An Empirical Evaluation On Comparative Machine Learning Techniques For Detection of The Distributed Denial of Service (DDoS) Attacks

  • Arnold Ojugo Department of Computer Science, Federal University of Petroleum Resources Effurun, Nigeria (NG)
  • Andrew Okonji Eboka Department of Computer Science Edu., Federal College of Education (Technical), Nigeria (NG)
Keywords: Intrusion, DDoS, network performance, network resources, machine learning, malicious attacks

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The advent of the Internet that aided the efficient sharing of resources. Also, it has introduced adversaries whom are today restlessly in their continued efforts at an effective, non-detectable means to invade secure systems, either for fun or personal gains. They achieve these feats via the use of malware, which is both on the rise, wreaks havoc alongside causing loads of financial losses to users. With the upsurge to counter these escapades, users and businesses today seek means to detect these evolving behavior and pattern by these adversaries. It is also to worthy of note that adversaries have also evolved, changing their own structure to make signature detection somewhat unreliable and anomaly detection tedious to network administrators. Our study investigates the detection of the distributed denial of service (DDoS) attacks using machine learning techniques. Results shows that though evolutionary models have been successfully implemented in the detection DDoS, the search for optima is an inconclusive and continuous task. That no one method yields a better optima than hybrids. That with hybrids, users must adequately resolve the issues of data conflicts arising from the dataset to be used, conflict from the adapted statistical methods arising from data encoding, and conflicts in parameter selection to avoid model overtraining, over-fitting and over-parameterization.


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A.A. Ojugo., A. Eboka., E. Okonta., R. Yoro., F. Aghware., Genetic algorithm rule-based intrusion detection system, J. of Emerging Trends in Comp. Info. Sys., 2012. 3(8): pp1182-1194

S.S. Kandeeban, R.S. Rajesh, GA for framing rules for intrusion detection, J. Comp. Sci and Security, 2007. 7(11), p285-290.

R. Gong, M. Zulkernine, P. Abolmaesumi, A software implementation of GA based approach to network intrusion detection, 2005,

A.A. Ojugo., E. Ben-Iwhiwhu, O. Kekeje., M. Yerokun., I. Iyawah., Malware propagation on social time varying networks: a comparative study of machine learning frameworks, Int. J. of Modern Education Comp. Sci., 2014, 6(8): pp25-33, doi: 10.5815/ijmecs.2014.08.04, [web]:

I.P. Okobah., A.A. Ojugo., Evolutionary memetic models for malware intrusion detection: a comparative quest for computational solution and convergence, IJCAOnline Int. J. Comp. Appl. 2018. 179(39): pp34-43

S.S. Kandeeban, R.S. Rajesh, Integrated intrusion detection system via soft computing, J. Network Security, 2010. 10(2), p87

A.A. Ojugo., A. Eboka., R. Yoro., M. Yerokun., F.N. Efozia., Hybrid model for early diabetes diagnosis, Mathematics and Computers in Science & Industry, 2015, 50, pp207-217, [web]

A.A. Ojugo., F.O. Aghware., R.E. Yoro., M.O. Yerokun., A.O. Eboka., C.N. Anujeonye., F. Efozia., Evolutionary model for virus propagation on networks, Automation, Control & Intelligent Systems, 2015, 3(4): pp56-62.

A.A. Ojugo., R.E. Yoro., Forging a machine learning intrusion detection model as tools to curb the distributed denial of service (DDoS) attacks, Accepted publication in Int. J. Elect. & Comp. Engr., 2020. 10(1): pp126-132

F. Olusegun, O.A. Oluwatobi, O. O. Adewale, ID-SOMGA: self-organising migrating GA-based solution for Intrusion Detection, Computer and Information Science, 2010. 3(4), p80

M. Perez, T. Marwala, Stochastic optimization for solving Sudoku, Proc. of IEEE on Evol. Computing, 2011. p256 – 279.

B. Shanmugam, N.B. Idris, Hybrid intrusion detection systems using fuzzy logic, 2011.

P. Diaz-Gomez, D. Hougen, Improved off-line intrusion detection using a GA, 2005.

A.A. Ojugo., A.O. Eboka., An intelligent hunting profile for evolvable metamorphic malware, African J. of Computing and ICT, 2015, 8(1-2): pp181 –190, [web]:

A.A. Ojugo., I.P. Okobah., Hybrid fuzzy-genetic algorithm trained neural network model for diabetes diagnosis, Digital Inno. & Contemporary Res. in Sci., Eng. & Tech., 2017, 5(4): pp69-90, [web]:

