Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability

In recent years, the topic of explainable machine learning (ML) has been extensively researched. Up until now, this research focused on regular ML users use-cases such as debugging a ML model. This paper takes a different posture and show that adversaries can leverage explainable ML to bypass multi-feature types malware classifiers. Previous adversarial attacks against such classifiers only add new features and not modify existing ones to avoid harming the modified malware executable's functionality. Current attacks use a single algorithm that both selects which features to modify and modifies them blindly, treating all features the same. In this paper, we present a different approach. We split the adversarial example generation task into two parts: First we find the importance of all features for a specific sample using explainability algorithms, and then we conduct a feature-specific modification, feature-by-feature. In order to apply our attack in black-box scenarios, we introduce the concept of transferability of explainability, that is, applying explainability algorithms to different classifiers using different features subsets and trained on different datasets still result in a similar subset of important features. We conclude that explainability algorithms can be leveraged by adversaries and thus the advocates of training more interpretable classifiers should consider the trade-off of higher vulnerability of those classifiers to adversarial attacks.Comment: Accepted as a conference paper at IJCNN 202

Conditioned Pain Modulation Is Associated with Common Polymorphisms in the Serotonin Transporter Gene

BACKGROUND: Variation in the serotonin transporter (5-HTT) gene (SLC6A4) has been shown to influence a wide range of affective processes. Low 5-HTT gene-expression has also been suggested to increase the risk of chronic pain. Conditioned pain modulation (CPM)--i.e. 'pain inhibits pain'--is impaired in chronic pain states and, reciprocally, aberrations of CPM may predict the development of chronic pain. Therefore we hypothesized that a common variation in the SLC6A4 is associated with inter-individual variation in CPM. Forty-five healthy subjects recruited on the basis of tri-allelic 5-HTTLPR genotype, with inferred high or low 5-HTT-expression, were included in a double-blind study. A submaximal-effort tourniquet test was used to provide a standardized degree of conditioning ischemic pain. Individualized noxious heat and pressure pain thresholds (PPTs) were used as subjective test-modalities and the nociceptive flexion reflex (NFR) was used to provide an objective neurophysiological window into spinal processing. RESULTS: The low, as compared to the high, 5-HTT-expressing group exhibited significantly reduced CPM-mediated pain inhibition for PPTs (p = 0.02) and heat-pain (p = 0.02). The CPM-mediated inhibition of the NFR, gauged by increases in NFR-threshold, did not differ significantly between groups (p = 0.75). Inhibition of PPTs and heat-pain were correlated (Spearman's rho = 0.35, p = 0.02), whereas the NFR-threshold increase was not significantly correlated with degree of inhibition of these subjectively reported modalities. CONCLUSIONS: Our results demonstrate the involvement of the tri-allelic 5-HTTLPR genotype in explaining clinically relevant inter-individual differences in pain perception and regulation. Our results also illustrate that shifts in NFR-thresholds do not necessarily correlate to the modulation of experienced pain. We discuss various possible mechanisms underlying these findings and suggest a role of regulation of 5-HT receptors along the neuraxis as a function of differential 5-HTT-expression

Incorporating concepts of inequality and inequity into health benefits analysis

BACKGROUND: Although environmental policy decisions are often based in part on both risk assessment information and environmental justice concerns, formalized approaches for addressing inequality or inequity when estimating the health benefits of pollution control have been lacking. Inequality indicators that fulfill basic axioms and agree with relevant definitions and concepts in health benefits analysis and environmental justice analysis can allow for quantitative examination of efficiency-equality tradeoffs in pollution control policies. METHODS: To develop appropriate inequality indicators for health benefits analysis, we provide relevant definitions from the fields of risk assessment and environmental justice and consider the implications. We evaluate axioms proposed in past studies of inequality indicators and develop additional axioms relevant to this context. We survey the literature on previous applications of inequality indicators and evaluate five candidate indicators in reference to our proposed axioms. We present an illustrative pollution control example to determine whether our selected indicators provide interpretable information. RESULTS AND CONCLUSIONS: We conclude that an inequality indicator for health benefits analysis should not decrease when risk is transferred from a low-risk to high-risk person, and that it should decrease when risk is transferred from a high-risk to low-risk person (Pigou-Dalton transfer principle), and that it should be able to have total inequality divided into its constituent parts (subgroup decomposability). We additionally propose that an ideal indicator should avoid value judgments about the relative importance of transfers at different percentiles of the risk distribution, incorporate health risk with evidence about differential susceptibility, include baseline distributions of risk, use appropriate geographic resolution and scope, and consider multiple competing policy alternatives. Given these criteria, we select the Atkinson index as the single indicator most appropriate for health benefits analysis, with other indicators useful for sensitivity analysis. Our illustrative pollution control example demonstrates how these indices can help a policy maker determine control strategies that are dominated from an efficiency and equality standpoint, those that are dominated for some but not all societal viewpoints on inequality averseness, and those that are on the optimal efficiency-equality frontier, allowing for more informed pollution control policies

Self-diagnosis techniques and their applications to error reduction for ultrasonic flow measurement

