STPID-Model : A novel approach to Perimeter Intrusion Detection

• Main Authors: Pitafi, S., Anwar, T., Sharif, Z., Hina, H.
• Format: Conference or Workshop Item
• Published: 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/37726/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174147314&doi=10.1109%2fCITA58204.2023.10262715&partnerID=40&md5=3e0249d3487ebb07cfe6a4cc135031b3

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. © 2023 IEEE.