Visible to the public Biblio

Filters: Keyword is back-end database  [Clear All Filters]
2018-06-07
Uwagbole, S. O., Buchanan, W. J., Fan, L..  2017.  An applied pattern-driven corpus to predictive analytics in mitigating SQL injection attack. 2017 Seventh International Conference on Emerging Security Technologies (EST). :12–17.

Emerging computing relies heavily on secure backend storage for the massive size of big data originating from the Internet of Things (IoT) smart devices to the Cloud-hosted web applications. Structured Query Language (SQL) Injection Attack (SQLIA) remains an intruder's exploit of choice to pilfer confidential data from the back-end database with damaging ramifications. The existing approaches were all before the new emerging computing in the context of the Internet big data mining and as such will lack the ability to cope with new signatures concealed in a large volume of web requests over time. Also, these existing approaches were strings lookup approaches aimed at on-premise application domain boundary, not applicable to roaming Cloud-hosted services' edge Software-Defined Network (SDN) to application endpoints with large web request hits. Using a Machine Learning (ML) approach provides scalable big data mining for SQLIA detection and prevention. Unfortunately, the absence of corpus to train a classifier is an issue well known in SQLIA research in applying Artificial Intelligence (AI) techniques. This paper presents an application context pattern-driven corpus to train a supervised learning model. The model is trained with ML algorithms of Two-Class Support Vector Machine (TC SVM) and Two-Class Logistic Regression (TC LR) implemented on Microsoft Azure Machine Learning (MAML) studio to mitigate SQLIA. This scheme presented here, then forms the subject of the empirical evaluation in Receiver Operating Characteristic (ROC) curve.

2017-12-20
Maleki, H., Rahaeimehr, R., Jin, C., Dijk, M. van.  2017.  New clone-detection approach for RFID-based supply chains. 2017 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :122–127.

Radio-Frequency Identification (RFID) tags have been widely used as a low-cost wireless method for detection of counterfeit product injection in supply chains. In order to adequately perform authentication, current RFID monitoring schemes need to either have a persistent online connection between supply chain partners and the back-end database or have a local database on each partner site. A persistent online connection is not guaranteed and local databases on each partner site impose extra cost and security issues. We solve this problem by introducing a new scheme in which a small Non-Volatile Memory (NVM) embedded in RFID tag is used to function as a tiny “encoded local database”. In addition our scheme resists “tag tracing” so that each partner's operation remains private. Our scheme can be implemented in less than 1200 gates satisfying current RFID technology requirements.