arXiv subjects

Patrick McDaniel

Publications and source records attributed to Patrick McDaniel.

At least 73 records · Page 4Linked to original sources

arXiv2018

IoTSan: Fortifying the Safety of IoT Systems

Today's IoT systems include event-driven smart applications (apps) that interact with sensors and actuators. A problem specific to IoT systems is that buggy apps, unforeseen bad app interactions, or device/communication failures, can cause unsafe and dangerous physical states. Detecting flaws that lead to such states, requires a holistic view of installed apps, component devices, their configurations, and more importantly, how they interact. In this paper, we design IoTSan, a novel practical system that uses model checking as a building block to reveal "interaction-level" flaws by identifying events that can lead the system to unsafe states. In building IoTSan, we design novel techniques tailored to IoT systems, to alleviate the state explosion associated with model checking. IoTSan also automatically translates IoT apps into a format amenable to model checking. Finally, to understand the root cause of a detected vulnerability, we design an attribution mechanism to identify problematic and potentially malicious apps. We evaluate IoTSan on the Samsung SmartThings platform. From 76 manually configured systems, IoTSan detects 147 vulnerabilities. We also evaluate IoTSan with malicious SmartThings apps from a previous effort. IoTSan detects the potential safety violations and also effectively attributes these apps as malicious.

cs.CR↗

arXiv2018

Regulating Access to System Sensors in Cooperating Programs

Modern operating systems such as Android, iOS, Windows Phone, and Chrome OS support a cooperating program abstraction. Instead of placing all functionality into a single program, programs cooperate to complete tasks requested by users. However, untrusted programs may exploit interactions with other programs to obtain unauthorized access to system sensors either directly or through privileged services. Researchers have proposed that programs should only be authorized to access system sensors on a user-approved input event, but these methods do not account for possible delegation done by the program receiving the user input event. Furthermore, proposed delegation methods do not enable users to control the use of their input events accurately. In this paper, we propose ENTRUST, a system that enables users to authorize sensor operations that follow their input events, even if the sensor operation is performed by a program different from the program receiving the input event. ENTRUST tracks user input as well as delegation events and restricts the execution of such events to compute unambiguous delegation paths to enable accurate and reusable authorization of sensor operations. To demonstrate this approach, we implement the ENTRUST authorization system for Android. We find, via a laboratory user study, that attacks can be prevented at a much higher rate (54-64% improvement); and via a field user study, that ENTRUST requires no more than three additional authorizations per program with respect to the first-use approach, while incurring modest performance (<1%) and memory overheads (5.5 KB per program).

cs.CR↗

arXiv2018

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be used to develop more robust machine learning models and to provide standardized benchmarks of models' performance in the adversarial setting. Benchmarks constructed without a standardized implementation of adversarial example construction are not comparable to each other, because a good result may indicate a robust model or it may merely indicate a weak implementation of the adversarial example construction procedure. This technical report is structured as follows. Section 1 provides an overview of adversarial examples in machine learning and of the CleverHans software. Section 2 presents the core functionalities of the library: namely the attacks based on adversarial examples and defenses to improve the robustness of machine learning models to these attacks. Section 3 describes how to report benchmark results using the library. Section 4 describes the versioning system.

cs.LG↗

arXiv2018

Soteria: Automated IoT Safety and Security Analysis

Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital systems have changed the way we live, play and work. Yet existing IoT platforms cannot evaluate whether an IoT app or environment is safe, secure, and operates correctly. In this paper, we present Soteria, a static analysis system for validating whether an IoT app or IoT environment (collection of apps working in concert) adheres to identified safety, security, and functional properties. Soteria operates in three phases; (a) translation of platform-specific IoT source code into an intermediate representation (IR), (b) extracting a state model from the IR, (c) applying model checking to verify desired properties. We evaluate Soteria on 65 SmartThings market apps through 35 properties and find nine (14%) individual apps violate ten (29%) properties. Further, our study of combined app environments uncovered eleven property violations not exhibited in the isolated apps. Lastly, we demonstrate Soteria on MalIoT, a novel open-source test suite containing 17 apps with 20 unique violations.

