Network Data Classification through Artificial Neural Networks and GenClust++ Algorithm
Ichrak LAFRAM1, Siham EL IDRISSI2, Aicha Marrhich3, Naoual BERBICHE4, Jamila EL ALAMI5
1Ichrak lafram, LASTIMI Laboratory, Superior School of Technologies of Sale, Mohammadia School of Engineering, Mohamed V University City of Rabat, Morocco.
2Siham El Idrissi, LASTIMI Laboratory, Superior School of Technologies of Sale, Mohammadia School of Engineering, Mohamed V University City of Rabat, Morocco.
3Aicha Marrhich, LASTIMI Laboratory, Superior School of Technologies of Sale, Mohammadia School of Engineering, Mohamed V University City of Rabat, Morocco.
4Naoual Berbiche, LASTIMI Laboratory, Superior School of Technologies of Sale, Mohammadia School of Engineering, Mohamed V University City of Rabat, Morocco.
5Jamila EL Alami, LASTIMI Laboratory, Superior School of Technologies of Sale, Mohammadia School of Engineering, Mohamed V University City of Rabat, Morocco.
Manuscript received on 08 August 2019 | Revised Manuscript received on 14 August 2019 | Manuscript published on 30 August 2019 | PP: 2743-2750 | Volume-8 Issue-10, August 2019 | Retrieval Number: J95650881019/2019©BEIESP | DOI: 10.35940/ijitee.J9565.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Information systems are becoming more and more complex and closely linked due to the exponential use of internet applications. These systems are encountering an enormous amount of traffic, this traffic can be a normal one destined for natural use or it may be a malicious one intended to violate the security of the system. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is the Intrusion Detection System (IDS). An IDS, while ensuring real – time connectivity, tries to identify fraudulent activity using predetermined signatures or pre-established network behavior while monitoring incoming traffic. Intrusion detection systems based on signature or behavior cannot detect new attacks and fall when small deviations occur. Also, current anomaly detection approaches suffer often from high false alarms. As a solution to these problems, machine learning techniques are a new and promising tool for the identification of attacks. In this paper, the authors present a hybrid approach, combining artificial neural networks and a hybrid clustering algorithm based on k-means and genetic algorithm called GenClust++. The final framework leads to a fast, highly scalable and precise packets classification system. We tested our work on the newly published dataset CICIDS 2017. The overall process was fast, showing high accuracy classification results.
Index terms: Intrusion Detection, Machine Learning, Traffic Classification, Artificial Neural Networks, Clustering, GenClust++.
Scope of the Article: Classification