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Advances in Distributed Computing and Machine Learning pp 207–218Cite as

Performance Analysis of Network Anomaly Detection Systems in Consumer Networks

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 127))

Abstract

There has been a significant increase in the amount of smart home appliances such as intelligent thermostats, Internet-connected baby monitors, lights being used; these devices have been a target for a number of cyber-attacks till date. Using only fundamental network information such as source and destination ports, packet sizes, TCP flags, time between subsequent packets, necessary features can be extracted to detect the aforementioned anomalies. This paper aims to analyze some critical operating system performance metrics on detecting such anomalies. It also consists of the taxonomy of various approaches to classify anomalies and detailed description on capturing and cleaning network packets.

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Authors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India

    P. Darsh & R. Rahul

Authors
  1. P. Darsh
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  2. R. Rahul
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Corresponding author

Correspondence to P. Darsh .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Asis Kumar Tripathy

  2. San Diego State University, San Diego, CA, USA

    Mahasweta Sarkar

  3. Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Suchismita Chinara

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Darsh, P., Rahul, R. (2021). Performance Analysis of Network Anomaly Detection Systems in Consumer Networks. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_21

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