arXiv subjects

Patrick McDaniel

Publications and source records attributed to Patrick McDaniel.

At least 55 records · Page 3Linked to original sources

arXiv2022

Adversarial Plannning

Planning algorithms are used in computational systems to direct autonomous behavior. In a canonical application, for example, planning for autonomous vehicles is used to automate the static or continuous planning towards performance, resource management, or functional goals (e.g., arriving at the destination, managing fuel fuel consumption). Existing planning algorithms assume non-adversarial settings; a least-cost plan is developed based on available environmental information (i.e., the input instance). Yet, it is unclear how such algorithms will perform in the face of adversaries attempting to thwart the planner. In this paper, we explore the security of planning algorithms used in cyber- and cyber-physical systems. We present two $\textit{adversarial planning}$ algorithms-one static and one adaptive-that perturb input planning instances to maximize cost (often substantially so). We evaluate the performance of the algorithms against two dominant planning algorithms used in commercial applications (D* Lite and Fast Downward) and show both are vulnerable to extremely limited adversarial action. Here, experiments show that an adversary is able to increase plan costs in 66.9% of instances by only removing a single action from the actions space (D* Lite) and render 70% of instances from an international planning competition unsolvable by removing only three actions (Fast Forward). Finally, we show that finding an optimal perturbation in any search-based planning system is NP-hard.

cs.CR↗

arXiv2022

Measuring and Mitigating the Risk of IP Reuse on Public Clouds

Public clouds provide scalable and cost-efficient computing through resource sharing. However, moving from traditional on-premises service management to clouds introduces new challenges; failure to correctly provision, maintain, or decommission elastic services can lead to functional failure and vulnerability to attack. In this paper, we explore a broad class of attacks on clouds which we refer to as cloud squatting. In a cloud squatting attack, an adversary allocates resources in the cloud (e.g., IP addresses) and thereafter leverages latent configuration to exploit prior tenants. To measure and categorize cloud squatting we deployed a custom Internet telescope within the Amazon Web Services us-east-1 region. Using this apparatus, we deployed over 3 million servers receiving 1.5 million unique IP addresses (56% of the available pool) over 101 days beginning in March of 2021. We identified 4 classes of cloud services, 7 classes of third-party services, and DNS as sources of exploitable latent configurations. We discovered that exploitable configurations were both common and in many cases extremely dangerous; we received over 5 million cloud messages, many containing sensitive data such as financial transactions, GPS location, and PII. Within the 7 classes of third-party services, we identified dozens of exploitable software systems spanning hundreds of servers (e.g., databases, caches, mobile applications, and web services). Lastly, we identified 5446 exploitable domains spanning 231 eTLDs-including 105 in the top 10,000 and 23 in the top 1000 popular domains. Through tenant disclosures we have identified several root causes, including (a) a lack of organizational controls, (b) poor service hygiene, and (c) failure to follow best practices. We conclude with a discussion of the space of possible mitigations and describe the mitigations to be deployed by Amazon in response to this study.

cs.CR↗

arXiv2022

A Machine Learning and Computer Vision Approach to Geomagnetic Storm Forecasting

Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of charged particles being emitted from the Sun, are an uncontrollable threat to modern technology. Notably, they have the potential to damage satellites and cause instability in power grids on Earth, among other disasters. They result from high sun activity, which are induced from cool areas on the Sun known as sunspots. Forecasting the storms to prevent disasters requires an understanding of how and when they will occur. However, current prediction methods at the National Oceanic and Atmospheric Administration (NOAA) are limited in that they depend on expensive solar wind spacecraft and a global-scale magnetometer sensor network. In this paper, we introduce a novel machine learning and computer vision approach to accurately forecast geomagnetic storms without the need of such costly physical measurements. Our approach extracts features from images of the Sun to establish correlations between sunspots and geomagnetic storm classification and is competitive with NOAA's predictions. Indeed, our prediction achieves a 76% storm classification accuracy. This paper serves as an existence proof that machine learning and computer vision techniques provide an effective means for augmenting and improving existing geomagnetic storm forecasting methods.