Flow metering plays a major role in modern life. In the process industry, flow metering is critical in industries ranging from food processing to cosmetics. It is also essential in custody transfer or billing, as flow meters are present in gas pumps and district heating substations. In the district heating industry, the ultrasonic flow meter has become the desired meter in many of its applications because it has a low cost while being accurate. This accuracy is however sensitive to installation effects and other sources of errors. This thesis stems from research that addresses the recognition of these installation effects, informs when they are unacceptable and considers reducing the measurement errors. To present these concepts, the thesis details the estimation of the mean flow velocity, the calibration of the meter and the measurement noise properties. Once installed, any kind of meter provides larger errors than in the facility where it has been calibrated and compensated. It is particularly true for ultrasonic flow meters as they are very sensitive to installation effects. Installation effects can either be static or dynamic. Special attention is paid to errors generated by temperature and velocity profile variations. Velocity profile variations can be due to pipe bends or flow pulsations. Such disturbances often induce a bias error and change the properties of the measurement noise. It is therefore with help of the change in noise that velocity profile disturbances can be detected. The detection of such abnormal behaviour of the measurement process constitutes a diagnosis. A diagnosis of the sensitivity of the meter to installations effects would allow for compensations for the errors. Signal analysis allows detection of specific noise properties, characteristic of installation effects. An example of self-diagnosis showing the detection of real pulsations in a flow is described in details. The detection of the flow pulsations and the estimation of their frequency allow to reduce the error of estimation on the flow rate. This technique is confirmed by the simulations of a pulsating flow. To empower one with the decision whether a flowmeter performance is normal or abnormal, a study of the relative error as a function of flow rate and temperature has been conducted.Godkänd; 2004; 20080708 (evan)</p

Self-diagnosis techniques and their applications to error reduction for ultrasonic flow measurement

Flow metering plays a major role in modern life. In the process industry, flow metering is critical in industries ranging from food processing to cosmetics. It is also essential in custody transfer or billing, as flow meters are present in gas pumps and district heating substations. In the district heating industry, the ultrasonic flow meter has become the desired meter in many of its applications because it has a low cost while being accurate. This accuracy is however sensitive to installation effects and other sources of errors. This thesis stems from research that addresses the recognition of these installation effects, informs when they are unacceptable and considers reducing the measurement errors. To present these concepts, the thesis details the estimation of the mean flow velocity, the calibration of the meter and the measurement noise properties. Once installed, any kind of meter provides larger errors than in the facility where it has been calibrated and compensated. It is particularly true for ultrasonic flow meters as they are very sensitive to installation effects. Installation effects can either be static or dynamic. Special attention is paid to errors generated by temperature and velocity profile variations. Velocity profile variations can be due to pipe bends or flow pulsations. Such disturbances often induce a bias error and change the properties of the measurement noise. It is therefore with help of the change in noise that velocity profile disturbances can be detected. The detection of such abnormal behaviour of the measurement process constitutes a diagnosis. A diagnosis of the sensitivity of the meter to installations effects would allow for compensations for the errors. Signal analysis allows detection of specific noise properties, characteristic of installation effects. An example of self-diagnosis showing the detection of real pulsations in a flow is described in details. The detection of the flow pulsations and the estimation of their frequency allow to reduce the error of estimation on the flow rate. This technique is confirmed by the simulations of a pulsating flow. To empower one with the decision whether a flowmeter performance is normal or abnormal, a study of the relative error as a function of flow rate and temperature has been conducted.Godkänd; 2004; 20080708 (evan)</p

Detection of dynamic installation effects by an ultrasound flow meter in non-stationary flow

Godkänd; 2004; 20060925 (ysko)</p

Detection of pulsating flows in an ultrasonic flow meter

Transit-time ultrasonic flowmeters present advantages for district heating applications, since they are accurate, non-intrusive, and cheap. However, such flowmeters are sensitive to velocity profile variations since the flow rate is measured in the volume area between two ultrasonic transducers. Ultrasonic flowmeters are therefore sensitive to installation effects. Installation effects could be either static or dynamic. A pulsating flow is a dynamic installation effect. In the field, the diagnostic can only be performed with the measured flow rate. Flow measurements with and without pulsating flow have been recorded in a flow meter calibration facility. The detection of a pulsating flow can be made by using Hinich's harmogram. It is possible to detect harmonics that emerge from the noise by using the harmogram.Godkänd; 2002; 20070328 (pekkari)Fjärrvärm

Reducing the flow measurement error caused by pulsations in flows

Different types of errors are generated by pulsations in flows. Among these errors is the sampling error due to a unadapted time-averaging of the flow rate. An improved model for pulsations in flows including harmonics is derived. The localization of the harmonics is performed by a detector. The period of the pulsations is estimated. It is then possible to reduce the sampling error by performing a correct averaging. The reduction of the sampling error is confirmed by simulations.Validerad; 2004; 20060922 (ysko)</p

Ultrasonic flow metering errors due to pulsating flow

Transit-time ultrasonic flow meters present some advantages over other flow meters for district heating industries. They are both accurate and non-intrusive. It is well-known that ultrasonic flow meters are sensitive to installation effects. Installation effects could be static or dynamic. Among the possible dynamic installation effects is pulsating flow. The influence of pulsating flow on the prediction and the zero-crossing operations is investigated. Expressions are found for the prediction error and the zero-crossing error. The relative errors due to the prediction and the zero-crossing are plotted. The prediction error can reach dramatic values while the zero-crossing operation is hardly influenced by flow pulsations.Validerad; 2004; 20060922 (ysko)</p