A ROBUST DETECTION OF CYBER INCIDENTS UTILIZING MACHINE LEARNING TECHNIQUES

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Mrs J. RATNAKUMARI
VANKAYALAPATI JYOTHI SAIPRIYA
SYED TEHZEEBA
THOTA SRINIVASA Krishna
SHAIKKHADAR Basha

Abstract

A reliable Cyber Attack Detection Model (CADM) is a system that works as safeguard for the users of modern technological devices and assistant for the operators of networks. The research paper aims to develop a CADM for analyzing the network data patterns to classify cyber-attacks. CADM finds out attack wise detection accuracy using ensemble classification method. LASSO has been used to extract important features. It can work with large datasets, and it has more visualization capability. Gradient Boosting and Random Forest algorithms have been used for classification of network traffic data to build an ensemble method. Gradient Boosting algorithm trains weak learning models and select the best decision trees to deliver more improved prediction accuracy and Random Forest algorithm trains each tree in parallel manner. In this research work, Jive datasets such as NSL-KDD, KDD Cup 99, UNSWNB15, URL 2016 and CICIDS 2017 are also applied to check the efficiency of the proposed model.

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How to Cite
RATNAKUMARI, M. J. ., SAIPRIYA, V. J. ., TEHZEEBA, S., Krishna, T. S., & Basha, S. . (2024). A ROBUST DETECTION OF CYBER INCIDENTS UTILIZING MACHINE LEARNING TECHNIQUES. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(1), 100–103. https://doi.org/10.61841/turcomat.v15i1.14547
Section
Research Articles

References

VibekanandaDutta ,MichałChora´s, Marek Pawlicki and RafałKozik, "A Deep Learning Ensemble for Network

Anomaly and Cyber-Attack Detection", Sensors, August 2020.

Quoc-Dung Ngo, Huy-Trung Nguyen, Van-Hoang Le, Doan-Hieu Nguyen, "A survey of IoT malware and

detection methods based on static features", ICT Express, December 2020.

B. Ahmad, W. Jian and Z. Anwar Ali, "Role of Machine Learning and Data Mining in Internet Security: Standing

State with Future Directions," J o u rn a l of Computer Networks and Communications, vol. 2018, pp. 1-10, 2018. doi:

1155/2018/6383145 [Accessed 2 October 2020].

A. Gupta, G. Prasad and S. Nayak, "A New and Secure Intrusion Detecting System for Detection of Anomalies

Within the Big Data," Studies in Big Data, pp. 177-190, 2018. doi: 10.1007/978-3-030-03359- 0_8 [Accessed 30

August 2020].

T. Tang, D. McLernon, L. Mhamdi, S. Zaidi and M. Ghogho, "Intrusion Detection in SDN-Based Networks: Deep

Recurrent Neural Network Approach," Deep Learning Applications for Cyber Security, pp. 175-195, 2019. doi:

1007/978-3-030-13057-2_8' [Accessed 30 August 2020].

C. Gayathri Harshitha, M. Kameswara Rao and P. Neelesh Kumar, "A Novel Mechanism for Host-Based Intrusion

Detection System," In Proc. First International Conference on Sustainable Technologies for Computational

Intelligence, pp. 527-536, 2019. doi: 10.1007/978-981-15- 0029-9 42 [Accessed 21 June 2020].

A. Ahmim, M. Ferrag, L. Maglaras, M. Derdour and H. Janicke, "A Detailed Analysis of Using Supervised

Machine Learning for Intrusion Detection," Strategic Innovative Marketing and Tourism, pp. 629-639, 2020. doi:

1007/978-3-030-36126-6 70 [Accessed 7 August 2020].

R. Jaiswal and S. Lokhande, "Analysis of Early Traffic Processing and Comparison of Machine Learning

Algorithms for Real Time Internet Traffic Identification Using Statistical Approach," Advanced Computing,

Networking and Informatics, vol. 2, Smart Innovation, Systems and Technologies, vol 28, pp. 577-587, 2014. doi:

1007/978-3-319-07350-7 64 [Accessed 24 September 2020].

W. Zong, Y. Chow and W. Susilo, "Interactive three-dimensional visualization of network intrusion detection

data for machine learning," Future Generation Com puter Systems, vol. 102, pp. 292-306, 2020. doi:

1016/j.future.2019.07.045 [Accessed

H. Liu and A. Gegov, "Collaborative Decision Making by Ensemble Rule Based Classification Systems," Studies

in Big Data, pp. 245-264, 2015. doi: 10.1007/978-3-319-16829-6_10 [Accessed 20 September 2020].

A. Bansal and S. Kaur, "Data Dimensionality Reduction (DDR) Scheme for Intrusion Detection System Using

Ensemble and Standalone Classifiers," In Proc. International Conference on Advances in Computing and Data

Sciences, vol. 1045, pp. 436-451, 2019. doi: 10.1007/978-981-13-9939-8 39 [Accessed 15 July 2020].

S. Sandosh, V. Govindasamy and G. Akila, "Enhanced intrusion detection system via agent clustering and

classification based on outlier detection," Peer-to-Peer Networking and Applications, vol. 13, no. 3, pp. 1038-1045,

doi: 10.1007/s12083-019-00822-3 [Accessed 15 July 2020].

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