Network analysis of intrusion detection based on Machine Learning and Deep Learning
Anurag Busha1, Vakeesh Kanna2, Sagar Naidu3, Sathya R4
1Anurag Busha, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
2Vakeesh Kanna, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
2Sagar Naidu, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
3Sathya R, Department of Assistant Professor, Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1458-1463 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6290048419/19©BEIESP
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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: The modern world is adopted to Machine learning and it shows superiority on almost all the conventional rule-based algorithms. These strategies are being combined in cyber security systems with the goal of supporting and even perhaps substituting the primary leve of security analysis. Although the entire automation detection and analysis is an an ideal goal, the effectiveness of machine learning in cyber security should be evaluated with due dilligence. We provide an analysis addressed to specialists of machine learning techniques applied to the detection of intrusion, malware and spam, The goal is twofold: to asses the present maturity of the solutions and to spot their main restrictions that stall a direct adoption of machine learning cyber detection schemes. Our conclusions are supported by an intensive review of the literature which include experiments performed on real enterprise systems and network traffic.
Keywords: Deep Learning, Machine Learning, Adversarial Learning, Cyber Security
Scope of the Article: Deep Learning