arXiv subjects

Earlence Fernandes

Publications and source records attributed to Earlence Fernandes.

27 records · Page 2Linked to original sources

arXiv2018

Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities

Recent advances in Internet of Things (IoT) have enabled myriad domains such as smart homes, personal monitoring devices, and enhanced manufacturing. IoT is now pervasive---new applications are being used in nearly every conceivable environment, which leads to the adoption of device-based interaction and automation. However, IoT has also raised issues about the security and privacy of these digitally augmented spaces. Program analysis is crucial in identifying those issues, yet the application and scope of program analysis in IoT remains largely unexplored by the technical community. In this paper, we study privacy and security issues in IoT that require program-analysis techniques with an emphasis on identified attacks against these systems and defenses implemented so far. Based on a study of five IoT programming platforms, we identify the key insights that result from research efforts in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues. We conclude by studying recent IoT analysis systems and exploring their implementations. Through these explorations, we highlight key challenges and opportunities in calibrating for the environments in which IoT systems will be used.

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arXiv2018

Tyche: Risk-Based Permissions for Smart Home Platforms

Emerging smart home platforms, which interface with a variety of physical devices and support third-party application development, currently use permission models inspired by smartphone operating systems-they group functionally similar device operations into separate units, and require users to grant apps access to devices at that granularity. Unfortunately, this leads to two issues: (1) apps that do not require access to all of the granted device operations have overprivileged access to them, (2) apps might pose a higher risk to users than needed because physical device operations are fundamentally risk-asymmetric-"door.unlock" provides access to burglars, and "door.lock" can potentially lead to getting locked out. Overprivileged apps with access to mixed-risk operations only increase the potential for damage. We present Tyche, a system that leverages the risk-asymmetry in physical device operations to limit the risk that apps pose to smart home users, without increasing the user's decision overhead. Tyche introduces the notion of risk-based permissions. When using risk-based permissions, device operations are grouped into units of similar risk, and users grant apps access to devices at that risk-based granularity. Starting from a set of permissions derived from the popular Samsung SmartThings platform, we conduct a user study involving domain-experts and Mechanical Turk users to compute a relative ranking of risks associated with device operations. We find that user assessment of risk closely matches that of domain experts. Using this ranking, we define risk-based groupings of device operations, and apply it to existing SmartThings apps, showing that risk-based permissions indeed limit risk if apps are malicious or exploitable.

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arXiv2018

Physical Adversarial Examples for Object Detectors

Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attacks generalize to the physical domain, to create perturbations on physical objects that fool image classifiers under a variety of real-world conditions. Such attacks pose a risk to deep learning models used in safety-critical cyber-physical systems. In this work, we extend physical attacks to more challenging object detection models, a broader class of deep learning algorithms widely used to detect and label multiple objects within a scene. Improving upon a previous physical attack on image classifiers, we create perturbed physical objects that are either ignored or mislabeled by object detection models. We implement a Disappearance Attack, in which we cause a Stop sign to "disappear" according to the detector-either by covering thesign with an adversarial Stop sign poster, or by adding adversarial stickers onto the sign. In a video recorded in a controlled lab environment, the state-of-the-art YOLOv2 detector failed to recognize these adversarial Stop signs in over 85% of the video frames. In an outdoor experiment, YOLO was fooled by the poster and sticker attacks in 72.5% and 63.5% of the video frames respectively. We also use Faster R-CNN, a different object detection model, to demonstrate the transferability of our adversarial perturbations. The created poster perturbation is able to fool Faster R-CNN in 85.9% of the video frames in a controlled lab environment, and 40.2% of the video frames in an outdoor environment. Finally, we present preliminary results with a new Creation Attack, where in innocuous physical stickers fool a model into detecting nonexistent objects.

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arXiv2018

Note on Attacking Object Detectors with Adversarial Stickers

Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks. However, deep learning algorithms are known to be vulnerable to adversarial examples. These adversarial inputs are created such that, when provided to a deep learning algorithm, they are very likely to be mislabeled. This can be problematic when deep learning is used to assist in safety critical decisions. Recent research has shown that classifiers can be attacked by physical adversarial examples under various physical conditions. Given the fact that state-of-the-art objection detection algorithms are harder to be fooled by the same set of adversarial examples, here we show that these detectors can also be attacked by physical adversarial examples. In this note, we briefly show both static and dynamic test results. We design an algorithm that produces physical adversarial inputs, which can fool the YOLO object detector and can also attack Faster-RCNN with relatively high success rate based on transferability. Furthermore, our algorithm can compress the size of the adversarial inputs to stickers that, when attached to the targeted object, result in the detector either mislabeling or not detecting the object a high percentage of the time. This note provides a small set of results. Our upcoming paper will contain a thorough evaluation on other object detectors, and will present the algorithm.

