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Pang, Y., Xue, X., Namin, A. S..  2016.  Early Identification of Vulnerable Software Components via Ensemble Learning. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). :476–481.

Software components, which are vulnerable to being exploited, need to be identified and patched. Employing any prevention techniques designed for the purpose of detecting vulnerable software components in early stages can reduce the expenses associated with the software testing process significantly and thus help building a more reliable and robust software system. Although previous studies have demonstrated the effectiveness of adapting prediction techniques in vulnerability detection, the feasibility of those techniques is limited mainly because of insufficient training data sets. This paper proposes a prediction technique targeting at early identification of potentially vulnerable software components. In the proposed scheme, the potentially vulnerable components are viewed as mislabeled data that may contain true but not yet observed vulnerabilities. The proposed hybrid technique combines the supports vector machine algorithm and ensemble learning strategy to better identify potential vulnerable components. The proposed vulnerability detection scheme is evaluated using some Java Android applications. The results demonstrated that the proposed hybrid technique could identify potentially vulnerable classes with high precision and relatively acceptable accuracy and recall.

H
Liu, R., Wu, H., Pang, Y., Qian, H., Yu, S..  2016.  A highly reliable and tamper-resistant RRAM PUF: Design and experimental validation. 2016 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :13–18.

This work presents a highly reliable and tamper-resistant design of Physical Unclonable Function (PUF) exploiting Resistive Random Access Memory (RRAM). The RRAM PUF properties such as uniqueness and reliability are experimentally measured on 1 kb HfO2 based RRAM arrays. Firstly, our experimental results show that selection of the split reference and offset of the split sense amplifier (S/A) significantly affect the uniqueness. More dummy cells are able to generate a more accurate split reference, and relaxing transistor's sizes of the split S/A can reduce the offset, thus achieving better uniqueness. The average inter-Hamming distance (HD) of 40 RRAM PUF instances is 42%. Secondly, we propose using the sum of the read-out currents of multiple RRAM cells for generating one response bit, which statistically minimizes the risk of early retention failure of a single cell. The measurement results show that with 8 cells per bit, 0% intra-HD can maintain more than 50 hours at 150 °C or equivalently 10 years at 69 °C by 1/kT extrapolation. Finally, we propose a layout obfuscation scheme where all the S/A are randomly embedded into the RRAM array to improve the RRAM PUF's resistance against invasive tampering. The RRAM cells are uniformly placed between M4 and M5 across the array. If the adversary attempts to invasively probe the output of the S/A, he has to remove the top-level interconnect and destroy the RRAM cells between the interconnect layers. Therefore, the RRAM PUF has the “self-destructive” feature. The hardware overhead of the proposed design strategies is benchmarked in 64 × 128 RRAM PUF array at 65 nm, while these proposed optimization strategies increase latency, energy and area over a naive implementation, they significantly improve the performance and security.