IoT devices are known to be vulnerable to various cyber-attacks, such as data exfiltration and the execution of flooding attacks as part of a DDoS attack. When it comes to detecting such attacks ...using network traffic analysis, it has been shown that some attack scenarios are not always equally easy to detect if they involve different IoT models. That is, when targeted at some IoT models, a given attack can be detected rather accurately, while when targeted at others the same attack may result in too many false alarms. In this research, we attempt to explain this variability of IoT attack detectability and devise a risk assessment method capable of addressing a key question: how easy is it for an anomaly-based network intrusion detection system to detect a given cyber-attack involving a specific IoT model? In the process of addressing this question we (a) investigate the predictability of IoT network traffic, (b) present a novel taxonomy for IoT attack detection which also encapsulates traffic predictability aspects, (c) propose an expert-based attack detectability estimation method which uses this taxonomy to derive a detectability score (termed `D-Score') for a given combination of IoT model and attack scenario, and (d) empirically evaluate our method while comparing it with a data-driven method.
Adversarial attacks against deep learning-based object detectors (ODs) have been studied extensively in the past few years. These attacks cause the model to make incorrect predictions by placing a ...patch containing an adversarial pattern on the target object or anywhere within the frame. However, none of prior research proposed a misclassification attack on ODs, in which the patch is applied on the target object. In this study, we propose a novel, universal, targeted, label-switch attack against the state-of-the-art object detector, YOLO. In our attack, we use (i) a tailored projection function to enable the placement of the adversarial patch on multiple target objects in the image (e.g., cars), each of which may be located a different distance away from the camera or have a different view angle relative to the camera, and (ii) a unique loss function capable of changing the label of the attacked objects. The proposed universal patch, which is trained in the digital domain, is transferable to the physical domain. We performed an extensive evaluation using different types of object detectors, different video streams captured by different cameras, and various target classes, and evaluated different configurations of the adversarial patch in the physical domain.
O-RAN is a new, open, adaptive, and intelligent RAN architecture. Motivated by the success of artificial intelligence in other domains, O-RAN strives to leverage machine learning (ML) to ...automatically and efficiently manage network resources in diverse use cases such as traffic steering, quality of experience prediction, and anomaly detection. Unfortunately, it has been shown that ML-based systems are vulnerable to an attack technique referred to as adversarial machine learning (AML). This special kind of attack has already been demonstrated in recent studies and in multiple domains. In this paper, we present a systematic AML threat analysis for O-RAN. We start by reviewing relevant ML use cases and analyzing the different ML workflow deployment scenarios in O-RAN. Then, we define the threat model, identifying potential adversaries, enumerating their adversarial capabilities, and analyzing their main goals. Next, we explore the various AML threats associated with O-RAN and review a large number of attacks that can be performed to realize these threats and demonstrate an AML attack on a traffic steering model. In addition, we analyze and propose various AML countermeasures for mitigating the identified threats. Finally, based on the identified AML threats and countermeasures, we present a methodology and a tool for performing risk assessment for AML attacks for a specific ML use case in O-RAN.
The outcome of a collective decision-making process, such as crowdsourcing, often relies on the procedure through which the perspectives of its individual members are aggregated. Popular aggregation ...methods, such as the majority rule, often fail to produce the optimal result, especially in high-complexity tasks. Methods that rely on meta-cognitive information, such as confidence-based methods and the Surprisingly Popular Option, had shown an improvement in various tasks. However, there is still a significant number of cases with no optimal solution. Our aim is to exploit meta-cognitive information and to learn from it, for the purpose of enhancing the ability of the group to produce a correct answer. Specifically, we propose two different feature-representation approaches: (1) Response-Centered feature Representation (RCR), which focuses on the characteristics of the individual response instances, and (2) Answer-Centered feature Representation (ACR), which focuses on the characteristics of each of the potential answers. Using these two feature-representation approaches, we train Machine-Learning (ML) models, for the purpose of predicting the correctness of a response and of an answer. The trained models are used as the basis of an ML-based aggregation methodology that, contrary to other ML-based techniques, has the advantage of being a "one-shot" technique, independent from the crowd-specific composition and personal record, and adaptive to various types of situations. To evaluate our methodology, we collected 2490 responses for different tasks, which we used for feature engineering and for the training of ML models. We tested our feature-representation approaches through the performance of our proposed ML-based aggregation methods. The results show an increase of 20% to 35% in the success rate, compared to the use of standard rule-based aggregation methods.
Neural scene representation and rendering Eslami, S M Ali; Jimenez Rezende, Danilo; Besse, Frederic ...
Science (American Association for the Advancement of Science),
06/2018, Volume:
360, Issue:
6394
Journal Article
Peer reviewed
Open access
Scene representation-the process of converting visual sensory data into concise descriptions-is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task ...when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.
Aims
To assess the short‐term immunogenicity to severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) mRNA vaccine in a population of heart transplant (HTx) recipients. A prospective ...single‐centre cohort study of HTx recipients who received a two‐dose SARS‐CoV‐2 mRNA vaccine (BNT162b2, Pfizer‐BioNTech).
