Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

Machine learning has become an important component for many systems and applications including computer vision, spam filtering, malware and network intrusion detection, among others. Despite the capabilities of machine learning algorithms to extract valuable information from data and produce accurate predictions, it has been shown that these algorithms are vulnerable to attacks. Data poisoning is one of the most relevant security threats against machine learning systems, where attackers can subvert the learning process by injecting malicious samples in the training data. Recent work in adversarial machine learning has shown that the so-called optimal attack strategies can successfully poison linear classifiers, degrading the performance of the system dramatically after compromising a small fraction of the training dataset. In this paper we propose a defence mechanism to mitigate the effect of these optimal poisoning attacks based on outlier detection. We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack. Hence, they can be detected with an appropriate pre-filtering of the training dataset

Correlation Clustering with Adaptive Similarity Queries

In correlation clustering, we are givennobjects together with a binary similarityscore between each pair of them. The goal is to partition the objects into clustersso to minimise the disagreements with the scores. In this work we investigatecorrelation clustering as an active learning problem: each similarity score can belearned by making a query, and the goal is to minimise both the disagreementsand the total number of queries. On the one hand, we describe simple activelearning algorithms, which provably achieve an almost optimal trade-off whilegiving cluster recovery guarantees, and we test them on different datasets. On theother hand, we prove information-theoretical bounds on the number of queriesnecessary to guarantee a prescribed disagreement bound. These results give a richcharacterization of the trade-off between queries and clustering error

Quantification of Women Who Could Benefit from Hormone Therapy after Endometrial Cancer Treatment: An Analysis of SEER Data

Our primary aim was to estimate the magnitude of stage I endometrial cancer (EC) survivors that could benefit from hormonal therapy (HT). Our secondary aims were to assess EC incidence in women below 50 and below 60 over the years, and analyze the overall survival and any influencing factors. We analyzed the endometrioid EC data from the Surveillance, Epidemiology, and End Results (SEER) program according to women’s age, tumor stage, and grade. We analyzed the proportions of EC survivors below 50 and below 60 years of age and stratified those age groups by race. For age distribution and survival analysis SEER, 18 registries’ research data (2000–2018) were analyzed. We analyzed the SEER 12 registries’ research data (1992–2019) for incidence time trends. Our investigation found a 14% and 40% cumulative prevalence of stage I EC that occurs in women below 50 or 60 years, respectively. EC’s prevalence has progressively risen in recent decades, but cancer-specific mortality remains low. The increasing number of women affected by EC in premenopause or early postmenopause face an 18 years-survival rate of 96.86% and 95.73%, respectively. A significant proportion of low-grade EC survivors can potentially benefit from HT treatment, and this requires awareness of other aspects of their health or quality of life, in addition to cancer treatments

Risk-Reducing Breast and Gynecological Surgery for BRCA Mutation Carriers: A Narrative Review

This narrative review aims to clarify the role of breast and gynecological risk-reduction surgery in BRCA mutation carriers. We examine the indications, contraindications, complications, technical aspects, timing, economic impact, ethical issues, and prognostic benefits of the most common prophylactic surgical options from the perspectives of a breast surgeon and a gynecologist. A comprehensive literature review was conducted using the PubMed/Medline, Scopus, and EMBASE databases. The databases were explored from their inceptions to August 2022. Three independent reviewers screened the items and selected those most relevant to this review’s scope. BRCA1/2 mutation carriers are significantly more likely to develop breast, ovarian, and serous endometrial cancer. Because of the Angelina effect, there has been a significant increase in bilateral risk-reducing mastectomy (BRRM) since 2013. BRRM and risk-reducing salpingo-oophorectomy (RRSO) significantly reduce the risk of developing breast and ovarian cancer. RRSO has significant side effects, including an impact on fertility and early menopause (i.e., vasomotor symptoms, cardiovascular disease, osteoporosis, cognitive impairment, and sexual dysfunction). Hormonal therapy can help with these symptoms. Because of the lower risk of developing breast cancer in the residual mammary gland tissue after BRRM, estrogen-only treatments have an advantage over an estrogen/progesterone combined treatment. Risk-reducing hysterectomy allows for estrogen-only treatments and lowers the risk of endometrial cancer. Although prophylactic surgery reduces the cancer risk, it has disadvantages associated with early menopause. A multidisciplinary team must carefully inform the woman who chooses this path of the broad spectrum of implications, from cancer risk reduction to hormonal therapies

AKT participates in endothelial dysfunction in hypertension.

