Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup

In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more appreciable attack results. Most of them concentrate on improving network architectures or putting forward novel algorithms, assuming that there are adequate profiling traces available to train an appropriate neural network. However, in practical scenarios, profiling traces are probably insufficient, which makes the network learn deficiently and compromises attack performance. In this paper, we investigate a kind of data augmentation technique, called mixup, and first propose to exploit it in deep-learning based side channel attacks, for the purpose of expanding the profiling set and facilitating the chances of mounting a successful attack. We perform Correlation Power Analysis for generated traces and original traces, and discover that there exists consistency between them regarding leakage information. Our experiments show that mixup is truly capable of enhancing attack performance especially for insufficient profiling traces. Specifically, when the size of the training set is decreased to 30% of the original set, mixup can significantly reduce acquired attacking traces. We test three mixup parameter values and conclude that generally all of them can bring about improvements. Besides, we compare three leakage models and unexpectedly find that least significant bit model, which is less frequently used in previous works, actually surpasses prevalent identity model and hamming weight model in terms of attack results

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces

Determination of chlorantraniliprole and penoxsulam residues in rice by QuEChERS-UPLC-MS/MS

Objective: To establish a new QuEChERS-ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the detection of chlorantraniliprole and penoxsulam residues in rice. Methods: After the sample was extracted with 0.2% formic acid-acetonitrile, purified with N-propylethylenediamine (PSA) and graphitized carbon black (GCB) packing, 0.2% formic acid water and acetonitrile were used as mobile phases for gradient elution, and then subjected to C18 chromatography. UPLC-MS/MS was used to column separation and determination. Results: The limits of quantification (LOQ) of chlorantraniliprole and penoxsulam were both 0.004 mg/kg, and the method detection limits (LOD) were both 0.001 mg/kg. Chlorantraniliprole and penoxsulam had a good linear relationship in the range of 0.002~0.5 mg/L, and their coefficient was greater than 0.993. At the addition levels of 0.05, 0.1, and 0.5 mg/kg, the average recovery rates of chlorantraniliprole and penoxsulam were 86% to 110% with the relative standard deviations (RSD) of 1.5% to 6.1%. Conclusion: This method is efficient and simple, has good stability and high sensitivity, and is suitable for the detection of chlorantraniliprole and penoxsulam in rice

Novel Y-chromosomal microdeletions associated with non-obstructive azoospermia uncovered by high throughput sequencing of sequence-tagged sites (STSs)

Y-chromosomal microdeletion (YCM) serves as an important genetic factor in non-obstructive azoospermia (NOA). Multiplex polymerase chain reaction (PCR) is routinely used to detect YCMs by tracing sequence-tagged sites (STSs) in the Y chromosome. Here we introduce a novel methodology in which we sequence 1,787 (post-filtering) STSs distributed across the entire male-specific Y chromosome (MSY) in parallel to uncover known and novel YCMs. We validated this approach with 766 Chinese men with NOA and 683 ethnically matched healthy individuals and detected 481 and 98 STSs that were deleted in the NOA and control group, representing a substantial portion of novel YCMs which significantly influenced the functions of spermatogenic genes. The NOA patients tended to carry more and rarer deletions that were enriched in nearby intragenic regions. Haplogroup O2* was revealed to be a protective lineage for NOA, in which the enrichment of b1/b3 deletion in haplogroup C was also observed. In summary, our work provides a new high-resolution portrait of deletions in the Y chromosome.National Key Scientific Program of China [2011CB944303]; National Nature Science Foundation of China [31271244, 31471344]; Promotion Program for Shenzhen Key Laboratory [CXB201104220045A]; Shenzhen Project of Science and Technology [JCYJ20130402113131202, JCYJ20140415162543017]SCI(E)[email protected]; [email protected]; [email protected]

High-grade tumor budding is a risk factor for survival in patients with laryngeal squamous cell carcinoma

Objective: With the increasing incidence and mortality of laryngeal squamous cell carcinoma worldwide, researchers continue to search for novel prognostic factors and treatment methods for preventing early laryngeal squamous cell carcinoma from becoming advanced laryngeal squamous cell carcinoma. This study aims to determine if tumor budding is an independent risk factor associated with the survival of patients with laryngeal squamous cell carcinoma. Methods: 268 cases of laryngeal squamous cell carcinoma were studied, and tumor budding was analyzed for associations with clinicopathological features and clinical outcomes. Results: Tumor budding was divided into low-grade tumor budding (0–6/0.785 mm2) and high-grade tumor budding (≥7/0.785 mm2) based on the results of the receiver operating characteristics curve analysis. Logistic regression analysis showed that smaller tumor cell nests, the low levels of tumor-infiltrating lymphocytes, and higher pathological T staging were the risk factors for high-grade tumor budding (p < 0.05). In the low-grade tumor budding group, there was no statistic difference in survival between patients without tumor budding and those with 1–6/0.785 mm2 tumor budding. Multivariate survival analysis showed high-grade tumor budding (p < 0.001) was independent prognostic factors for disease-free survival and overall survival in laryngeal squamous cell carcinoma. High-grade tumor budding was also an independent prognostic factor for disease-free survival (p = 0.037) and overall survival (p = 0.009) in T1-2N0 laryngeal squamous cell carcinoma. Conclusions: Smaller tumor cell nests, the low levels of tumor-infiltrating lymphocytes, and higher pathological T staging were closely associated with high-grade tumor budding in laryngeal squamous cell carcinoma. High-grade tumor budding may be an adverse risk factor that affects not only the disease-free survival and overall survival of laryngeal squamous cell carcinoma patients but also the survival of T1-2N0 laryngeal squamous cell carcinoma patients. Level of Evidence: Level 4