Methods and results
Whole blood for anti‐spike IgG (S‐IgG) antibodies was drawn at days 21–26 and at days 35–40 after the first vaccine dose. Geometric mean titres (GMT) ≥50 AU/mL were interpreted positive. Included were 42 HTx recipients at a median age of 61 interquartile range (IQR) 44–69 years. Median time from HTx to the first vaccine dose was 9.1 (IQR 2.6–14) years. Only 15% of HTx recipients demonstrated the presence of positive S‐IgG antibody titres in response to the first vaccine dose GMT 90 (IQR 54–229) AU/mL. Overall, 49% of HTx recipients induced S‐IgG antibodies in response to either the first or the full two‐dose vaccine schedule GMT 426 (IQR 106–884) AU/mL. Older age 68 (IQR 59–70) years vs. 46 (IQR 34–63) years, P = 0.034 and anti‐metabolite‐based immunosuppression protocols (89% vs. 44%, P = 0.011) were associated with low immunogenicity. Importantly, 36% of HTx recipients who were non‐responders to the first vaccine dose became S‐IgG seropositive in response to the second vaccine dose. Approximately a half of HTx recipients did not generate S‐IgG antibodies following SARS‐CoV‐2 two‐dose vaccine.
Conclusions
The generally achieved protection from SARS‐CoV‐2 mRNA vaccination should be regarded with caution in the population of HTx recipients. The possible benefit of additive vaccine should be further studied.
Background
Tracheal invasion in thyroid cancer is a well-known form of advanced disease. There is an ongoing controversy over outcomes of tracheal shaving in this situation. The aim of this study was ...to compare the results of tracheal shaving to radical resections in patients with low-volume tracheal involvement.
Methods
An institutional case series and a meta-analysis was conducted. All studies that included patients diagnosed with well-differentiated thyroid cancer (WDTC) and tracheal invasion were analyzed. Patients with low-volume tracheal invasion (according to the Shin classification) were extracted from the various studies and subsequently included in this study. The outcomes of tracheal shaving and radical resection were consolidated and compared. All recurrences and mortality over 10 years of follow-up were calculated using the Kaplan–Meier method.
Results
Institutional case series included 22 patients diagnosed with WDTC and tracheal invasion that underwent resection. There was one case of recurrence (4.5%) during the follow-up period and no mortality. The meta-analysis yielded a total of 284 patients from six studies who met the inclusion criteria. The 10-year overall survival was 82.4% for the shave group and 80.8% for the resection group. The combined Kaplan–Meier curves revealed no statistically significant difference between the two techniques (hazard ratio HR = 0.86,
P
= .768). The combined 10-year local control rate of the shave group was 90.2%.
Conclusions
The outcomes of tracheal shaving in low-volume invasion are similar to more aggressive forms of tracheal resections. Shave resection is oncologically safe in carefully selected WDTC patients demonstrating minimal tracheal invasion.
Regeneration failure after spinal cord injury (SCI) results in part from the lack of a pro-regenerative response in injured neurons, but the response to SCI has not been examined specifically in ...injured sensory neurons. Using RNA sequencing of dorsal root ganglion, we determined that thoracic SCI elicits a transcriptional response distinct from sciatic nerve injury (SNI). Both SNI and SCI induced upregulation of ATF3 and Jun, yet this response failed to promote growth in sensory neurons after SCI. RNA sequencing of purified sensory neurons one and three days after injury revealed that unlike SNI, the SCI response is not sustained. Both SCI and SNI elicited the expression of ATF3 target genes, with very little overlap between conditions. Pathway analysis of differentially expressed ATF3 target genes revealed that fatty acid biosynthesis and terpenoid backbone synthesis were downregulated after SCI but not SNI. Pharmacologic inhibition of fatty acid synthase, the enzyme generating palmitic acid, decreased axon growth and regeneration in vitro. These results support the notion that decreased expression of lipid metabolism-related genes after SCI, including fatty acid synthase, may restrict axon regenerative capacity after SCI.
Unlike visceral adipose tissue (VAT), the association between subcutaneous adipose tissue (SAT) and obesity-related morbidity is controversial. In patients with type 2 diabetes, we assessed whether ...this variability can be explained by a putative favorable, distinct association between abdominal superficial SAT (SSAT) (absolute amount or its proportion) and cardiometabolic parameters.
We performed abdominal magnetic resonance imaging (MRI) in 73 patients with diabetes (mean age 58 years, 83% were men) and cross-sectionally analyzed fat distribution at S1-L5, L5-L4, and L3-L2 levels. Patients completed food frequency questionnaires, and subgroups had 24-h ambulatory blood pressure monitoring and 24-h ambulatory electrocardiography.
Women had higher %SSAT (37 vs. 23% in men; P < 0.001) despite a similar mean waist circumference. Fasting plasma glucose (P = 0.046) and HbA(1c) (P = 0.006) were both lower with increased tertile of absolute SSAT. In regression models adjusted for age, waist circumference, and classes of medical treatments used in this patient population, increased %SSAT was significantly associated with decreased HbA(1c) (β = -0.317; P = 0.013), decreased daytime ambulatory blood pressure (β = -0.426; P = 0.008), and increased HDL cholesterol (β = 0.257; P = 0.042). In contrast, increased percent of deep SAT (DSAT) was associated with increased HbA(1c) (β = 0.266; P = 0.040) and poorer heart rate variability parameters (P = 0.030). Although total fat and energy intake were not correlated with fat tissue distribution, increased intake of trans fat tended to be associated with total SAT (r = 0.228; P = 0.05) and DSAT (r = 0.20; P = 0.093), but not with SSAT.
Abdominal SAT is composed of two subdepots that associate differently with cardiometabolic parameters. Higher absolute and relative distribution of fat in abdominal SSAT may signify beneficial cardiometabolic effects in patients with type 2 diabetes.
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