Performance Assessment of Ml Techniques for Detecting Intrusions in the Network
Katikela Haritha2, CH. Mallikarjuna Rao2

1Katikela Haritha, PG Scholar, M.Tech, Department of Computer Science and Engineering, GRIET , Affiliated to JNTUH, Hyderabad, India.
2Dr. CH. Mallikarjuna Rao, Professor of CSE, GRIET, Affiliated to JNTUH, Hyderabad, India.

Manuscript received on September 17, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4095-4100 | Volume-8 Issue-12, October 2019. | Retrieval Number: L36401081219/2019©BEIESP | DOI: 10.35940/ijitee.L3640.1081219
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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: It has become crucial for the organizations, military and personal computer users to make the network security. Day by day, security has become a major issue with the increase of internet usage. The improvement in the security technology can be much understood from the security history. Network security is an immense field and it is in development stage. An immense amount of data is being generated every second due to technological advancement and reforms. Social networking and cloud computing are generating a huge amount of data every second. Every minute data is being captured in the computing world from the click of the mouse to video people tend to watch generating an immediate recommendation. Everything a user is doing on the internet is being captured in different ways for multiple intents. Now it all ends up monitoring the system and network and, securing lines and servers. This mechanism is called Intrusion Detection System(IDS). Hacker uses multiple numbers of ways to attack the system which can be detected through a number of algorithm and techniques. A comprehensive survey of some major techniques of machine learning implemented for detecting intrusions. Classification techniques are SVM, Random Forest algorithm, Extreme learning machine, and Decision Tree. NSL-KDD is the dataset used to get the higher rate of detection. The Result Analysis shows that, in terms of accuracy, this paper accomplishes better results when compared to any other related methods.
Keywords: Detection Rate, Decision Tree, ELM, Machine Learning (ML), NSL_KDD Dataset, Random Forest, Support Vector Machine.
Scope of the Article: Machine Learning