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arXiv2018

Robust Physical-World Attacks on Deep Learning Models

Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. Given that that emerging physical systems are using DNNs in safety-critical situations, adversarial examples could mislead these systems and cause dangerous situations.Therefore, understanding adversarial examples in the physical world is an important step towards developing resilient learning algorithms. We propose a general attack algorithm,Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions. Using the real-world case of road sign classification, we show that adversarial examples generated using RP2 achieve high targeted misclassification rates against standard-architecture road sign classifiers in the physical world under various environmental conditions, including viewpoints. Due to the current lack of a standardized testing method, we propose a two-stage evaluation methodology for robust physical adversarial examples consisting of lab and field tests. Using this methodology, we evaluate the efficacy of physical adversarial manipulations on real objects. Witha perturbation in the form of only black and white stickers,we attack a real stop sign, causing targeted misclassification in 100% of the images obtained in lab settings, and in 84.8%of the captured video frames obtained on a moving vehicle(field test) for the target classifier.

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arXiv2017

IFTTT vs. Zapier: A Comparative Study of Trigger-Action Programming Frameworks

The growing popularity of online services and IoT platforms along with increased developer's access to devices and services through RESTful APIs is giving rise to a new class of frameworks that support trigger-action programming. These frameworks provide an interface for end-users to bridge different RESTful APIs in a trigger-action model and easily create automated tasks across diverse platforms. Past work has characterized the space of user-created trigger-action combinations in the context of IFTTT, a popular trigger-action framework. In this work, we characterize the space of possible functionality that such frameworks open up to end-users in the context of two major frameworks -IFTTT and Zapier- and discuss results from our comparative analysis of these frameworks. We create a snapshot of 6406 triggers and actions from 1051 channels/apps across these two frameworks and compare the available functions, distribution of channels, and functions shared between them. We examine user's ability to define their own channels, triggers, and actions; analyze the growth of these frameworks; and discuss future research opportunities in this domain.

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arXiv2017

Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?

The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure. Building security into this new computing paradigm is a major technical challenge today. However, what are the security problems in IoT that we can solve using existing security principles? And, what are the new problems and challenges in this space that require new security mechanisms? This article summarizes the intellectual similarities and differences between classic information technology security research and IoT security research.

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arXiv2017

Decoupled-IFTTT: Constraining Privilege in Trigger-Action Platforms for the Internet of Things

Trigger-Action platforms are an emerging class of web-based systems that enable users to create automation rules (or recipes) of the form, "If there is a smoke alarm, then turn off my oven." These platforms stitch together various online services including Internet of Things devices, social networks, and productivity tools by obtaining OAuth tokens on behalf of users. Unfortunately, these platforms also introduce a long-term security risk: If they are compromised, the attacker can misuse the OAuth tokens belonging to millions of users to arbitrarily manipulate their devices and data. In this work, we first quantify the risk users face in the context of If-This-Then-That (IFTTT). We perform the first empirical analysis of the OAuth-based authorization model of IFTTT using semi-automated tools that we built to overcome the challenges of IFTTT's closed source nature and of online service API inconsistencies. We find that 75% of IFTTT's channels, an abstraction of online services, use overprivileged OAuth tokens, increasing risks in the event of a compromise. Even if the OAuth tokens were to be privileged correctly, IFTTT's compromise will not prevent their misuse. Motivated by this empirical analysis, we design and evaluate Decoupled-IFTTT (dIFTTT), the first trigger-action platform where users do not have to give it highly-privileged access to their online services. Our design pushes the notion of fine-grained OAuth tokens to its extreme and ensures that even if the cloud service is controlled by the attacker, it cannot misuse the OAuth tokens to invoke unauthorized actions. Our evaluation establishes that dIFTTT poses modest overhead: it adds less than 15ms of latency to recipe execution time, and reduces throughput by 2.5%.

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arXiv2014

Anception: Application Virtualization For Android

The problem of malware has become significant on Android devices. Library operating systems and application virtualization are both possible solutions for confining malware. Unfortunately, such solutions do not exist for Android. Designing mechanisms for application virtualization is a significant chal- lenge for several reasons: (1) graphics performance is important due to popularity of games and (2) applications with the same UID can share state. This paper presents Anception, the first flexible application virtualization framework for Android. It is imple- mented as a modification to the Android kernel and supports application virtualization that addresses the above requirements. Anception is able to confine many types of malware while supporting unmodified Android applications. Our Anception- based system exhibits up to 3.9% overhead on various 2D/3D benchmarks, and 1.8% overhead on the SunSpider benchmark.

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