cs.LG↗

arXiv2022

Generating Practical Adversarial Network Traffic Flows Using NIDSGAN

Network intrusion detection systems (NIDS) are an essential defense for computer networks and the hosts within them. Machine learning (ML) nowadays predominantly serves as the basis for NIDS decision making, where models are tuned to reduce false alarms, increase detection rates, and detect known and unknown attacks. At the same time, ML models have been found to be vulnerable to adversarial examples that undermine the downstream task. In this work, we ask the practical question of whether real-world ML-based NIDS can be circumvented by crafted adversarial flows, and if so, how can they be created. We develop the generative adversarial network (GAN)-based attack algorithm NIDSGAN and evaluate its effectiveness against realistic ML-based NIDS. Two main challenges arise for generating adversarial network traffic flows: (1) the network features must obey the constraints of the domain (i.e., represent realistic network behavior), and (2) the adversary must learn the decision behavior of the target NIDS without knowing its model internals (e.g., architecture and meta-parameters) and training data. Despite these challenges, the NIDSGAN algorithm generates highly realistic adversarial traffic flows that evade ML-based NIDS. We evaluate our attack algorithm against two state-of-the-art DNN-based NIDS in whitebox, blackbox, and restricted-blackbox threat models and achieve success rates which are on average 99%, 85%, and 70%, respectively. We also show that our attack algorithm can evade NIDS based on classical ML models including logistic regression, SVM, decision trees and KNNs, with a success rate of 70% on average. Our results demonstrate that deploying ML-based NIDS without careful defensive strategies against adversarial flows may (and arguably likely will) lead to future compromises.

cs.CR↗

arXiv2022

Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations

One of the principal uses of physical-space sensors in public safety applications is the detection of unsafe conditions (e.g., release of poisonous gases, weapons in airports, tainted food). However, current detection methods in these applications are often costly, slow to use, and can be inaccurate in complex, changing, or new environments. In this paper, we explore how machine learning methods used successfully in cyber domains, such as malware detection, can be leveraged to substantially enhance physical space detection. We focus on one important exemplar application--the detection and localization of radioactive materials. We show that the ML-based approaches can significantly exceed traditional table-based approaches in predicting angular direction. Moreover, the developed models can be expanded to include approximations of the distance to radioactive material (a critical dimension that reference tables used in practice do not capture). With four and eight detector arrays, we collect counts of gamma-rays as features for a suite of machine learning models to localize radioactive material. We explore seven unique scenarios via simulation frameworks frequently used for radiation detection and with physical experiments using radioactive material in laboratory environments. We observe that our approach can outperform the standard table-based method, reducing the angular error by 37% and reliably predicting distance within 2.4%. In this way, we show that advances in cyber-detection provide substantial opportunities for enhancing detection in public safety applications and beyond.

cs.LG↗

arXiv2022

HoneyModels: Machine Learning Honeypots

Machine Learning is becoming a pivotal aspect of many systems today, offering newfound performance on classification and prediction tasks, but this rapid integration also comes with new unforeseen vulnerabilities. To harden these systems the ever-growing field of Adversarial Machine Learning has proposed new attack and defense mechanisms. However, a great asymmetry exists as these defensive methods can only provide security to certain models and lack scalability, computational efficiency, and practicality due to overly restrictive constraints. Moreover, newly introduced attacks can easily bypass defensive strategies by making subtle alterations. In this paper, we study an alternate approach inspired by honeypots to detect adversaries. Our approach yields learned models with an embedded watermark. When an adversary initiates an interaction with our model, attacks are encouraged to add this predetermined watermark stimulating detection of adversarial examples. We show that HoneyModels can reveal 69.5% of adversaries attempting to attack a Neural Network while preserving the original functionality of the model. HoneyModels offer an alternate direction to secure Machine Learning that slightly affects the accuracy while encouraging the creation of watermarked adversarial samples detectable by the HoneyModel but indistinguishable from others for the adversary.

cs.CR↗

arXiv2022

Building a Privacy-Preserving Smart Camera System

Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such as service providers (e.g., the cloud). Third parties therefore can (and in some instances have) access the video footage without the users' knowledge or consent -- violating the core tenet of user privacy. In this paper, we present CaCTUs, a privacy-preserving smart Camera system Controlled Totally by Users. CaCTUs returns control to the user; the root of trust begins with the user and is maintained through a series of cryptographic protocols, designed to support popular features, such as sharing, deleting, and viewing videos live. We show that the system can support live streaming with a latency of 2s at a frame rate of 10fps and a resolution of 480p. In so doing, we demonstrate that it is feasible to implement a performant smart-camera system that leverages the convenience of a cloud-based model while retaining the ability to control access to (private) data.

cs.CR↗

arXiv2021

On the Robustness of Domain Constraints

Machine learning is vulnerable to adversarial examples-inputs designed to cause models to perform poorly. However, it is unclear if adversarial examples represent realistic inputs in the modeled domains. Diverse domains such as networks and phishing have domain constraints-complex relationships between features that an adversary must satisfy for an attack to be realized (in addition to any adversary-specific goals). In this paper, we explore how domain constraints limit adversarial capabilities and how adversaries can adapt their strategies to create realistic (constraint-compliant) examples. In this, we develop techniques to learn domain constraints from data, and show how the learned constraints can be integrated into the adversarial crafting process. We evaluate the efficacy of our approach in network intrusion and phishing datasets and find: (1) up to 82% of adversarial examples produced by state-of-the-art crafting algorithms violate domain constraints, (2) domain constraints are robust to adversarial examples; enforcing constraints yields an increase in model accuracy by up to 34%. We observe not only that adversaries must alter inputs to satisfy domain constraints, but that these constraints make the generation of valid adversarial examples far more challenging.

