Smart manufacturing is expanding at an increasing pace due to the recent advances in information and communication technologies. While the number of Industrial Internet of Things (IIoT) devices is increasing exponentially, complex and sophisticated attacks continue to be a big challenge as connectivity of IIoT devices with the Internet will significantly increase attack vectors for cyber attackers. Traditional security approaches will no longer suffice for securing smart factories. Instead, we need scalable, multi-layered and fully integrated strategic security approaches to protect manufacturing facilities against cyber threats. Moreover, how to securely exchange and collaboratively analyse data across smart manufacturing supply chains while ensuring data integrity, accountability, and traceability – and hence realise the vision of collaborative manufacturing – are still challenges that have not been resolved. 

In this project, to address the above challenges, I will first develop a secure data exchange environment for connected IIoT devices by leveraging Intel SGX technology. Particularly, Intel SGX will help to protect code and data from disclosure or modification to cyber attackers in untrusted environment. Next, I will design a cyber attack detection technique by exploiting the physical property of IIoT device. Specifically, I will design an anomaly detection technique leveraging a unique electronic fingerprint that each device creates via its physical characteristics. Next, I will integrate machine learning technique to automate the anomaly detection procedure for defending against complex and sophisticated attacks. Finally, I will design a fundamental tool for identification of cyber attacks in smart manufacturing. The salient feature of my project is secure data exchange environment, machine learning based anomaly detection and cyber attack identification as well as introducing related security- and privacy-aware defenses in the smart manufacturing ecosystem. 

Publications

• S. Halder, A. Ghosal, T. Newe and S. K. Das, "SmartLens: Robust Detection of Rogue Device via Frequency Domain Features in LoRa-Enabled IIoT," In Proc. of 11th Int'l Conf. on Communications and Network Security (IEEE CNS), pp. 1-9, Oct 2023. 

• 
  

• S. Halder and T. Newe, "Radio Fingerprinting for Anomaly Detection Using Federated Learning in LoRa Enabled IIoT," In Future Generation Computer Systems, Elsevier, 143, pp. 322-336, 2023. 

• S. Halder and T. Newe, "Robust Anomaly Detection via Radio Fingerprinting in LoRa-Enabled IIoT," In Proc. of 17th International Conference on Information Security Practice and Experience (ISPEC), LNCS, vol. 13620, pp. 161-178, Nov 23-25, 2022.

• S. Halder and T. Newe, "Enabling Secure Time Series Data Sharing via Homomorphic Encryption in Cloud-Assisted IIoT," In Future Generation Computer Systems, vol. 133, pp. 351-363,  2022. 

• S. Halder and T. Newe, "Secure Time Series Data Sharing with Fine-Grained Access Control in Cloud-Enabled IIoT," In Proc. of 18th IIEEE/IFIP Network Operations and Management Symposium (IEEE/IFIP NOMS), pp. 1-9, Apr 25-29, 2022.

• S. Halder and T. Newe, "SmartCrypt: Secure Storing and Sharing of Time Series Data Streams in IIoT," In Proc. of 19th IEEE Consumer Communications and Networking Conference (IEEE CCNC), pp. 248-251, Jan 8-11, 2022.

My earlier publications related to MSCA project:

• S. Halder and M. Conti, "CrypSH: A Novel IoT Data Protection Scheme Based on BGN Cryptosystem," In IEEE Transactions on Cloud Computing, 10(4), pp. 2437-2450, 2022.

• S. Halder, M. Conti and S. K. Das, "A Holistic Approach to Power Efficiency in a Clock Offset Based Intrusion Detection Systems for Controller Area Networks," In Pervasive and Mobile Computing, Elsevier, 73, pp. 1-20, article no. 101385, 2021.

• S. Halder, M. Conti and S. K. Das, "COIDS: A Clock Offset Based Intrusion Detection System for Controller Area Networks," Proc. of 21st ACM International Conference on Distributed Computing and Networking (ACM ICDCN), pp. 1-10, Jan 2020

• S. Halder and M. Conti, “Don't Hesitate to Share! A Novel IoT Data Protection Scheme Based on BGN Cryptosystem,” Proc. of 34th ACM Symposium On Applied Computing (ACM SAC), pp. 282-289, Apr 2019.