In hypertension, reduced nitric oxide production and blunted endothelial vasorelaxation are observed. It was recently reported that AKT phosphorylates and activates endothelial nitric oxide synthase and that impaired kinase activity may be involved in endothelial dysfunction.To identify the physiological role of the kinase in normotensive Wistar-Kyoto rats (WKY) and spontaneously hypertensive rats (SHR), we used adenoviral vectors to transfer the human AKT1 gene selectively to the common carotid endothelium. In vitro, endothelial vasorelaxations to acetylcholine, isoproterenol, and insulin were blunted in control carotids from SHR compared with WKY rats, and human AKT1 overexpression corrected these responses. Similarly, blood flow assessed in vivo by Doppler ultrasound was reduced in SHR compared with WKY carotids and normalized after AKT1 gene transfer. In primary cultured endothelial cells, we evaluated AKT phosphorylation, activity, and compartmentalization and observed a mislocalization of the kinase in SHR.We conclude that AKT participates in the settings of endothelial dysfunction in SHR rats by impaired membrane localization. Our data suggest that AKT is involved in endothelium dysfunction in hypertension

Enhanced B-cell differentiation and reduced proliferative capacity in chronic hepatitis C and chronic hepatitis B virus infections

BACKGROUND & AIMS: Chronic microial infections aare frequently associated with B-cell activation and polyclonal proliferation, potentially leading to autoimmunity and lymphoproliferative disorders. We assessed B-cell phenotype and function in chronic hepatitis B (HBV) and chronic hepatitis C (HCV) virus infection. METHODS: We studied 70 patients with chronic HCV infection, 34 with chronic HBV infection and 54 healthy controls, B-cell phenotype was assessed by flow cytometry using monoclonal antibodies specific for CD27, the CD69, CD71, and CD86 activation markers and the chemokine receptor CXCR3. Differentiation into immunoglobulin-producing cells (IPC) was analysed by ELISpot upon stimulation and with CD40 ligand+IL-10 as surrogate bystander T-cell help or CpG oligodeoxynucleotide+IL-2, as innate immunity signal. Proliferation was examined by cytometry using carboxyfluorescein diacetate succinimidyl ester (CFSE) after stimulation with CpG. RESULTS: A significantly higher proportion of B cells from both HCV-and HBV-infected patients expressed activation markers compared with controls and a positive correlation was found between CXCR3(+) B cells and HCV RNA values. Memory B cells from patients with chronic HCV and HBV infections showed enhanced differentiation into IPC compared with controls, although this was restricted to IgG and at a lower level in HCV-compared with HBV-infected patients. Moreover, patients' activated B cells displayed significantly lower proliferative ability compared to healthy donors despite low expression of the FcRL4 exhaustin marker. CONCLUSIONS: B-cell activation, but not exhaustion, is common in chronic viral hepatitis. However, enhanced B-cell differentiation and deficient proliferative capacity were not associated with commitment to terminal differentiation

Label sanitization against label flipping poisoning attacks

Many machine learning systems rely on data collected in the wild from untrusted sources, exposing the learning algorithms to data poisoning. Attackers can inject malicious data in the training dataset to subvert the learning process, compromising the performance of the algorithm producing errors in a targeted or an indiscriminate way. Label flipping attacks are a special case of data poisoning, where the attacker can control the labels assigned to a fraction of the training points. Even if the capabilities of the attacker are constrained, these attacks have been shown to be effective to significantly degrade the performance of the system. In this paper we propose an efficient algorithm to perform optimal label flipping poisoning attacks and a mechanism to detect and relabel suspicious data points, mitigating the effect of such poisoning attacks

An experiment with conceptual clustering for the analysis of security alerts

In response to attack against corporative and enterprise networks, administrators deploy intrusion detection systems, monitors, vulnerability scans and log systems. These systems monitor and record host and network device activities searching for signs of anomalies and security incidents. Doing that, these systems generally produce a huge number of alerts that overwhelms security analysts. This paper proposes the application of a conceptual clustering technique for filtering alerts and shows the results obtained for seven months of security alerts generated in a real large scale SaaS Cloud system. The technique has been useful to support manual analysis activities conducted by the operations team of the reference Cloud system