Computer Network Attack Detection Using Enhanced Clustering Technologies


  • Dhamyaa Salim Mutar Business Administration Department, Baghdad College of Economic Sciences University, Baghdad, Iraq



FFNN, KDD, K-means, DB Scan, Training, Testing, Intrusion, Attack, Weights, Performance


The need for security means has brought from the fact of privacy of data especially after the communication revolution in the recent times. The advancement of data mining and machine learning technology has paved the road for establishment an efficient attack prediction paradigm for protecting of large scaled networks. In this project, computer network intrusions had been eliminated by using smart machine learning algorithm. Referring a big dataset named as KDD computer intrusion dataset which includes large number of connections that diagnosed with several types of attacks; the model is established for predicting the type of attack by learning through this data. Feed forward neural network model is outperformed over the other proposed clustering models in attack prediction accuracy.


Habibzadeh, H.; Nussbaum, B.H.; Anjomshoa, F.; Kantarci, B.; Soyata, T. A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities. Sustain. Cities Soc. 2019, 50, 101660.

Gartner. Gartner’s Top 10 Security Predictions 2016. Available online: (accessed on 1 February 2020).

Geer, D. The Internet of Things: Top five threats to IoT devices. Available online: (accessed on 1 February 2020).

Ande, R.; Adebisi, B.; Hammoudeh, M.; Saleem, J. Internet of Things: Evolution and technologies from a security perspective. Sustain. Cities Soc. 2019, 54, 101728, doi:10.1016/j.scs.2019.101728.

Riahi, A.; Challal, Y.; Natalizio, E.; Chtourou, Z.; Bouabdallah, A. A Systemic Approach for IoT Security. In Proceedings of the 2013 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Cambridge, MA, USA, 20–23 May 2013.

Jesus Pacheco, S.H. IoT Security Framework for Smart Cyber Infrastructures. In Proceedings of the IEEE International Workshops on Foundations and Applications of Self* Systems, Augsburg, Germany, 12–16 September 2016.

Mijwil, M. M., and Abttan R. A., “Artificial Intelligence: A Survey on Evolution and Future Trends,” Asian Journal of Applied Sciences, vol.9, no.2, pp:87-93, April 2021,

Gauravaram, P. Blockchain for IoT security and privacy: The case study of a smart home. In Proceedings of the 2017 IEEE International Conference on Pervasive Computing andCommunications Workshops (PerCom Workshops), Kona, HI, USA, 13–17 March 2017.

Yao, X.; Han, X.; Du, X.; Zhou, X. A Lightweight Multicast Authentication Mechanism for Small Scale IoT Applications. IEEE Sens. 2013, 13, 3693–3701. [CrossRef]

Butun, I.; Morgera, S.D.; Sankar, R. A Survey of Intrusion Detection Systems in Wireless Sensor Networks. IEEE Commun. Surv. Tutor. 2014, 16, 266–282.

Debar, H.; Dacier, M.; Wespi, A. Towards a taxonomy of intrusion-detection systems. Comput. Netw. 1999, 31, 805–822.

Meng, G.; Liu, Y.; Zhang, J.; Pokluda, A.; Boutaba, R.Collaborative Security: A Survey and Taxonomy. ACM Comput. Surv. 2015, 48, 1:1–1:42.

M. R. Zaidan, "Power System Fault Detection, Classification And Clearance By Artificial Neural Network Controller," in 2019 Global Conference for Advancement in Technology (GCAT) , Bangalore, 2019.

NAMASUDRA, Suyel, et al. Towards DNA based data security in the cloud computing environment. Computer Communications, 2020, 151: 539-547.‏

NAMASUDRA, Suyel, et al. Towards DNA based data security in the cloud computing environment. Computer Communications, 2020, 151: 539-547.‏

Mutar D. S., "Decoding of Convolutional Codes Using Viterbi Algorithm", Journal of Positive Sciences, vol.2021, no.3, pp:1-5, 2021.

Islabudeen, M., & Devi, M. K. (2020). A smart approach for intrusion detection and prevention system in mobile ad hoc networks against security attacks. Wireless Personal Communications, 112(1), 193-224.‏

HALDORAI, Anandakumar; RAMU, Arulmurugan. Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability. Neural Processing Letters, 2021, 53.4: 2385-2401.‏

ANTOLAK, Ernest; PUŁKA, Andrzej. Energy-Efficient Task Scheduling in Design of Multithread Time Predictable Real-Time Systems. IEEE Access, 2021, 9: 121111-121127.‏

OZANICH, Emma; GERSTOFT, Peter; NIU, Haiqiang. A feedforward neural network for direction-of-arrival estimation. The journal of the acoustical society of America, 2020, 147.3: 2035-2048.‏

BELARCHE, Lahoucine, et al. Three-dimensional simulation of controlled cooling of electronic component by natural and mixed convection. Thermal Science, 2020, 00: 181-181.‏




How to Cite

Mutar, D. S. (2021). Computer Network Attack Detection Using Enhanced Clustering Technologies. Asian Journal of Applied Sciences, 9(6).