DISTROY: Detecting Integrated Circuit Trojans with Compressive Measurements

Detecting Trojans in an integrated circuit (IC) is an important but hard problem. A Trojan is malicious hardware it can be extremely small in size and dormant until triggered by some unknown circuit state. To allow wake-up, a Trojan could draw a minimal amount of power, for example, to run a clock or a state machine, or to monitor a triggering event. We introduce DISTROY (Discover Trojan), a new approach that can effciently and reliably detect extremely small background power leakage that a Trojan creates and as a result, we can detect the Trojan. We formulate our method based on compressive sensing, a recent advance in signal processing, which can recover a signal using the number of measurements approximately proportional to its sparsity rather than size. We argue that circuit states in which the Trojan background power consumption stands out are rare, and thus sparse, so that we can apply compressive sensing. We describe how this is done in DISTROY so as to afford suffcient measurement statistics to detect the presence of Trojans. Finally, we present our initial simulation results that validate DISTROY and discuss the impact of our work in the field of hardware security.Engineering and Applied Science

Compressive Sensing with Optimal Sparsifying Basis and Applications in Spectrum Sensing

We describe a method of integrating Karhunen-Loève Transform (KLT) into compressive sensing, which can as a result improve the compression ratio without affecting the accuracy of decoding. We present two complementary results: 1) by using KLT to find an optimal basis for decoding we can drastically reduce the number of measurements for compressive sensing used in applications such as radio spectrum analysis; 2) by using compressive sensing we can estimate and recover the KLT basis from compressive measurements of an input signal. In particular, we propose CS-KLT, an online estimation algorithm to cope with nonstationarity of wireless channels in reality. We validate our results with empirical data collected from a wideband UHF spectrum and eld experiments to detect multiple radio transmitters, using software-defined radios.Engineering and Applied Science

Statistical screening for IC Trojan detection

We present statistical screening of test vectors for detecting a Trojan, malicious circuitry hidden inside an integrated circuit (IC). When applied a test vector, a Trojan-embedded chip draws extra leakage current that is unfortunately too small for the detector in most cases and concealed by process variation related to chip fabrication. To remedy the problem, we formulate a statistical approach that can screen and select test vectors in detecting Trojans. We validate our approach analytically and with gate-level simulations and show that our screening method leads to a substantial reduction in false positives and false negatives when detecting IC Trojans of various sizes.Engineering and Applied Science

Identifying Bad Measurements in Compressive Sensing

Abstract-We consider the problem of identifying bad measurements in compressive sensing. These bad measurements can be present due to malicious attacks and system malfunction. Since the system of linear equations in compressive sensing is underconstrained, errors introduced by these bad measurements can result in large changes in decoded solutions. We describe methods for identifying bad measurements so that they can be removed before decoding. In a new separation-based method we separate out top nonzero variables by ranking, eliminate the remaining variables from the system of equations, and then solve the reduced overconstrained problem to identify bad measurements. Comparing to prior methods based on direct or joint ℓ1-minimization, the separation-based method can work under a much smaller number of measurements. In analyzing the method we introduce the notion of inversions which governs the separability of large nonzero variables

A Chip Architecture for Compressive Sensing Based Detection of IC Trojans

We present a chip architecture for a compressive sensing based method that can be used in conjunction with the JTAG standard to detect IC Trojans. The proposed architecture compresses chip output resulting from a large number of test vectors applied to a circuit under test (CUT). We describe our designs in sensing leakage power, computing random linear combinations under compressive sensing, and piggybacking these new functionalities on JTAG. Our architecture achieves approximately a 10× speedup and 1000× reduction in output bandwidth while incurring a small area overhead.Engineering and Applied Science

Sign-based spectral clustering

Sign-based spectral clustering performs data grouping based on signs of components in the eigenvectors of the input. This paper introduces the concept of sign-based clustering, proves some of its basic properties and describes its use in applications. It is shown that for certain applications where a relatively small number of clusters are sought the sign-based approach can greatly simplify clustering by just examining the signs of components in the eigenvectors, while improving the speed and robustness of the clustering process. For other such applications, it can provide useful initial approximations in improving the performance of cluster searching heuristics such as k-means. 1

Compressive Sensing with Directly Recoverable Optimal Basis and Applications in Spectrum Sensing

We describe a method of integrating Karhunen-Loeve Transform (KLT) into compressive sensing, which can as a result leverage KLT’s optimality in revealing the sparsity of a signal. We present two complementary results: (1) by using the KLT to find the optimal basis for decoding we can drastically reduce the number of measurements for compressive sensing used in applications such as spectrum sensing; (2) by using compressive sensing we can compute the KLT basis directly from measurements of the input signal, with substantially fewer samples than the Nyquist rate. For a non-stationary signal, we suggest strategies in addressing the trade-off of incurring additional measurements for updating a KLT basis or compensating an obsolete KLT basis in signal recovery. We validate our results with field experiments to detect multiple radio transmitters and sense the UHF spectrums using software-defined radios.Engineering and Applied Science

CloudSense: Continuous Fine-Grain Cloud Monitoring With Compressive Sensing

Continuous fine-grain status monitoring of a cloud data center enables rapid response to anomalies, but handling the resulting torrent of data poses a significant challenge. As a solution, we propose CloudSense, a new switch design that performs in-network compression of status streams via compressive sensing. Using MapReduce straggler detection as an example of cloud monitoring, we give evidence that CloudSense allows earlier detection of stragglers, since finer-grain status can be reported for a given bandwidth budget. Furthermore, CloudSense showcases the advantage of an intrinsic property of compressive sensing decoding that enables detection of the slowest stragglers first. Finally, CloudSense achieves in-network compression via a low-complexity encoding scheme, which is easy and convenient to implement in a switch. We envision that CloudSense switches could form the foundation of a “compressed status information plane ” that is useful for monitoring not only the cloud data center itself, but also the user applications that it hosts.