SYNTERAResearch Group

Research / SYNTERA Connect

Internet of Things

  • Edge AI
  • Sensor networks
  • IoT security
  • Energy-efficient devices

Why it matters

Billions of small connected devices sense and act in the real world. They need to be secure, lightweight and intelligent at the edge.

Key challenges

  • Securing resource-constrained devices
  • Intrusion detection on the edge
  • Data quality across protocol stacks
  • Trust, privacy and reliability of connected systems

Our approach

Cryptography and cybersecurity, adversarially robust machine learning, network protocols and reliability engineering for IoT and IoMT.

Publications

Selected publications

  1. Journal2026

    An IoT-aware certificateless signature scheme for protection against type-I and type-II super adversaries

    Dadkhah, P.; Rastegari, P.; Dakhilalian, M.; Yeoh, P.; Wang, M.; Saremi, S.; … Gupta, B. B.

    IoT, 7(2)

    DOI 10.3390/iot7020040
  2. Journal2026

    Cryptanalysis and improvement of the SMEP-IoV protocol: A secure and lightweight protocol for message exchange in IoV paradigm

    Ghadim, G. O.; Rastegari, P.; Dakhilalian, M.; Hendessi, F.; Saremi, S.; Shibl, R.; … Nguyen, T. T.

    IoT, 7(2), 31

    DOI 10.3390/iot7020031
  3. Preprint2026

    Intrusion detection for the Internet of Medical Things: A cost-aware comparative benchmark of machine learning approaches

    Ahmed, H.; Dermody, G.; Mateen, A.; Saremi, S.; Shibl, R.

    Under review

  4. Journal2026

    Latent-space feature squeezing for adversarially robust intrusion detection on IoT edge devices

    Mirzaei, M.; Saremi, S.; Khan, A.; Ahmed, H.; Rastegari, P.; Nooraei Abadeh, M.; Shibl, R.; …

    International Journal of Intelligent Computing and Cybernetics, 19(4)

  5. Journal2026

    Protecting HWSNs from super adversaries with robust certificateless signcryption

    Dadkhah, P.; Rastegari, P.; Dakhilalian, M.; Yeoh, P.; Wang, M.; Saremi, S.; Gupta, B. B.

    Telecom, 7(2), 37

    DOI 10.3390/telecom7020037
  6. Journal2026

    SELM-CTR: A stacking ensemble deep learning model with SHAP-based analysis for large-scale click-through rate prediction

    Ali, Z.; Ahmed, H.; Khan, A.; Saremi, S.; Shibl, R.; Mirzaei, M.; Rastegari, P.; Wang, M.

    International Journal of Intelligent Computing and Cybernetics, 19(3)

    DOI 10.1108/ijicc-04-2026-0403

All SYNTERA Connect papers →

Team

Who works on this