A.A. Ojugo., D. Allenotor, Text mining identification and detection using the exact string matching algorithm: a comparative analysis, Digital Innovations & Contemporary Research In Science, Engineering & Technology, 2018, 6(1): pp169 – 180

M. Perez, T. Marwala, Microarray data feature selection using hybrid genetic algorithm simulated annealing, IEEE conference on Electrical and Electronics Engineers, 2012, pp 1 – 5, doi: 10.1109/EEEI.2010.6377146

Schafer, J.D., (1985): Multiple objective optimization with vector evaluated Genetic Algorithm,

A.A. Ojugo., A. Eboka., R. Yoro., M. Yerokun., F. Efozia., Framework design for statistical fraud detection, Mathematics and Computers in Science & Industry, 2015, 50, pp176-182

P.J. Criscuolo, Distributed Denial of Service, Tribe Flood Network, and Stacheldraht CIAC-2319. Department of Energy Computer Incident Advisory Capability (CIAC), 2010.

H. Monowar, H. Bhuyan, D.K. Kashyap et al. Detecting Distributed Denial of Service Attacks: Methods, Tools and Future Directions. The Computer Journal, 2012, 3-19.

P.K. Munivara, M. Rama, R.A. Mohan, R.K. Venugopal, DoS and DDoS Attacks: Defense, Detection and Traceback - A Survey, Global J. of Computer Science and Technology: E Network, Web & Security, 2014. 14 (7), 15-31

S. Alexander, An anomaly intrusion detection system based on intelligent user recognition. 2012. Ph.D Thesis, University of Jyväskylä, Faculty of Information Technology, Finland

K. Apoorv, How to deal with IP addresses in Machine Learning algorithms. 2016. Retrieved October 6, 2018, from Quora:

W. Eddy, TCP SYN flooding Attacks and Common Mitigation. 2017. Retrieved June 16, 2018,

P. Garcia-Teodoro, Diaz-Verdejo J., Macia-Fernandez G., E. Vazquez, Anomaly-based network intrusion detection: techniques, systems and challenges. 2012. Computers and Security, 28, 18-28

A.A. Ojugo, T.O. Eduvie, An anomaly-based intrusion detection system using the profile hidden markov chain model. 2018, B.Sc Thesis, Department of Computer Sci, Federal University of Petroleum Resources Effurun, Nigeria.

A.A. Ojugo., A.O. Eboka., Memetic algorithm for short messaging service spam filter text normalization and semantic approach, Int. J. of Info. & Comm. Tech., 9(1): pp13 – 27, doi: 10.11591/ijict.v9i1.pp9-18, [web]:

A.A. Ojugo., A.O. Eboka., (2019). Signature-based malware detection using approximate Boyer Moore string matching algorithm, Int. J. of Math. Sciences and Computing, 3(5): pp49-62, doi: 10.5815/ijmsc.2019.03.05

B. Lavender, Implementation of GA into IDS and integration into nprobe, 2010. Retrieved from [web]

W. Li, GA approach to network IDS, 2004, Retrieved from

T. Vollmer, J. Alves-Foss, M. Manic, Autonomous rule creation for intrusion detection, 2011, Retrieved from [web]:

A.A. Ojugo, and A.O. Eboka, “Comparative Evaluation for High Intelligent Performance Adaptive Model for Spam Phishing Detection.” Digital Technologies, vol. 3, no. 1 (2018): 915. doi: 10.12691/dt-3-1-2.

A.A. Ojugo, and D. Otakore, “Improved Early Detection of Gestational Diabetes via Intelligent Classification Models: A Case of the Niger Delta Region in Nigeria.” Journal of Computer Sciences and Applications, vol. 6, no. 2 (2018): 82-90. doi: 10.12691/jcsa-6-2-5.

A.A. Ojugo & A.O. Eboka, An Intelligent Hunting Profile for Evolvable Metamorphic Malware. Afr J. of Comp & ICTs. 2015. Vol 7, No. 3. Pp 181-190,

Received 2020-06-13
Published 2020-03-21
How to Cite
A. Ojugo and A. O. Eboka, “An Empirical Evaluation On Comparative Machine Learning Techniques For Detection of The Distributed Denial of Service (DDoS) Attacks”, J. Appl. Sci. Eng. Technol. Educ., vol. 2, no. 1, pp. 18-27, Mar. 2020.