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How Biometric Authentication Poses New Challenges to Our Security and Privacy

July 20, 2017

Nasir Memon

The use of biometric data—an individual’s measurable physical and behavioral characteristics—isn’t new. Government and law enforcement agencies have long used it. … Using biometric data to access our personal devices is increasing as a way to get around the limitations of the commonly used password-based mechanism: it’s easier, more convenient, and (theoretically) more secure. But biometric data can also be stolen and used in malicious ways.

Efficient Detection for Malicious and Random Errors in Additive Encrypted Computation

July 3, 2017

Nektarios Georgios Tsoutsos and Michail Maniatakos

Although data confidentiality is the primary security objective in additive encrypted computation applications, such as the aggregation of encrypted votes in electronic elections, ensuring the trustworthiness of data is equally important. And yet, integrity protections are generally orthogonal to additive homomorphic encryption, which enables efficient encrypted computation, due to the inherent malleability of homomorphic ciphertexts. Since additive homomorphic schemes are founded on modular arithmetic, our framework extends residue numbering to support fast modular reductions and homomorphic syndromes for detecting random errors inside homomorphic ALUs and data memories. In addition, our methodology detects malicious modifications of memory data, using keyed syndromes and block cipher-based integrity trees, which allow preserving the homomorphism of ALU operations, while enforcing non-malleability of memory data. Compared to traditional memory integrity protections, our tree-based syndrome generation and updating is parallelizable for increased efficiency, while requiring a small Trusted Computing Base for secret key storage and block cipher operations. Our evaluation shows more than 99.999% detection rate for random ALUs errors, as well as 100% detection rate of single bit-flips and clustered multiple bit upsets, for a runtime overhead between 1.2% and 5.5%, and a small area penalty.

Cyber– Physical Systems Security and Privacy

June 30, 2017

Guest Editors: Michail Maniatakos, Ramesh Karri and Alvaro A. Cardenas

During the past decade, several catch-phrases have been used to emphasize the increasing importance of cyber–physical systems (CPS) in our everyday life: Internet-of-Things, Internet-of-Everything, Smart-Cities, Smart-X, Intelligent-X, etc. All such systems, in their core, consist of networked computing (cyber) devices continuously interacting with the physical world. From fitness trackers and smart thermostats, to traffic light control and smart-grid devices, CPS have increased efficiency, enabled interesting applications and introduced major technological advancements. At the same time, due to their criticality, CPS have become a lucrative target for malicious actors. The wide deployment of CPS, as well as the increasing complexity of the underlying computing devices has increased the attack surface allowing a plethora of cyberattacks. The end-goal of the adversaries can be on the privacy side (e.g., leaking customer information), on the security side (e.g., causing a blackout), or both. Power and area constraints, as well as real-time requirements of CPS are limiting the defense capabilities of the computing devices.

SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud

June 30, 2017

Zahra Ghodsi, Tianyu Gu and Siddharth Garg

Inference using deep neural networks is often outsourced to the cloud since it is a computationally demanding task. However, this raises a fundamental issue of trust. How can a client be sure that the cloud has performed inference correctly? A lazy cloud provider might use a simpler but less accurate model to reduce its own computational load, or worse, maliciously modify the inference results sent to the client. We propose SafetyNets, a framework that enables an untrusted server (the cloud) to provide a client with a short mathematical proof of the correctness of inference tasks that they perform on behalf of the client. Specifically, SafetyNets develops and implements a specialized interactive proof (IP) protocol for verifiable execution of a class of deep neural networks, i.e., those that can be represented as arithmetic circuits. Our empirical results on three- and four-layer deep neural networks demonstrate the run-time costs of SafetyNets for both the client and server are low. SafetyNets detects any incorrect computations of the neural network by the untrusted server with high probability, while achieving state-of-the-art accuracy on the MNIST digit recognition (99.4%) and TIMIT speech recognition tasks (75.22%).

Throughput maximization of large-scale secondary networks over licensed and unlicensed spectra

June 29, 2017

Manjesh K. Hanawal, Yezekael Hayel and Quanyan Zhu.

