A novel machine learning model for perimeter intrusion detection using intrusion image dataset
Issued Date
2024-12-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-85212930120
Journal Title
PLoS ONE
Volume
19
Issue
12
Rights Holder(s)
SCOPUS
Bibliographic Citation
PLoS ONE Vol.19 No.12 (2024)
Suggested Citation
Pitafi S., Anwar T., Widia I.D.M., Sharif Z., Yimwadsana B. A novel machine learning model for perimeter intrusion detection using intrusion image dataset. PLoS ONE Vol.19 No.12 (2024). doi:10.1371/journal.pone.0313890 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/102571
Title
A novel machine learning model for perimeter intrusion detection using intrusion image dataset
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Author's Affiliation
Corresponding Author(s)
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Abstract
Perimeter Intrusion Detection Systems (PIDS) are crucial for protecting any physical locations by detecting and responding to intrusions around its perimeter. Despite the availability of several PIDS, challenges remain in detection accuracy and precise activity classification. To address these challenges, a new machine learning model is developed. This model utilizes the pre-trained InceptionV3 for feature extraction on PID intrusion image dataset, followed by t-SNE for dimensionality reduction and subsequent clustering. When handling high-dimensional data, the existing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm faces efficiency issues due to its complexity and varying densities. To overcome these limitations, this research enhances the traditional DBSCAN algorithm. In the enhanced DBSCAN, distances between minimal points are determined using an estimation for the epsilon values with the Manhattan distance formula. The effectiveness of the proposed model is evaluated by comparing it to state-of-the-art techniques found in the literature. The analysis reveals that the proposed model achieved a silhouette score of 0.86, while comparative techniques failed to produce similar results. This research contributes to societal security by improving location perimeter protection, and future researchers can utilize the developed model for human activity recognition from image datasets.