Reviewing a New Optimized an ANFIS-Based Framework for Detecting Intrusion Detection System with Machine Learning Algorithms (Deep Learning Algorithm)
Khushbu Rai1, Megha Kamble2
1Khushbu Rai, Department of Computer Science and Engineering, LNCT University, Bhopal (M.P), India.
2Dr. Megha Kamble, Department of Computer Science and Engineering, LNCT University, Bhopal (M.P), India.
Manuscript received on 14 November 2022 | Revised Manuscript received on 06 December 2022 | Manuscript Accepted on 15 December 2022 | Manuscript published on 28 February 2023 | PP: 35-42 | Volume-12 Issue-3, February 2023 | Retrieval Number: 100.1/ijeat.B39161212222 | DOI: 10.35940/ijeat.B3916.0212323
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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: Today’s world is becoming more interconnected due to the current global internet, communication, or ways of doing business that have recently shifted to cloud computing platforms to increase their speed and productivity. However, such systems can also be affected by cyberattacks on cloud infrastructure services executed on various cloud platforms, thereby increasing the number of attacks on these systems to mitigate any harm caused by a cyberattack on cloud-based infrastructure. Although network administrators utilise intrusion detection systems (IDS) to detect threats and anomalies, they frequently only make them available post-attack, ready to act in cyber warfare. If we could predict risky behavior, network administrators or security-enhancing software could intervene before harm was done. Incoming intrusion detection messages should be viewed as a sequence. The fundamental function of an intrusion detection system (IDS) is to distinguish between regular and abnormal network traffic. As a result, robust intrusion detection systems (IDS) using deep learning models are required to identify such cyber risks in the form of threats and anomalies on cloud-based infrastructure.
Keywords: Intrusion Detection Systems (IDS), Artificial Neural Networks (ANN), DDoS attacks, Crow Search Algorithm (CSA), ANFIS, Machine learning (ML), Deep Learning (DL).
Scope of the Article: Deep Learning