Throughput of a mobile ad hoc network (MANET) operating on an unlicensed spectrum can increase if nodes can also transmit on a (shared) licensed spectrum. However, the transmissions on the licensed spectrum has to be limited to avoid degradation of quality of service (QoS) to primary users (PUs). We address the problem of how the nodes of a MANET or secondary users (SUs) should spread their transmissions on both licensed and unlicensed spectra to maximize network throughput, and characterize ‘throughput gain’ achieved in such spectrum sharing systems. We show that the gain can be significant and is increasing in the density of the SUs. The primary and secondary users are modeled as two independent Poisson point processes and their performance is evaluated using techniques from stochastic geometry.

IoT-enabled Distributed Cyber-attacks on Transmission and Distribution Grids.

June 22, 2017

Yury Dvorkin and Siddharth Garg

The Internet of things (IoT) will make it possible to interconnect and simultaneously control distributed electrical loads. Various technical and regulatory concerns have been raised that IoT-operated loads are being deployed without appropriately considering and systematically addressing potential cyber-security challenges. Hence, one can envision a hypothetical scenario when an ensemble of IoT-controlled loads can be hacked with malicious intentions of compromising operations of the electrical grid. Under this scenario, the attacker would use geographically distributed IoT-controlled loads to alternate their net power injections into the electrical grid in such a way that may disrupt normal grid operations.
This paper presents a modeling framework to analyze grid impacts of distributed cyber-attacks on IoT-controlled loads. This framework is used to demonstrate how a hypothetical distributed cyber-attack propagates from the distribution electrical grid, where IoT-controlled loads are expected to be installed, to the transmission electrical grid. The techno-economic interactions between the distribution and transmission electrical grids are accounted for by means of bilevel optimization. The case study is carried out on the modified versions of the 3-area IEEE Reliability Test System (RTS) and the IEEE 13-bus distribution feeder. Our numerical results demonstrate that the severity of such attacks depends on the penetration level of IoT-controlled loads and the strategy of the attacker.

ObfusCADe: Obfuscating Additive Manufacturing CAD Models Against Counterfeiting: Invited

June 22, 2017

Nikhil Gupta, Fei Chen,Nektarios Georgios Tsoutsos and Michail Maniatakos

As additive manufacturing (AM) becomes more pervasive, its supply chains shift towards distributed business models that heavily rely on cloud resources. Despite its countless benefits, this paradigm raises significant concerns about the trustworthiness of the globalized process, as there exist several classes of cybersecurity attacks that can undermine its security guarantees. In this work, we focus on the protection of the intellectual property (IP) of 3D designs, and introduce ObfusCADe, which is a novel protection method against counterfeiting, by embedding special features in CAD models. The introduced features interfere with the integrity of the design, effectively restricting high quality manufacturing to only a unique set of processing settings and conditions; under all other conditions, the printed artifact suffers from poor quality, premature failures and/or malfunctions.

Security as a Service for Cloud-Enabled Internet of Controlled Things under Advanced Persistent Threats: A Contract Design Approach

June 21, 2017

Juntao Chen and Quanyan Zhu

In this paper, we aim to establish a holistic framework that integrates the cyber-physical layers of a cloud-enabled Internet of Controlled Things (IoCT) through the lens of contract theory. At the physical layer, the device uses cloud services to operate the system. The quality of cloud services is unknown to the device, and hence the device designs a menu of contracts to enable a reliable and incentive-compatible service. Based on the received contracts, the cloud service provider (SP) serves the device by determining its optimal cyber defense strategy. A contract-based FlipCloud game is used to assess the security risk and the cloud quality of service (QoS) under advanced persistent threats. The contract design approach creates a pricing mechanism for on-demand security as a service for cloud-enabled IoCT. By focusing on high and low QoS types of cloud SPs, we find that the contract design can be divided into two regimes (regimes I and II) with respect to the provided cloud QoS. Specifically, the physical devices whose optimal contracts are in regime I always request the best possible cloud security service. In contrast, the device only asks for a cloud security level that can stabilize the system when the optimal contracts lie in regime II. We illustrate the obtained results via case studies of a cloud-enabled smart home.