cs.CR↗

arXiv2020

MLSNet: A Policy Complying Multilevel Security Framework for Software Defined Networking

Ensuring that information flowing through a network is secure from manipulation and eavesdropping by unauthorized parties is an important task for network administrators. Many cyber attacks rely on a lack of network-level information flow controls to successfully compromise a victim network. Once an adversary exploits an initial entry point, they can eavesdrop and move laterally within the network (e.g., scan and penetrate internal nodes) to further their malicious goals. In this paper, we propose a novel multilevel security (MLS) framework to enforce a secure inter-node information flow policy within the network and therein vastly reduce the attack surface available to an adversary who has penetrated it. In contrast to prior work on multilevel security in computer networks which relied on enforcing the policy at network endpoints, we leverage the centralization of software-defined networks (SDNs) by moving the task to the controller and providing this service transparently to all nodes in the network. Our framework, MLSNet, formalizes the generation of a policy compliant network configuration (i.e., set of flow rules on the SDN switches) as network optimization problems, with the objectives of (1) maximizing the number of flows satisfying all security constraints and (2) minimizing the security cost of routing any remaining flows to guarantee availability. We demonstrate that MLSNet can securely route flows that satisfy the security constraints (e.g., >80% of flows in a performed benchmark) and route the remaining flows with a minimal security cost.

cs.NI↗

arXiv2020

KRATOS: Multi-User Multi-Device-Aware Access Control System for the Smart Home

In a smart home system, multiple users have access to multiple devices, typically through a dedicated app installed on a mobile device. Traditional access control mechanisms consider one unique trusted user that controls the access to the devices. However, multi-user multi-device smart home settings pose fundamentally different challenges to traditional single-user systems. For instance, in a multi-user environment, users have conflicting, complex, and dynamically changing demands on multiple devices, which cannot be handled by traditional access control techniques. To address these challenges, in this paper, we introduce Kratos, a novel multiuser and multi-device-aware access control mechanism that allows smart home users to flexibly specify their access control demands. Kratos has three main components: user interaction module, backend server, and policy manager. Users can specify their desired access control settings using the interaction module which are translated into access control policies in the backend server. The policy manager analyzes these policies and initiates negotiation between users to resolve conflicting demands and generates final policies. We implemented Kratos and evaluated its performance on real smart home deployments featuring multi-user scenarios with a rich set of configurations (309 different policies including 213 demand conflicts and 24 restriction policies). These configurations included five different threats associated with access control mechanisms. Our extensive evaluations show that Kratos is very effective in resolving conflicting access control demands with minimal overhead and robust against different attacks.

cs.CR↗

arXiv2020

Ensemble Adversarial Training: Attacks and Defenses

Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model's loss. We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss. The model thus learns to generate weak perturbations, rather than defend against strong ones. As a result, we find that adversarial training remains vulnerable to black-box attacks, where we transfer perturbations computed on undefended models, as well as to a powerful novel single-step attack that escapes the non-smooth vicinity of the input data via a small random step. We further introduce Ensemble Adversarial Training, a technique that augments training data with perturbations transferred from other models. On ImageNet, Ensemble Adversarial Training yields models with strong robustness to black-box attacks. In particular, our most robust model won the first round of the NIPS 2017 competition on Defenses against Adversarial Attacks. However, subsequent work found that more elaborate black-box attacks could significantly enhance transferability and reduce the accuracy of our models.

stat.ML↗

arXiv2020

Computational-Experimental Investigation of a Fission Thermal Probe in TREAT

The development of nuclear fuels requires unsteady/transient testing for design process and qualification under postulated accident conditions. Breach, rupture, fracture, melting, and other fuel failure modes may occur during the use of nuclear fuels or when they are exposed to extreme overpower conditions. Therefore, it is crucial for the fuel to retain reasonable structural integrity and coolable geometry. The experimental facility of Transient Reactor Test (TREAT) was designed and constructed in November 1958 to conduct transient testing of fuels and structural materials. The magnitude of nuclear heating is one of the most important key parameters in test design, analysis, and data interpretation in TREAT. Some steady-state tests are able to measure heating directly via enthalpy rise of coolant in thermally-isolated loops, but an enormous number of important tests must rely upon nuclear modeling to correlate core operating parameters to specimen nuclear heating. Uncertainties of these models and their inputs can prevent experimenters from achieving desired conditions and hamper advanced models from describing the phenomena to the level of confidence needed. The situation can be even more difficult for unsteady/transient tests where nuclear heating is intentionally varied over short time scales. This research develops a novel nuclear heating sensor technology which directly measures the parameters of interest using spatially-resolved real-time thermometry of fissionable instrument materials, demonstrate the instruments' use in TREAT, and perform data comparisons to nuclear models to facilitate an advanced understanding of transient power coupling. Here we present our results of the designed probe and its testing outcome.

