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Performance Comparison of Anomaly Detection Algorithms

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Inventive Communication and Computational Technologies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 311))

Abstract

In several studies, machine learning techniques that are repossessed in intrusion detection systems wangle wide-ranging acknowledgment by changing into a high-yielding domain and continues to be the main target of the researcher's vast significance. After several years of study, the intrusion detection community still faces difficult issues. During the process of detecting unexpected new attacks, reducing the high rate of false alarms remains an unanswered problem. Identification of anomalies is a key problem in malware detection in which the existence of planned or unintentional caused assaults, defects elsewhere is demonstrated by disturbances of normal conduct. This paper offers a top-level view of analysis directions for the utilization of tagged and untagged information to handle the difficulty of the identification of anomalies. By performance comparison of the available semi-supervised and unsupervised algorithms, you can select the best anomaly detection algorithm. The documented references would cowl the most theoretical issues, leading the researcher in new ways for study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Maya Manish Kumar & G. R. Ramya

Authors
  1. Maya Manish Kumar
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  2. G. R. Ramya
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Correspondence to Maya Manish Kumar .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

    G. Ranganathan

  2. Department of Electrical and Computer Engineering, Ryerson Communications Lab, Toronto, ON, Canada

    Xavier Fernando

  3. Rutgers University, New Brunswick, NJ, USA

    Fuqian Shi

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Manish Kumar, M., Ramya, G.R. (2022). Performance Comparison of Anomaly Detection Algorithms. In: Ranganathan, G., Fernando, X., Shi, F. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 311. Springer, Singapore. https://doi.org/10.1007/978-981-16-5529-6_58

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  • DOI: https://doi.org/10.1007/978-981-16-5529-6_58

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5528-9

  • Online ISBN: 978-981-16-5529-6

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