TTLock: Tenacious and traceless logic locking

June 19, 2017

Muhammad Yasin, Bodhisatwa Mazumdar, Jeyavijayan J V Rajendran and Ozgur Sinanoglu

Logic locking is an intellectual property (IP) protection technique that prevents IP piracy, reverse engineering and overbuilding attacks by the untrusted foundry or endusers. Existing logic locking techniques are all vulnerable to various attacks, such as sensitization, key-pruning and signal skew analysis enabled removal attacks. In this paper, we propose TTLock that provably withstands all known attacks. TTLock protects a designer-specified number of input patterns, enabling a controlled and provably-secure trade-off between key-pruning attack resilience and removal attack resilience. All the key-bits converge on a single signal, creating maximal interference and thus resisting sensitization attacks. And, obfuscation is performed by modifying the design IP in a secret and traceless way, thwarting signal skew analysis and the removal attack it enables. Experimental results confirm our theoretical expectations that the computational complexity of attacks launched on TTLock grows exponentially with increasing key-size, while the area, power, and delay overhead increases only linearly.

On Mitigation of Side-Channel Attacks in 3D ICs: Decorrelating Thermal Patterns from Power and Activity

June 19, 2017

Johann Knechtel and Ozgur Sinanoglu

Various side-channel attacks (SCAs) on ICs have been successfully demonstrated and also mitigated to some degree. In the context of 3D ICs, however, prior art has mainly focused on efficient implementations of classical SCA countermeasures. That is, SCAs tailored for up-and-coming 3D ICs have been overlooked so far. In this paper, we conduct such a novel study and focus on one of the most accessible and critical side channels: thermal leakage of activity and power patterns. We address the thermal leakage in 3D ICs early on during floorplanning, along with tailored extensions for power and thermal management. Our key idea is to carefully exploit the specifics of material and structural properties in 3D ICs, thereby decorrelating the thermal behaviour from underlying power and activity patterns. Most importantly, we discuss powerful SCAs and demonstrate how our open-source tool helps to mitigate them.

Distributed Transfer Linear Support Vector Machines

June 15, 2017

Rui Zhang and Quanyan Zhu

Transfer learning has been developed to improve the performances of different but related tasks in machine learning. However, such processes become less efficient with the increase of the size of training data and the number of tasks. Moreover, privacy can be violated as some tasks may contain sensitive and private data, which are communicated between nodes and tasks. We propose a consensus-based distributed transfer learning framework, where several tasks aim to find the best linear support vector machine (SVM) classifiers in a distributed network. With alternating direction method of multipliers, tasks can achieve better classification accuracies more efficiently and privately, as each node and each task train with their own data, and only decision variables are transferred between different tasks and nodes. Numerical experiments on MNIST datasets show that the knowledge transferred from the source tasks can be used to decrease the risks of the target tasks that lack training data or have unbalanced training labels. We show that the risks of the target tasks in the nodes without the data of the source tasks can also be reduced using the information transferred from the nodes who contain the data of the source tasks. We also show that the target tasks can enter and leave in real-time without rerunning the whole algorithm.

A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization

June 8, 2017

Jeffrey Pawlick and Quanyan Zhu
Data ecosystems are becoming larger and more complex due to online tracking, wearable computing, and the Internet of Things. But privacy concerns are threatening to erode the potential benefits of these systems. Recently, users have developed obfuscation techniques that issue fake search engine queries, undermine location tracking algorithms, or evade government surveillance. Interestingly, these techniques raise two conflicts: one between each user and the machine learning algorithms which track the users, and one between the users themselves. In this paper, we use game theory to capture the first conflict with a Stackelberg game and the second conflict with a mean field game. We combine both into a dynamic and strategic bi-level framework which quantifies accuracy using empirical risk minimization and privacy using differential privacy. In equilibrium, we identify necessary and sufficient conditions under which 1) each user is incentivized to obfuscate if other users are obfuscating, 2) the tracking algorithm can avoid this by promising a level of privacy protection, and 3) this promise is incentive-compatible for the tracking algorithm.

