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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.

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.

Inspiring trust in outsourced integrated circuit fabrication

May 15, 2017

Siddharth Garg

The fabrication of integrated circuits (ICs) is typically outsourced to an external semiconductor foundry to reduce cost. However, this can come at the expense of trust. How can a designer ensure the integrity of the ICs fabricated by an external foundry? The talk will discuss a new approach for inspiring trust in outsourced IC fabrication by complementing the untrusted (outsourced) with an IC fabricated at a low-end but trusted foundry. This approach is referred to as split fabrication. We present two different ways in which split fabrication can be used to enhance security: logic obfuscation and verifiable ASICs.

A game-theoretic analysis of label flipping attacks on distributed support vector machines

May 15, 2017

Rui Zhang and Quanyan Zhu

Distributed machine learning algorithms play a significant role in processing massive data sets over large networks. However, the increasing reliance on machine learning on information and communication technologies makes it inherently vulnerable to cyber threats. This work aims to develop secure distributed algorithms to protect the learning from adversaries. We establish a game-theoretic framework to capture the conflicting goals of a learner who uses distributed support vector machines (DSVM) and an attacker who is capable of flipping training labels.

Minimax robust optimal control of multiscale linear-quadratic systems

May 15, 2017

Hamza Anwar and Quanyan Zhu

With a growing system complexity in the IoT framework, many networked cyber-physical systems work in a hierarchical fashion. Layers of information outputs and command inputs are available. An active area of research is in optimizing the design of policies and control command that influence information flow for such multi-layered systems. Our focus in current research is to first formulate the control command flow for hierarchical systems in the form of multiscale state-space models on a tree, and then the design of an optimal control law under constraints that relate the states of information across the system layers. We propose a game-theoretic formulation of a robust optimal controller for the broad class of multiscale systems having underlying hierarchical structure.

What to Lock?: Functional and Parametric Locking

May 12, 2017

Muhammad Yasin, Abhrajit Sengupta, Benjamin Carrion Schafer, Yiorgos Makris, Ozgur Sinanoglu and Jeyavijayan (JV) Rajendran

Logic locking is an intellectual property (IP) protection technique that prevents IP piracy, reverse engineering and overbuilding attacks by the untrusted foundry or end-users. Existing logic locking techniques are all based on locking the functionality; the design/chip is nonfunctional unless the secret key has been loaded. Existing techniques are vulnerable to various attacks, such as sensitization, key-pruning, and signal skew analysis enabled removal attacks. In this paper, we propose a tenacious and traceless logic locking technique, TTlock, that locks functionality and provably withstands all known attacks, such as SAT-based, sensitization, removal, etc. TTLock protects a secret input pattern; the output of a logic cone is flipped for that pattern, where this flip is restored only when the correct key is applied. 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.

The Need for Declarative Properties in Digital IC Security

May 12, 2017

Mohamed El Massad, Frank Imeson, Siddharth Garg and Mahesh Tripunitara.

We emphasize the need to articulate precise, declarative properties in the context of securing Digital ICs. We do this by discussing two pieces of our work on securing Digital ICs. In one, we discuss a seemingly compelling approach to protecting Intellectual Property — IC camouflaging. We demonstrate that an adversary can carry out a decamouflaging attack, in practice, much more efficiently than previously thought. Underlying our attack is strong foundations: an identification of the computational-complexity of the problems an attacker faces, and how they can be addressed using off-the-shelf constraint solvers. We identify the lack of a precise characterization of “security” in this context as an issue. In the other piece of work, we present an example of the articulation of such a security property for 3D IC technology, in the context of securing a supply-chain. The property is articulated declaratively, with explicit assumptions that underlie the threat model.

On the Difficulty of Inserting Trojans in Reversible Computing Architectures

May 2, 2017

Xiaotong Cui, Samah Saeed, Alwin Zulehner, Robert Wille, Rolf Drechsler, Kaijie Wu and Ramesh Karri

Fabrication-less design houses outsource their designs to 3rd party foundries to lower fabrication cost. However, this creates opportunities for a rogue in the foundry to introduce hardware Trojans, which stay inactive most of the time and cause unintended consequences to the system when triggered. Hardware Trojans in traditional CMOS-based circuits have been studied and Design-for-Trust (DFT) techniques have been proposed to detect them.
Different from traditional circuits in many ways, reversible circuits implement one-to-one, bijective input/output mappings. We will investigate the security implications of reversible circuits with a particular focus on susceptibility to hardware Trojans. We will consider inherently reversible circuits and non-reversible functions embedded in reversible circuits.

Proactive Population-Risk Based Defense Against Denial of Cyber-Physical Service Attacks

May 1, 2017

Jeffrey Pawlick and Quanyan Zhu

—While the Internet of things (IoT) promises to improve areas such as energy efficiency, health care, and transportation, it is highly vulnerable to cyberattacks. In particular, DDoS attacks work by overflowing the bandwidth of a server. But many IoT devices form part of cyber-physical systems (CPS). Therefore, they can be used to launch a “physical” denial-ofservice attack (PDoS) in which IoT devices overflow the “physical bandwidth” of a CPS. In this paper, we quantify the populationbased risk to a group of IoT devices targeted by malware for a PDoS attack. To model the recruitment of bots, we extend a traditional game-theoretic concept and create a “Poisson signaling game.” Then we analyze two different mechanisms (legal and economic) to deter botnet recruitment.