physics.ins-det↗

arXiv2020

IoTRepair: Systematically Addressing Device Faults in Commodity IoT (Extended Paper)

IoT devices are decentralized and deployed in un-stable environments, which causes them to be prone to various kinds of faults, such as device failure and network disruption. Yet, current IoT platforms require programmers to handle faults manually, a complex and error-prone task. In this paper, we present IoTRepair, a fault-handling system for IoT that (1)integrates a fault identification module to track faulty devices,(2) provides a library of fault-handling functions for effectively handling different fault types, (3) provides a fault handler on top of the library for autonomous IoT fault handling, with user and developer configuration as input. Through an evaluation in a simulated lab environment and with various fault injectio nmethods,IoTRepair is compared with current fault-handling solutions. The fault handler reduces the incorrect states on average 50.01%, which corresponds to less unsafe and insecure device states. Overall, through a systematic design of an IoT fault handler, we provide users flexibility and convenience in handling complex IoT fault handling, allowing safer IoT environments.

cs.SE↗

arXiv2019

Real-time Analysis of Privacy-(un)aware IoT Applications

Users trust IoT apps to control and automate their smart devices. These apps necessarily have access to sensitive data to implement their functionality. However, users lack visibility into how their sensitive data is used (or leaked), and they often blindly trust the app developers. In this paper, we present IoTWatcH, a novel dynamic analysis tool that uncovers the privacy risks of IoT apps in real-time. We designed and built IoTWatcH based on an IoT privacy survey that considers the privacy needs of IoT users. IoTWatcH provides users with a simple interface to specify their privacy preferences with an IoT app. Then, in runtime, it analyzes both the data that is sent out of the IoT app and its recipients using Natural Language Processing (NLP) techniques. Moreover, IoTWatcH informs the users with its findings to make them aware of the privacy risks with the IoT app. We implemented IoTWatcH on real IoT applications. Specifically, we analyzed 540 IoT apps to train the NLP model and evaluate its effectiveness. IoTWatcH successfully classifies IoT app data sent to external parties to correct privacy labels with an average accuracy of 94.25%, and flags IoT apps that leak privacy data to unauthorized parties. Finally, IoTWatcH yields minimal overhead to an IoT app's execution, on average 105 ms additional latency.

cs.CR↗

arXiv2019

How Relevant is the Turing Test in the Age of Sophisbots?

Popular culture has contemplated societies of thinking machines for generations, envisioning futures from utopian to dystopian. These futures are, arguably, here now-we find ourselves at the doorstep of technology that can at least simulate the appearance of thinking, acting, and feeling. The real question is: now what?

cs.CY↗

arXiv2019

Curie: Policy-based Secure Data Exchange

Data sharing among partners---users, organizations, companies---is crucial for the advancement of data analytics in many domains. Sharing through secure computation and differential privacy allows these partners to perform private computations on their sensitive data in controlled ways. However, in reality, there exist complex relationships among members. Politics, regulations, interest, trust, data demands and needs are one of the many reasons. Thus, there is a need for a mechanism to meet these conflicting relationships on data sharing. This paper presents Curie, an approach to exchange data among members whose membership has complex relationships. The CPL policy language that allows members to define the specifications of data exchange requirements is introduced. Members (partners) assert who and what to exchange through their local policies and negotiate a global sharing agreement. The agreement is implemented in a multi-party computation that guarantees sharing among members will comply with the policy as negotiated. The use of Curie is validated through an example of a health care application built on recently introduced secure multi-party computation and differential privacy frameworks, and policy and performance trade-offs are explored.

cs.CR↗

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.

cs.CR↗

arXiv2018

More or Less? Predict the Social Influence of Malicious URLs on Social Media

Users of Online Social Networks (OSNs) interact with each other more than ever. In the context of a public discussion group, people receive, read, and write comments in response to articles and postings. In the absence of access control mechanisms, OSNs are a great environment for attackers to influence others, from spreading phishing URLs, to posting fake news. Moreover, OSN user behavior can be predicted by social science concepts which include conformity and the bandwagon effect. In this paper, we show how social recommendation systems affect the occurrence of malicious URLs on Facebook. We exploit temporal features to build a prediction framework, having greater than 75% accuracy, to predict whether the following group users' behavior will increase or not. Included in this work, we demarcate classes of URLs, including those malicious URLs classified as creating critical damage, as well as those of a lesser nature which only inflict light damage such as aggressive commercial advertisements and spam content. It is our hope that the data and analyses in this paper provide a better understanding of OSN user reactions to different categories of malicious URLs, thereby providing a way to mitigate the influence of these malicious URL attacks.

cs.SI↗