When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing

Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a fusion center. However, existing studies usually assume network or attackers as passive entities, e.g., assuming the prior knowledge of attacks is known or fixed. In practice, attackers can actively adopt arbitrary behaviors and avoid pre-assumed patterns or assumptions used by defense strategies. In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center. Based on the black-box nature of the fusion center in cooperative spectrum sensing, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center's decision model. We propose a generic algorithm to create malicious sensing data using this surrogate model. Our real-world experiments show that the LEB attack is effective to beat a wide range of existing defense strategies with an up to 82% of success ratio. Given the gap between the proposed LEB attack and existing defenses, we introduce a non-invasive method named as influence-limiting defense, which can coexist with existing defenses to defend against LEB attack or other similar attacks. We show that this defense is highly effective and reduces the overall disruption ratio of LEB attack by up to 80%

An Analysis of Aluminum Extrusion Industrial Cluster in Dali Town, Nanhai

中国はアルミ形材の生産が世界トップであるが，中国のなかでこの産業が最も集中しているのが広東省佛山市南海区大瀝鎮である．この町のアルミ加工業の源流は，中華民国期に勃興した非鉄金属リサイクル業にある．計画経済の時代にも「社隊企業」の名義を借りてアルミ加工業が発展し，1990 年代に至ってアルミ製窓枠などへの需要拡大の波に乗って大きく成長した．産業集積の発展の過程では，価格決定の混乱や過剰包装などで買い手からの信用を失う危機に直面したこともあったが，鎮政府と業界団体の介入によって問題を克服してきた．近年は鎮における土地の狭隘化，賃金上昇，環境規制の強化により，工場を広東省内の他の地域に移転する企業が増えている．しかし，こうした制約は産業のレベルアップを促す可能性がある．China produces the largest amount of aluminum extrusions in the world. Among many industrial clusters that specialize in aluminum extrusions, Dali Town of Nanhai District, Foshan, Guangdong Province is the largest. The aluminum processing industry of this town dates back to the Republican Era, when some local craftsmen engaged in recycling non-ferrous metals from wastes. Even during the Planned Economy, aluminum processing enterprises emerged under the disguise of “brigade and commune enterprises”.Since 1990s, local aluminum extrusion manufacturers flourished, because of the rise of domestic demand for aluminum-made building materials. During the course of development, Dali’s industrial cluster has experienced several crises, such as deterioration of reputation caused by opportunistic pricing and excessive packaging. The township government and industrial associations have made collective efforts to overcome such crises. In recent years, the industrial cluster faces the problem of scarcity of land, rise of wages, and stricter environmental restrictions. But such restrictions may induce the upgrading of the local industry.特集　中国沿海部の産業集

Reproducibility of an HPLC-ESI-MS/MS Method for the Measurement of Stable-Isotope Enrichment of in Vivo-Labeled Muscle ATP Synthase Beta Subunit

We sought to evaluate the reproducibility of a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based approach to measure the stable-isotope enrichment of in vivo-labeled muscle ATP synthase β subunit (β-F1-ATPase), a protein most directly involved in ATP production, and whose abundance is reduced under a variety of circumstances. Muscle was obtained from a rat infused with stable-isotope-labeled leucine. The muscle was homogenized, β-F1-ATPase immunoprecipitated, and the protein was resolved using 1D-SDS PAGE. Following trypsin digestion of the isolated protein, the resultant peptide mixtures were subjected to analysis by HPLC-ESI-MS/MS, which resulted in the detection of multiple β-F1-ATPase peptides. There were three β-F1-ATPase unique peptides with a leucine residue in the amino acid sequence, and which were detected with high intensity relative to other peptides and assigned with >95% probability to β-F1-ATPase. These peptides were specifically targeted for fragmentation to access their stable-isotope enrichment based on MS/MS peak areas calculated from extracted ion chromatographs for selected labeled and unlabeled fragment ions. Results showed best linearity (R2 = 0.99) in the detection of MS/MS peak areas for both labeled and unlabeled fragment ions, over a wide range of amounts of injected protein, specifically for the β-F1-ATPase134-143 peptide. Measured stable-isotope enrichment was highly reproducible for the β-F1-ATPase134-143 peptide (CV = 2.9%). Further, using mixtures of synthetic labeled and unlabeled peptides we determined that there is an excellent linear relationship (R2 = 0.99) between measured and predicted enrichment for percent enrichments ranging between 0.009% and 8.185% for the β-F1-ATPase134-143 peptide. The described approach provides a reliable approach to measure the stable-isotope enrichment of in-vivo-labeled muscle β-F1-ATPase based on the determination of the enrichment of the β-F1-ATPase134-143 peptide

