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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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10262029/ |
| _version_ | 1827796607659147264 |
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| author | Shahneela Pitafi Toni Anwar I. Dewa Made Widia Boonsit Yimwadsana |
| author_facet | Shahneela Pitafi Toni Anwar I. Dewa Made Widia Boonsit Yimwadsana |
| author_sort | Shahneela Pitafi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T19:09:23Z |
| format | Article |
| id | doaj.art-a201f7c37b11437c8f3608fff1aac1d3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T19:09:23Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a201f7c37b11437c8f3608fff1aac1d32023-10-09T23:01:39ZengIEEEIEEE Access2169-35362023-01-011110695410696610.1109/ACCESS.2023.331860010262029Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach With Curated Dataset Generation for Enhanced SecurityShahneela Pitafi0https://orcid.org/0000-0003-3640-961XToni Anwar1I. Dewa Made Widia2Boonsit Yimwadsana3Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, MalaysiaComputer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, MalaysiaFaculty of Vocational Studies, Brawijaya University, Malang, East Java, IndonesiaComputer Science Academic Group, Faculty of Information and Communication Technology, Mahidol University, Salaya, ThailandPerimeter 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.https://ieeexplore.ieee.org/document/10262029/Intrusion detectionperimeter intrusion detection systemmachine learningDBSCANintrusion activities |
| spellingShingle | Shahneela Pitafi Toni Anwar I. Dewa Made Widia Boonsit Yimwadsana Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach With Curated Dataset Generation for Enhanced Security IEEE Access Intrusion detection perimeter intrusion detection system machine learning DBSCAN intrusion activities |
| title | 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_short | 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 |
| topic | Intrusion detection perimeter intrusion detection system machine learning DBSCAN intrusion activities |
| url | https://ieeexplore.ieee.org/document/10262029/ |
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