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Conference Paper
Zhang, F., Masna, N. V. R., Bhunia, S., Chen, C., Mandal, S..  2017.  Authentication and Traceability of Food Products through the Supply Chain Using NQR Spectroscopy. 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS). :1–4.

Maintaining the security and integrity of our food supply chain has emerged as a critical need. In this paper, we describe a novel authentication approach that can significantly improve the security of the food supply chain. It relies on applying nuclear quadrupole resonance (NQR) spectroscopy to authenticate the contents of packaged food products. NQR is a non-invasive, non-destructive, and quantitative radio frequency (RF) spectroscopic technique. It is sensitive to subtle features of the solid-state chemical environment such that signal properties are influenced by the manufacturing process, thus generating a manufacturer-specific watermark or intrinsic tag for the product. Such tags enable us to uniquely characterize and authenticate products of identical composition but from different manufacturers based on their NQR signal parameters. These intrinsic tags can be used to verify the integrity of a product and trace it through the supply chain. We apply a support vector machine (SVM)-based classification approach that trains the SVM with measured NQR parameters and then authenticates food products by checking their test responses. Measurement on an example substance using semi-custom hardware shows promising results (95% classification accuracy) which can be further improved with improved instrumentation.

Zhu, L., Chen, C., Su, Z., Chen, W., Li, T., Yu, Z..  2020.  BBS: Micro-Architecture Benchmarking Blockchain Systems through Machine Learning and Fuzzy Set. 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). :411–423.
Due to the decentralization, irreversibility, and traceability, blockchain has attracted significant attention and has been deployed in many critical industries such as banking and logistics. However, the micro-architecture characteristics of blockchain programs still remain unclear. What's worse, the large number of micro-architecture events make understanding the characteristics extremely difficult. We even lack a systematic approach to identify the important events to focus on. In this paper, we propose a novel benchmarking methodology dubbed BBS to characterize blockchain programs at micro-architecture level. The key is to leverage fuzzy set theory to identify important micro-architecture events after the significance of them is quantified by a machine learning based approach. The important events for single programs are employed to characterize the programs while the common important events for multiple programs form an importance vector which is used to measure the similarity between benchmarks. We leverage BBS to characterize seven and six benchmarks from Blockbench and Caliper, respectively. The results show that BBS can reveal interesting findings. Moreover, by leveraging the importance characterization results, we improve that the transaction throughput of Smallbank from Fabric by 70% while reduce the transaction latency by 55%. In addition, we find that three of seven and two of six benchmarks from Blockbench and Caliper are redundant, respectively.
Shen, N., Yeh, J., Chen, C., Chen, Y., Zhang, Y..  2019.  Ensuring Query Completeness in Outsourced Database Using Order-Preserving Encryption. 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom). :776–783.
Nowadays database outsourcing has become business owners' preferred option and they are benefiting from its flexibility, reliability, and low cost. However, because database service providers cannot always be fully trusted and data owners will no longer have a direct control over their own data, how to make the outsourced data secure becomes a hot research topic. From the data integrity protection aspect, the client wants to make sure the data returned is correct, complete, and up-to-date. Previous research work in literature put more efforts on data correctness, while data completeness is still a challenging problem to solve. There are some existing works that tried to protect the completeness of data. Unfortunately, these solutions were considered not fully solving the problem because of their high communication or computation overhead. The implementations and limitations of existing works will be further discussed in this paper. From the data confidentiality protection aspect, order-preserving encryption (OPE) is a widely used encryption scheme in protecting data confidentiality. It allows the client to perform range queries and some other operations such as GROUP BY and ORDER BY over the OPE encrypted data. Therefore, it is worthy to develop a solution that allows user to verify the query completeness for an OPE encrypted database so that both data confidentiality and completeness are both protected. Inspired by this motivation, we propose a new data completeness protecting scheme by inserting fake tuples into databases. Both the real and fake tuples are OPE encrypted and thus the cloud server cannot distinguish among them. While our new scheme is much more efficient than all existing approaches, the level of security protection remains the same.
Han, H., Wang, Q., Chen, C..  2019.  Policy Text Analysis Based on Text Mining and Fuzzy Cognitive Map. 2019 15th International Conference on Computational Intelligence and Security (CIS). :142—146.
With the introduction of computer methods, the amount of material and processing accuracy of policy text analysis have been greatly improved. In this paper, Text mining(TM) and latent semantic analysis(LSA) were used to collect policy documents and extract policy elements from them. Fuzzy association rule mining(FARM) technique and partial association test (PA) were used to discover the causal relationships and impact degrees between elements, and a fuzzy cognitive map (FCM) was developed to deduct the evolution of elements through a soft computing method. This non-interventionist approach avoids the validity defects caused by the subjective bias of researchers and provides policy makers with more objective policy suggestions from a neutral perspective. To illustrate the accuracy of this method, this study experimented by taking the state-owned capital layout adjustment related policies as an example, and proved that this method can effectively analyze policy text.
Nelson, Jennifer, Lin, X., Chen, C., Iglesias, J., Li, J. J..  2016.  Social Engineering for Security Attacks. Proceedings of the The 3rd Multidisciplinary International Social Networks Conference on SocialInformatics 2016, Data Science 2016. :6:1–6:4.

Social Engineering is a kind of advance persistent threat (APT) that gains private and sensitive information through social networks or other types of communication. The attackers can use social engineering to obtain access into social network accounts and stays there undetected for a long period of time. The purpose of the attack is to steal sensitive data and spread false information rather than to cause direct damage. Such targets can include Facebook accounts of government agencies, corporations, schools or high-profile users. We propose to use IDS, Intrusion Detection System, to battle such attacks. What the social engineering does is try to gain easy access, so that the attacks can be repeated and ongoing. The focus of this study is to find out how this type of attacks are carried out so that they can properly detected by IDS in future research.