| Title | Detection of Malware in UHF RFID User Memory Bank using Random Forest Classifier on Signal Strength Data in the Frequency Domain |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Hasnaeen, Shah Md Nehal, Chrysler, Andrew |
| Conference Name | 2022 IEEE International Conference on RFID (RFID) |
| Date Published | may |
| Keywords | codes, computer viruses, Conferences, Data models, feature extraction, Forestry, frequency-domain analysis, human factors, Malware, maxima detection, pubcrawl, Random Forest, resilience, Resiliency, RFID tags, RFIDs, signal strength |
| Abstract | A method of detecting UHF RFID tags with SQL in-jection virus code written in its user memory bank is explored. A spectrum analyzer took signal strength readings in the frequency spectrum while an RFID reader was reading the tag. The strength of the signal transmitted by the RFID tag in the UHF range, more specifically within the 902-908 MHz sub-band, was used as data to train a Random Forest model for Malware detection. Feature reduction is accomplished by dividing the observed spectrum into 15 ranges with a bandwidth of 344 kHz each and detecting the number of maxima in each range. The malware-infested tag could be detected more than 80% of the time. The frequency ranges contributing most in this detection method were the low (903.451-903.795 MHz, 902.418-902.762 MHz) and high (907.238-907.582 MHz) bands in the observed spectrum. |
| Notes | ISSN: 2573-7635 |
| DOI | 10.1109/RFID54732.2022.9795967 |
| Citation Key | hasnaeen_detection_2022 |
Detection of Malware in UHF RFID User Memory Bank using Random Forest Classifier on Signal Strength Data in the Frequency Domain