Ramyapandian Vijayakanthan
Irfan Ahmed
ABSTRACT
As the need and adaptation for smart environments continue to rise, owing mainly to the evolution in IoT technology's processing and sensing capabilities, the security community must contend with increasing attack surfaces on our network, critical systems, and infrastructures. Thus, developing an effective fingerprint to deal with some of these threats is of paramount importance. As such, in this paper, we explored the use of memory snapshots for effective dynamic process-level fingerprints. Our technique transforms a memory snapshot into a sound wave signal, from which we then retrieve their distinctive Mel-Frequency Cepstral Coefficients (MFCC) features as unique process-level identifiers. The evaluation of this proposed technique on our dataset demonstrated that MFCC-based fingerprints generated from the same IoT process memory at different times exhibit much stronger similarities than those acquired from different IoT process spaces.
Supplemental Material
- Long Cheng, Ke Tian, Daphne Yao, Lui Sha, and Raheem A Beyah. 2019. Checking is believing: Event-aware program anomaly detection in cyber-physical systems. IEEE Transactions on Dependable and Secure Computing (2019).Google Scholar
- V Rama Krishna and R Subhashini. 2020. Mimicking attack by botnet and detection at gateway. Peer-to-Peer Networking and Applications (2020), 1--11.Google Scholar
- Poonam Yadav, Angelo Feraudo, Budi Arief, Siamak F Shahandashti, and Vassilios G Vassilakis. 2020. Position paper: A systematic framework for categorising IoT device fingerprinting mechanisms. In Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things. 62--68.Google ScholarDigital Library
- Michal Zalewski. 2003. Memfetch. https://github.com/citypw/lcamtuf-memfetchGoogle Scholar
Index Terms
- Transforming Memory Image to Sound Wave Signals for an Effective IoT Fingerprinting
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