cs.CR↗

arXiv2018

Detection under Privileged Information

For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network event, potential malware sample, suspicious email) is deemed malicious or non-malicious based on its similarity to the learned model at runtime. However, the training of the models has been historically limited to only those features available at runtime. In this paper, we consider an alternate learning approach that trains models using "privileged" information--features available at training time but not at runtime--to improve the accuracy and resilience of detection systems. In particular, we adapt and extend recent advances in knowledge transfer, model influence, and distillation to enable the use of forensic or other data unavailable at runtime in a range of security domains. An empirical evaluation shows that privileged information increases precision and recall over a system with no privileged information: we observe up to 7.7% relative decrease in detection error for fast-flux bot detection, 8.6% for malware traffic detection, 7.3% for malware classification, and 16.9% for face recognition. We explore the limitations and applications of different privileged information techniques in detection systems. Such techniques provide a new means for detection systems to learn from data that would otherwise not be available at runtime.

cs.CR↗

arXiv2018

Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

Deep neural networks (DNNs) enable innovative applications of machine learning like image recognition, machine translation, or malware detection. However, deep learning is often criticized for its lack of robustness in adversarial settings (e.g., vulnerability to adversarial inputs) and general inability to rationalize its predictions. In this work, we exploit the structure of deep learning to enable new learning-based inference and decision strategies that achieve desirable properties such as robustness and interpretability. We take a first step in this direction and introduce the Deep k-Nearest Neighbors (DkNN). This hybrid classifier combines the k-nearest neighbors algorithm with representations of the data learned by each layer of the DNN: a test input is compared to its neighboring training points according to the distance that separates them in the representations. We show the labels of these neighboring points afford confidence estimates for inputs outside the model's training manifold, including on malicious inputs like adversarial examples--and therein provides protections against inputs that are outside the models understanding. This is because the nearest neighbors can be used to estimate the nonconformity of, i.e., the lack of support for, a prediction in the training data. The neighbors also constitute human-interpretable explanations of predictions. We evaluate the DkNN algorithm on several datasets, and show the confidence estimates accurately identify inputs outside the model, and that the explanations provided by nearest neighbors are intuitive and useful in understanding model failures.

cs.LG↗

arXiv2018

Sensitive Information Tracking in Commodity IoT

Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital connectivity has had profound effects on society--smart homes, personal monitoring devices, enhanced manufacturing and other IoT apps have changed the way we live, play, and work. Yet extant IoT platforms provide few means of evaluating the use (and potential avenues for misuse) of sensitive information. Thus, consumers and organizations have little information to assess the security and privacy risks these devices present. In this paper, we present SainT, a static taint analysis tool for IoT applications. SainT operates in three phases; (a) translation of platform-specific IoT source code into an intermediate representation (IR), (b) identifying sensitive sources and sinks, and (c) performing static analysis to identify sensitive data flows. We evaluate SainT on 230 SmartThings market apps and find 138 (60%) include sensitive data flows. In addition, we demonstrate SainT on IoTBench, a novel open-source test suite containing 19 apps with 27 unique data leaks. Through this effort, we introduce a rigorously grounded framework for evaluating the use of sensitive information in IoT apps---and therein provide developers, markets, and consumers a means of identifying potential threats to security and privacy.

cs.CR↗

arXiv2018

Attacking Strategies and Temporal Analysis Involving Facebook Discussion Groups

Online social network (OSN) discussion groups are exerting significant effects on political dialogue. In the absence of access control mechanisms, any user can contribute to any OSN thread. Individuals can exploit this characteristic to execute targeted attacks, which increases the potential for subsequent malicious behaviors such as phishing and malware distribution. These kinds of actions will also disrupt bridges among the media, politicians, and their constituencies. For the concern of Security Management, blending malicious cyberattacks with online social interactions has introduced a brand new challenge. In this paper we describe our proposal for a novel approach to studying and understanding the strategies that attackers use to spread malicious URLs across Facebook discussion groups. We define and analyze problems tied to predicting the potential for attacks focused on threads created by news media organizations. We use a mix of macro static features and the micro dynamic evolution of posts and threads to identify likely targets with greater than 90% accuracy. One of our secondary goals is to make such predictions within a short (10 minute) time frame. It is our hope that the data and analyses presented in this paper will support a better understanding of attacker strategies and footprints, thereby developing new system management methodologies in handing cyber attacks on social networks.

