Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security
Perimeter intrusion detection systems (PIDS) play a crucial role in safeguarding critical infrastructures from unauthorized access and potential security breaches. Security is the main concern everywhere in the world. There are already many PIDS available, but the PID systems are still lacking in te...
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Institute of Electrical and Electronics Engineers Inc.
2023
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oai:scholars.utp.edu.my:375672023-10-13T13:00:04Z http://scholars.utp.edu.my/id/eprint/37567/ Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security Pitafi, S. Anwar, T. Dewa Made Widia, I. Yimwadsana, B. Perimeter intrusion detection systems (PIDS) play a crucial role in safeguarding critical infrastructures from unauthorized access and potential security breaches. Security is the main concern everywhere in the world. There are already many PIDS available, but the PID systems are still lacking in terms of probability of detection, false intrusion, and the activity recognition of intrusion. To solve the above problem, we designed a prototype for PIDS using a DHT22 temperature and humidity sensor, vibration sensor SW- 420 Module Pinout, Mini PIR motion sensor, and Arduino UNO. After collecting the data from above mentioned sensors we applied machine learning algorithms DBSCAN to cluster the data points and K-NN classification to classify those clusters in one-dimensional data, but the results were not much satisfying. From there we got the motivation to improve the algorithm and applied it to two-dimensional data. The existing DBSCAN is not efficient due to its high complexity and the varying densities. To overcome these issues in this algorithm, we have improved the existing DBSCAN to ST-DBSCAN where we have used the estimation for the epsilon value and used the Manatton distance formula to find out the distance between points which produces 94.9853 accuracy on our dataset. Another contribution of the proposed work is that we have developed our own dataset named STPID-dataset, captured from security cameras installed in various locations which can be used by future researchers. Author Institute of Electrical and Electronics Engineers Inc. 2023 Article NonPeerReviewed Pitafi, S. and Anwar, T. and Dewa Made Widia, I. and Yimwadsana, B. (2023) Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security. IEEE Access. p. 1. ISSN 21693536 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172998631&doi=10.1109%2fACCESS.2023.3318600&partnerID=40&md5=7056f716406ad4be90918a89bc9302ad 10.1109/ACCESS.2023.3318600 10.1109/ACCESS.2023.3318600 10.1109/ACCESS.2023.3318600 |
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Perimeter intrusion detection systems (PIDS) play a crucial role in safeguarding critical infrastructures from unauthorized access and potential security breaches. Security is the main concern everywhere in the world. There are already many PIDS available, but the PID systems are still lacking in terms of probability of detection, false intrusion, and the activity recognition of intrusion. To solve the above problem, we designed a prototype for PIDS using a DHT22 temperature and humidity sensor, vibration sensor SW- 420 Module Pinout, Mini PIR motion sensor, and Arduino UNO. After collecting the data from above mentioned sensors we applied machine learning algorithms DBSCAN to cluster the data points and K-NN classification to classify those clusters in one-dimensional data, but the results were not much satisfying. From there we got the motivation to improve the algorithm and applied it to two-dimensional data. The existing DBSCAN is not efficient due to its high complexity and the varying densities. To overcome these issues in this algorithm, we have improved the existing DBSCAN to ST-DBSCAN where we have used the estimation for the epsilon value and used the Manatton distance formula to find out the distance between points which produces 94.9853 accuracy on our dataset. Another contribution of the proposed work is that we have developed our own dataset named STPID-dataset, captured from security cameras installed in various locations which can be used by future researchers. Author |
| format |
Article |
| author |
Pitafi, S. Anwar, T. Dewa Made Widia, I. Yimwadsana, B. |
| spellingShingle |
Pitafi, S. Anwar, T. Dewa Made Widia, I. Yimwadsana, B. Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security |
| author_facet |
Pitafi, S. Anwar, T. Dewa Made Widia, I. Yimwadsana, B. |
| author_sort |
Pitafi, S. |
| title |
Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security |
| title_short |
Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security |
| title_full |
Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security |
| title_fullStr |
Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security |
| title_full_unstemmed |
Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security |
| title_sort |
revolutionizing perimeter intrusion detection: a machine learning-driven approach with curated dataset generation for enhanced security |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| url |
http://scholars.utp.edu.my/id/eprint/37567/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172998631&doi=10.1109%2fACCESS.2023.3318600&partnerID=40&md5=7056f716406ad4be90918a89bc9302ad |
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