From M-polynomial, some degree based topological dicriptors are determined. Some topological indices of these compounds are compared by plotting graphs.

H∞ hybrid control and MRD in a steel frame building subjected to excessivevibrations caused by the dynamic action of wind and earthquake

The dynamic loads from earthquakes and winds can destroy lives, cause collapse in civil structures, and interrupt basic services provided to the population. In this scenario, structural designs must be developed to decrease the damage induced by these actions. The objective of this work is to design a hybrid controller based on the H∞ optimization via state feedback and the magneto-rheological damper (MRD) to mitigate the excessive vibrations of a three-story steel frame building, represented through the shear building model, subjected to the simultaneous dynamic action of wind and earthquake. All research is based on computational simulation, experimental research and results will not be addressed. In the numerical analysis, digital computer and MATLAB® software are used, and implemented codes generate the expected results based on the mathematical modeling. With the application of the H∞ control technique via state feedback, the displacements were reduced by 77%. With MRD this reduction was 79%. With the hybrid controller, this reduction was 100%. Thus, the verifications in relation to maximum displacements were met for NBR 15421:2006, NBR 8800:2008 and NBR 6118:2014. From the results, it is concluded that the hybrid controller proved to be more efficient and achieved the proposed objective. The exogenous inputs had zero influence on the behavior of the system output.

Topological analysis of multiple tables

The paper proposes a topological approach in order to explore several data tables simultaneously. These data tables of quantitative and/or qualitative variables measured on different homogeneous themes, collected from the same individuals. This approach, called topological analysis of multiple tables (TAMT), is based on the notion of neighborhood graphs in the context of a joint analysis of several data tables. It allows the simultaneous study of possible links between several thematic tables. The structure of the correlations or associations of the variables in each thematic table is analyzed according to quantitative, qualitative or mixed variables considered. Like the multiple factorial analysis (MFA), the TAMT allows several tables of variables to be analyzed simultaneously, and to obtain results, in particular graphical representations, which make it possible to study the relationship between individuals, variables and tables of data. These can also be tables of temporal data, collected at different times on the same individuals. The proposed TAMT approach is illustrated using real data associated with several and different homogeneous themes. Its results are compared to those from the MFA method.

Development of a stacked hybrid Decision Tree model leveraging the NSL-KDD dataset