cs.SI↗

arXiv2017

On the (Statistical) Detection of Adversarial Examples

Machine Learning (ML) models are applied in a variety of tasks such as network intrusion detection or Malware classification. Yet, these models are vulnerable to a class of malicious inputs known as adversarial examples. These are slightly perturbed inputs that are classified incorrectly by the ML model. The mitigation of these adversarial inputs remains an open problem. As a step towards understanding adversarial examples, we show that they are not drawn from the same distribution than the original data, and can thus be detected using statistical tests. Using thus knowledge, we introduce a complimentary approach to identify specific inputs that are adversarial. Specifically, we augment our ML model with an additional output, in which the model is trained to classify all adversarial inputs. We evaluate our approach on multiple adversarial example crafting methods (including the fast gradient sign and saliency map methods) with several datasets. The statistical test flags sample sets containing adversarial inputs confidently at sample sizes between 10 and 100 data points. Furthermore, our augmented model either detects adversarial examples as outliers with high accuracy (> 80%) or increases the adversary's cost - the perturbation added - by more than 150%. In this way, we show that statistical properties of adversarial examples are essential to their detection.

cs.CR↗

arXiv2017

Patient-Driven Privacy Control through Generalized Distillation

The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits, and clinical history. This data is then used by statistical models to predict personalized treatments. However, due to privacy concerns, patients often desire to withhold sensitive information. This self-censorship can impede proper diagnosis and treatment, which may lead to serious health complications and even death over time. In this paper, we present privacy distillation, a mechanism which allows patients to control the type and amount of information they wish to disclose to the healthcare providers for use in statistical models. Meanwhile, it retains the accuracy of models that have access to all patient data under a sufficient but not full set of privacy-relevant information. We validate privacy distillation using a corpus of patients prescribed to warfarin for a personalized dosage. We use a deep neural network to implement privacy distillation for training and making dose predictions. We find that privacy distillation with sufficient privacy-relevant information i) retains accuracy almost as good as having all patient data (only 3\% worse), and ii) is effective at preventing errors that introduce health-related risks (only 3.9\% worse under- or over-prescriptions).

cs.CR↗

arXiv2017

Feasibility of Direct Disposal of Salt Waste from Electochemical Processing of Spent Nuclear Fuel

The US Department of Energy decided in 2000 to treat its sodium-bonded spent nuclear fuel, produced for experiments on breeder reactors, with an electrochemical process. The metallic waste produced is to be cast into ingots and the salt waste further processed to form a ceramic waste form for disposal in a mined repository. However, alternative disposal pathways for metallic and salt waste streams are being investigated that may reduce the processing complexity. As summarized here, performance assessments analyzing the direct disposal the salt waste demonstrate that both mined repositories in salt and deep boreholes in basement crystalline rock can easily accommodate the salt waste. Also summarized here is an analysis of the feasibility of transporting the salt waste in a proposed vessel. The vessel is viable for transport to and disposal in a generic mined repository in salt or deep borehole but a portion of the salt waste would need to be diluted for disposal in the Waste Isolation Pilot Plant. The generally positive results continue to demonstrate the feasibility of direct disposal of salt waste after electrochemical processing of spent nuclear fuel.

physics.app-ph↗

arXiv2017

Achieving Secure and Differentially Private Computations in Multiparty Settings

Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. Although SMC is instrumental in such distributed settings, it does not provide any guarantees not to leak any information about individuals to adversaries. Differential privacy (DP) can be utilized to address this; however, achieving SMC with DP is not a trivial task, either. In this paper, we propose a novel Secure Multiparty Distributed Differentially Private (SM-DDP) protocol to achieve secure and private computations in a multiparty environment. Specifically, with our protocol, we simultaneously achieve SMC and DP in distributed settings focusing on linear regression on horizontally distributed data. That is, parties do not see each others' data and further, can not infer information about individuals from the final constructed statistical model. Any statistical model function that allows independent calculation of local statistics can be computed through our protocol. The protocol implements homomorphic encryption for SMC and functional mechanism for DP to achieve the desired security and privacy guarantees. In this work, we first introduce the theoretical foundation for the SM-DDP protocol and then evaluate its efficacy and performance on two different datasets. Our results show that one can achieve individual-level privacy through the proposed protocol with distributed DP, which is independently applied by each party in a distributed fashion. Moreover, our results also show that the SM-DDP protocol incurs minimal computational overhead, is scalable, and provides security and privacy guarantees.

