Performance Analysis of Proposed Hybrid Machine Learning Model for Efficient Intrusion Detection
Aditya Harbola1, Priti Dimri2, Deepti Negi3
1Aditya Harbola*, School of computing, Graphic Era Hill University, Dehradun, India.
2Priti Dimri, Computer science and applications, GBPEC, Ghurdouri, Pauri, Pauri, India.
3Deepti Negi, d School of computing, Graphic Era Hill University, Dehradun, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 27, 2020. | Manuscript published on April 10, 2020. | PP: 904-911 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3467049620/2020©BEIESP | DOI: 10.35940/ijitee.E3467.049620
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© 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: At present networking technologies has provided a better medium for people to communicate and exchange information on the internet. This is the reason in the last ten years the number of internet users has increased exponentially. The high-end use of network technology and the internet has also presented many security problems. Many intrusion detection techniques are proposed in combination with KDD99, NSL-KDD datasets. But there are some limitations of available datasets. Intrusion detection using machine learning algorithms makes the detection system more accurate and fast. So in this paper, a new hybrid approach of machine learning combining feature selection and classification algorithms is presented. The model is examined with the UNSW NB15 intrusion dataset. The proposed model has achieved better accuracy rate and attack detection also improved while the false attack rate is reduced. The model is also successful to accurately classify rare cyber attacks like worms, backdoor, and shellcode.
Keywords: Intrusion Detection, Feature Selection, machine learning, UNSW NB15.
Scope of the Article: Internet of Things (IoT)