IL-2 Inhibition of Th17 Generation Rather Than Induction of Treg Cells Is Impaired in Primary Sjögren’s Syndrome Patients

ObjectiveTo investigate the role of IL-2 in the balance of Th17 and Tregs and elucidate the underlying mechanisms of enhanced Th17 differentiation in primary Sjögren’s syndrome (pSS) patients.MethodsThis study involved 31 pSS patients, 7 Sicca patients, and 31 healthy subjects. Th17 and Treg cells were determined by flow cytometry, and IL-17A was detected by immunohistochemistry. IL-2 and IL-6 levels were assessed by ELISA and qPCR. p-STAT5 and p-STAT3 in salivary glands (SGs) were evaluated by immunohistochemistry and flow cytometry. The binding of STAT5 and STAT3 to the Il17a gene locus was measured by chromatin immunoprecipitation.ResultsWe found that the percentage of Th17 cells was increased in the periphery and SG of pSS patients when compared with healthy subjects, but the Treg cells was unchanged. Meanwhile, the IL-2 level was reduced, and the IL-6 and IL-17A level was increased in the plasma of pSS patients. The ratio of IL-2 and IL-6 level was also decreased and IL-2 level was negatively correlated with the level of IL-17A. The expression of Il6 and Il17a mRNA was significantly increased, whereas Foxp3, Tgfb1, Tnfa, and Ifng mRNA were comparable. Furthermore, the level of STAT5 phosphorylation (p-STAT5) was reduced and p-STAT3 was enhanced in the SGs and in peripheral CD4+ T cells of pSS patients. In vitro IL-2 treatment-induced STAT5 competed with STAT3 binding in human Il17a locus, leading to decreased Th17 differentiation, which was associated with the reduced transcription activation marker H3K4me3.ConclusionOur findings demonstrated a Treg-independent upregulation of Th17 generation in pSS, which is likely due to a lack of IL-2-mediated suppression of Th17 differentiation. This study identified a novel mechanism of IL-2-mediated immune suppression in pSS

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Security Attacks and Defenses in Cyber Systems: From an AI Perspective

Security of real-world cyber systems has drawn a lot of attention in recent years, especially when machine learning techniques are widely deployed into different layers of cyber systems. With the technology of machine learning, especially adversarial machine learning techniques, the attacks and defenses in cyber systems have shown a lot of new characteristics. In this dissertation, two major works regarding the attacks and defenses in real world cyber systems including dynamic spectrum sensing systems and High Performance Computing (HPC) systems and software systems are discussed. In the first work, we revisit this security vulnerability of cooperative spectrum sensing as an adversarial machine learning problem and propose a novel learning-empowered framework named Learning-Evaluation-Beating (LEB) to mislead fusion centers. Given the gap between the new LEB attack and existing defenses, we introduced a non-invasive and parallel method named influence-limiting defense sided with existing defenses to defend against LEB-based or other similar attacks. In the second work, we offer a novel perspective, treating the anomaly detection in HPC systems based on log files as a sequential decision process, and further applying reinforcement learning techniques to detect anomalies or malicious users. Start from there, we also provide a binary code similarity detection-based method that can be applied to a more general scenario in software systems through utilizing Recurrent Neural Network (RNN) and Siamese Neural Network to detect malwares from the binaries generated by the processor that executing the program

Security Attacks and Defenses in Cyber Systems: From an AI Perspective

Security of real-world cyber systems has drawn a lot of attention in recent years, especially when machine learning techniques are widely deployed into different layers of cyber systems. With the technology of machine learning, especially adversarial machine learning techniques, the attacks and defenses in cyber systems have shown a lot of new characteristics. In this dissertation, two major works regarding the attacks and defenses in real world cyber systems including dynamic spectrum sensing systems and High Performance Computing (HPC) systems and software systems are discussed. In the first work, we revisit this security vulnerability of cooperative spectrum sensing as an adversarial machine learning problem and propose a novel learning-empowered framework named Learning-Evaluation-Beating (LEB) to mislead fusion centers. Given the gap between the new LEB attack and existing defenses, we introduced a non-invasive and parallel method named influence-limiting defense sided with existing defenses to defend against LEB-based or other similar attacks. In the second work, we offer a novel perspective, treating the anomaly detection in HPC systems based on log files as a sequential decision process, and further applying reinforcement learning techniques to detect anomalies or malicious users. Start from there, we also provide a binary code similarity detection-based method that can be applied to a more general scenario in software systems through utilizing Recurrent Neural Network (RNN) and Siamese Neural Network to detect malwares from the binaries generated by the processor that executing the program