cs.CR↗

arXiv2017

The Space of Transferable Adversarial Examples

Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time. They often transfer: the same adversarial example fools more than one model. In this work, we propose novel methods for estimating the previously unknown dimensionality of the space of adversarial inputs. We find that adversarial examples span a contiguous subspace of large (~25) dimensionality. Adversarial subspaces with higher dimensionality are more likely to intersect. We find that for two different models, a significant fraction of their subspaces is shared, thus enabling transferability. In the first quantitative analysis of the similarity of different models' decision boundaries, we show that these boundaries are actually close in arbitrary directions, whether adversarial or benign. We conclude by formally studying the limits of transferability. We derive (1) sufficient conditions on the data distribution that imply transferability for simple model classes and (2) examples of scenarios in which transfer does not occur. These findings indicate that it may be possible to design defenses against transfer-based attacks, even for models that are vulnerable to direct attacks.

stat.ML↗

arXiv2017

Extending Defensive Distillation

Machine learning is vulnerable to adversarial examples: inputs carefully modified to force misclassification. Designing defenses against such inputs remains largely an open problem. In this work, we revisit defensive distillation---which is one of the mechanisms proposed to mitigate adversarial examples---to address its limitations. We view our results not only as an effective way of addressing some of the recently discovered attacks but also as reinforcing the importance of improved training techniques.

cs.LG↗

arXiv2017

Practical Black-Box Attacks against Machine Learning

Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24% of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19% and 88.94%. We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder.

cs.CR↗

arXiv2016

Towards the Science of Security and Privacy in Machine Learning

Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive---new systems and models are being deployed in every domain imaginable, leading to rapid and widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited. We systematize recent findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date. We articulate a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework. Key insights resulting from works both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by formally exploring the opposing relationship between model accuracy and resilience to adversarial manipulation. Through these explorations, we show that there are (possibly unavoidable) tensions between model complexity, accuracy, and resilience that must be calibrated for the environments in which they will be used.

cs.CR↗

arXiv2016

On the Effectiveness of Defensive Distillation

We report experimental results indicating that defensive distillation successfully mitigates adversarial samples crafted using the fast gradient sign method, in addition to those crafted using the Jacobian-based iterative attack on which the defense mechanism was originally evaluated.

cs.CR↗

arXiv2016

Adversarial Perturbations Against Deep Neural Networks for Malware Classification

Deep neural networks, like many other machine learning models, have recently been shown to lack robustness against adversarially crafted inputs. These inputs are derived from regular inputs by minor yet carefully selected perturbations that deceive machine learning models into desired misclassifications. Existing work in this emerging field was largely specific to the domain of image classification, since the high-entropy of images can be conveniently manipulated without changing the images' overall visual appearance. Yet, it remains unclear how such attacks translate to more security-sensitive applications such as malware detection - which may pose significant challenges in sample generation and arguably grave consequences for failure. In this paper, we show how to construct highly-effective adversarial sample crafting attacks for neural networks used as malware classifiers. The application domain of malware classification introduces additional constraints in the adversarial sample crafting problem when compared to the computer vision domain: (i) continuous, differentiable input domains are replaced by discrete, often binary inputs; and (ii) the loose condition of leaving visual appearance unchanged is replaced by requiring equivalent functional behavior. We demonstrate the feasibility of these attacks on many different instances of malware classifiers that we trained using the DREBIN Android malware data set. We furthermore evaluate to which extent potential defensive mechanisms against adversarial crafting can be leveraged to the setting of malware classification. While feature reduction did not prove to have a positive impact, distillation and re-training on adversarially crafted samples show promising results.

cs.CR↗