Intrusion Detection using Machine Learning and Deep Learning
Venkata Ramani Varanasi1, Shaik Razia2
1Venkata Ramani Varanasi*, Department of Computer Science and Engineering , Research Scholar, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
2Dr. Shaik Razia, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9704-9719 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9999118419/2019©BEIESP | DOI: 10.35940/ijrte.D9999.118419
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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: With the increase in usage of networking technology and the Internet, Intrusion detection becomes important and challenging security problem. A number of techniques came into existence to detect the intrusions on the basis of machine learning and deep learning procedures. This paper will give inspiration to the use of ML and DL systems to IP traffic and gives a concise depiction of every one of the ML and DL strategies. This paper gives an audit of 40 noteworthy works that covers the period from 2015 to 2019. ML and DL methods are compared with regard to their accuracy and detection potential to detect different types of intrusions. Future Research includes ML and DL methods to find the intrusions so as to improve the detection rate, accuracy and to minimize the false positive rate.
Keywords: Machine learning, Deep Learning, intrusions, attacks, network security, datasets, metrics.
Scope of the Article: Deep Learning.