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Although the inhibitory roles of ursolic acid (UA) have been established in various tumors, its effects on the stemness of non-small cell lung cancer (NSCLC) cells are still unclear. Here, we constructed NSCLC cells with paclitaxel resistance (A549-PR) and showed that A549-PR exhibited a remarkably stronger stemness than the parental A549 cells, which is evident by the increase of spheroid formation capacity, stemness marker expression, and ALDH1 activity. Additionally, UA significantly reduced the stemness and paclitaxel resistance of A549-PR cells. Mechanistic investigations revealed that UA inhibited the miR-149-5p/MyD88 signaling, which is responsible for UA-mediated effects on the stemness of A549-PR cells. Notably, miR-149-5p/MyD88 axis promoted the stemness of A549 cells, while inhibition of this axis attenuated the stemness of A549-PR cells. Therefore, these results suggest that UA could attenuate the stemness and chemoresistance of NSCLC cells through targeting miR-149-5p/MyD88 axis.

The promise of strong physical unclonable functions (PUF) is to utilize the manufacturing variations of circuit elements to produce an independent and unpredictable response to any input challenge vector. Attacks on PUFs that predict the responses to input challenge vectors offer an interesting research problem. An attacking approach based on the optimization theory and side-channel information is proposed where we estimate the manufacturing variations of the circuit elements and predict the PUF’s responses to challenge vectors whose actual responses are not known. We apply this attacking approach on some popular PUF designs, including the Arbiter PUFs, the Memristor Crossbar PUFs, and the XOR Arbiter PUFs. Simulations show a substantial reduction in attack complexity compared with previously proposed machine-learning (ML)-based attacks: we achieve an average reduction of 66% in attack time compared with the ML approach. Despite some overhead, our approach is also applicable when the PUF responses are noisy.

To combat security threats to integrated circuit (IC) fabrication outsourcing, 2.5D integration can obfuscate the outsourced design by lifting a portion of the wires into an interposer layer. A security-aware physical design flow for 2.5D IC technology can then help defend against attacks by increasing the layout and functionality obfuscation.

Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits (ICs), also known as hardware Trojans (HTs), has attracted attention in academia as well as industry. To reduce the risks associated with Trojans, researchers have proposed different approaches to detect them. Among these approaches, test-time detection approaches have drawn the greatest attention. Many test-time approaches assume the existence of a Trojan-free (TF) chip/model also known as “golden model.” Prior works suggest using reverse engineering (RE) to identify such TF ICs for the golden model. However, they did not state how to do this efficiently. In fact, RE is a very costly process which consumes lots of time and intensive manual effort. It is also very error prone. In this paper, we propose an innovative and robust RE scheme to identify the TF ICs. We reformulate the Trojan-detection problem as clustering problem. We then adapt a widely used machine learning method, ${K}$ -means clustering, to solve our problem. Simulation results using state-of-the-art tools on several publicly available circuits show that the proposed approach can detect HTs with high accuracy rate. A comparison of this approach with our previously proposed approach [1] is also conducted. Both the limitations and application scenarios of the two methods are discussed in detail.

The hardware Trojan threat has motivated development of Trojan detection schemes at all stages of the integrated circuit (IC) lifecycle. While the majority of existing schemes focus on ICs at test-time, there are many unique advantages offered by post-deployment/run-time Trojan detection. However, run-time approaches have been underutilized with prior work highlighting the challenges of implementing them with limited hardware resources. In this paper, we propose three innovative low-overhead approaches for run-time Trojan detection which exploit the thermal sensors already available in many modern systems to detect deviations in power/thermal profiles caused by Trojan activation. The first one is a local sensor-based approach that uses information from thermal sensors together with hypothesis testing to make a decision. The second one is a global approach that exploits correlation between sensors and maintains track of the ICs thermal profile using a Kalman filter (KF). The third approach incorporates leakage power into the system dynamic model and apply extended KF (EKF) to track ICs thermal profile. Simulation results using state-of-the-art tools on ten publicly available Trojan benchmarks verify that all three proposed approaches can detect active Trojans quickly and with few false positives. Among three approaches, EKF is flawless in terms of the ten benchmarks tested but would require the most overhead.