Security and Privacy in Cyber-Physical Systems: A Survey of Surveys

May 29, 2017

Jairo Giraldo, Esha Sarkar, Alvaro Cardenas, Michail Maniatakos and Murat Kantarcioglu

Cyber-Physical Systems (CPS) are engineered systems combining computation, communications, and physical resources. Over the last decade—alongside technical advances in CPS—a vibrant and active community of security and privacy researchers have proposed and developed a mature research agenda addressing fundamental problems and risks of CPS deployments. The field has matured to a point where there are now several CPS security surveys. In this paper we highlight the diversity of research presenting by a meta-survey of CPS security and privacy surveys. Our goal is two-fold: first, we want to present newcomers to the field with an overview of the trends and main results in CPS security, and privacy; and secondly, we want to help established researchers in this field, identify other areas or domains where their cross-cutting principles can apply.

Under the Shadow of Sunshine: Understanding and Detecting Bulletproof Hosting on Legitimate Service Provider Networks

May 24, 2017

Sumayah Alrwais, Xiaojing Liao , Xianghang Mi , Peng Wang , XiaoFeng Wang , Feng Qian , Raheem Beyah and Damon McCoy

BulletProof Hosting (BPH) services provide criminal actors with technical infrastructure that is resilient to complaints of illicit activities, which serves as a basic building block for streamlining numerous types of attacks.In this paper, we present the first systematic study on this new trend of BPH services. By collecting and analyzing a large amount of data (25 Whois snapshots of the entire IPv4 address space, 1.5 TB of passive DNS data, and longitudinal data from several blacklist feeds), we are able to identify a set of new features that uniquely characterizes BPH on sub-allocations and are costly to evade. Based upon these features, we train a classifier for detecting malicious sub-allocated network blocks, achieving a 98% recall and 1.5% false discovery rates according to our evaluation. Using a conservatively trained version of our classifier, we scan the whole IPv4 address space and detect 39K malicious network blocks.

To Catch a Ratter: Monitoring the Behavior of Amateur DarkComet RAT Operators in the Wild

May 23, 2017

Brown Farinholt , Mohammad Rezaeirad , Paul Pearce , Hitesh Dharmdasani, Haikuo Yin Stevens Le Blondk , Damon McCoy, Kirill Levchenko

Remote Access Trojans (RATs) give remote attackers interactive control over a compromised machine. Unlike largescale malware such as botnets, a RAT is controlled individually by a human operator interacting with the compromised machine remotely. The versatility of RATs makes them attractive to actors of all levels of sophistication: they’ve been used for espionage, information theft, voyeurism and extortion. Despite their increasing use, there are still major gaps in our understanding of RATs and their operators, including motives, intentions, procedures, and weak points where defenses might be most effective. In this work we study the use of DarkComet, a popular commercial RAT.

DeepMasterPrint: Generating Fingerprints for Presentation Attacks

May 21, 2017

Philip Bontrager, Julian Togelius and Nasir Memon

We present two related methods for creating MasterPrints, synthetic fingerprints that a fingerprint verification system identifies as many different people. Both methods start with training a Generative Adversarial Network (GAN) on a set of real fingerprint images. The generator network is then used to search for images that can be recognized as multiple individuals. The first method uses evolutionary optimization in the space of latent variables, and the second uses gradient-based search. Our method is able to design a MasterPrint that a commercial fingerprint system matches to 22% of all users in a strict security setting, and 75% of all users at a looser security setting.

Malicious firmware detection with hardware performance counters

May 17, 2017

Xueyang Wang, Charalambos Konstantinou, Michail Maniatakos, Ramesh Karri, Serena Lee, Patricia Robison, Paul Stergiou, and Steve Kim

Critical infrastructure components nowadays use microprocessor-based embedded control systems. It is often infeasible, however, to employ the same level of security measures used in general purpose computing systems, due to the stringent performance and resource constraints of embedded control systems. Furthermore, as software sits atop and relies on the firmware for proper operation, software-level techniques cannot detect malicious behavior of the firmware.


BandiTS: Dynamic timing speculation using multi-armed bandit based optimization

May 15, 2017

Jeff Jun Zhang and Siddharth Garg

Timing speculation has recently been proposed as a method for increasing performance beyond that achievable by conventional worst-case design techniques. Starting with the observation of fast temporal variations in timing error probabilities, we propose a run-time technique to dynamically determine the optimal degree of timing speculation (i.e., how aggressively the processor is over-clocked) based on a novel formulation of the dynamic timing speculation problem as a multi-armed bandit problem.