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Submitted URL: https://doi.org/10.1109/ICAECC54045.2022.9716648
Effective URL: https://ieeexplore.ieee.org/document/9716648/
Submission: On May 02 via manual from SG — Scanned from DE
Effective URL: https://ieeexplore.ieee.org/document/9716648/
Submission: On May 02 via manual from SG — Scanned from DE
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Machine Learning Based Intrusion Detection | IEEE Conference Publication | IEEE
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Conferences >2022 IEEE Fourth Internationa...
MACHINE LEARNING BASED INTRUSION DETECTION
Publisher: IEEE
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Shivam Kejriwal; Devika Patadia; Saloni Dagli; Prachi Tawde
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Abstract
Document Sections
* I.
Introduction
* II.
Related Works
* III.
Datasets and Pre-Processing
* IV.
Methodology
* V.
Experimental Results
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Abstract:Intrusion refers to any malicious activity done in order to access
confidential data. An intrusion detection system (IDS) detects these attacks
and, on detection, it repo...View more
Metadata
Abstract:
Intrusion refers to any malicious activity done in order to access confidential
data. An intrusion detection system (IDS) detects these attacks and, on
detection, it reports them to the administrator. It does so either by comparing
the new activity with the past activities or by analyzing the network
performance. This system forms a part of the vast security module and works with
several other such sub-modules in order to make sure that these unwanted
intrusions do not go unreported. The system that has been implemented in this
paper is an anomaly-based Intrusion Detection System (IDS). The primary purpose
of this implementation is to develop an efficient system in order to detect any
external or internal unauthenticated activity. Several models have been
experimented with in order to find one that suits the system the best and gives
a good enough accuracy. The models that have been experimented with include
Logistic Regressor, Random Forest Classifier, K Nearest Neighbor classifier,
XGBoost Classifier, Gaussian Naive Bayes Classifier and a Multi-Layer Perceptron
Classifier (MLP). Further, the accuracy of each of these models was calculated,
and a comparative analysis was done between the performance of these models. The
model that performed the best in this particular use case was the Random Forest
Classifier giving an accuracy of 99.8% and a macro average F1-Score of 0.98.
Published in: 2022 IEEE Fourth International Conference on Advances in
Electronics, Computers and Communications (ICAECC)
Date of Conference: 10-11 January 2022
Date Added to IEEE Xplore: 23 February 2022
ISBN Information:
ISSN Information:
DOI: 10.1109/ICAECC54045.2022.9716648
Publisher: IEEE
Conference Location: Bengaluru, India
Contents
--------------------------------------------------------------------------------
I. INTRODUCTION
With the proliferation in the field of information technology comes the increase
in the dependency of large-scale organizations on technology. This calls for a
need to develop efficient methodologies in order to provide security to these
companies to make sure that the information, as well as the data of these
companies, is protected from intrusions. An intrusion refers to any activity
that is not authorized and authenticated by the company. Intrusion is not only
limited to the access of company resources by an external source but also
includes unauthenticated access to information within the company itself.
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