@inproceedings{scholars19113,
         journal = {2023 13th International Conference on Information Technology in Asia, CITA 2023},
           title = {STPID-Model : A novel approach to Perimeter Intrusion Detection},
           pages = {7--12},
            note = {cited By 1; Conference of 13th International Conference on Information Technology in Asia, CITA 2023 ; Conference Date: 3 August 2023 Through 4 August 2023; Conference Code:193023},
            year = {2023},
             doi = {10.1109/CITA58204.2023.10262715},
          author = {Pitafi, S. and Anwar, T. and Sharif, Z. and Hina, H.},
        keywords = {Alarm systems; Automation; Computer crime; Fake detection; Image enhancement; Intelligent buildings; Intrusion detection; Machine learning; Network security, DBSCAN; Detection system; Intelligent detection; Intelligent detection system dataset; Intrusion-Detection; Machine-learning; Perimeter intrusion detection; Perimeter intrusion detection systems; Smart homes; Smart hospital, Internet of things},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174147314&doi=10.1109\%2fCITA58204.2023.10262715&partnerID=40&md5=3e0249d3487ebb07cfe6a4cc135031b3},
        abstract = {The internet of things (IoT) has been embedded in many aspects of our lives and is evolving in almost all sectors such as (smart homes, smart cities, smart hospitals, etc.). As security is the main concern everywhere in daily routine due to the increase in cases of crimes and vulnerabilities. Intrusion detection systems (IDS) are extremely important to make the specific location secure from unauthorized access but still the perimeter intrusion detection systems (PIDS) are facing issues in real intrusion detection and false alarm rate (FAR) that are caused by the environmental intrusion. In order to solve these problems in perimeter intrusion detection systems (PIDS). In this paper, we proposed a new machine learning-based model named STPID-Model where we used the Imagery library for intelligent detection systems (i-LIDS) dataset, we derived images from the recordings available in i-LIDS dataset and then we applied enhanced algorithm I-DBSCAN for intrusion detection and distinguish between fake and real intrusion. Our proposed model performed better than the others. {\^A}{\copyright} 2023 IEEE.}
}