Trojan

About: Trojan is a research topic. Over the lifetime, 2028 publications have been published within this topic receiving 33209 citations.

Detecting hardware trojans using backside optical imaging of embedded watermarks

Abstract: Hardware Trojans are a critical security threat to integrated circuits. We propose an optical method to detect and localize Trojans inserted during the chip fabrication stage. We engineer the fill cells in a standard cell library to be highly reflective at near-IR wavelengths so that they can be readily observed in an optical image taken through the backside of the chip. The pattern produced by their locations produces an easily measured watermark of the circuit layout. Replacement, modification or re-arrangement of these cells to add a Trojan can therefore be detected through rapid post-fabrication backside imaging. We evaluate our approach using various hardware blocks where the Trojan circuit area is less than 0.1% of the total area and it consumes less than 2% leakage power of the entire chip. In addition, we evaluate the tolerance of our methodology to background measurement noise and process variation.

The Dynamics of the Trojan Asteroids

Abstract: The state of the art in the dynamics of the Trojan asteroids has progressed rapidly, since it has been possible to perform numerical integrations of many orbits for millions of years. Accurate proper elements are now computed by the synthetic method, that is from the output of a numerical integration; their stability, at least for time spans of a few million years, is good. This has allowed identification of Trojan families with an automated procedure closely mimicking the one used in the main belt. Although the families identified in a reliable way are only four, the occurrence of significant collisional evolution, not unlike that of the main belt, can be confirmed. The dynamical structure of the Trojan cloud, including the location of the main secular resonances, can be deduced from the proper elements and frequencies by a simple fit. However, many problems are not solved : the origin of a significant percentage of chaotic orbits showing no indications of instability; the location of the stability boundary of the Trojan cloud; the origin of the high inclination of most Trojans. We also do not know if there are “Trojans” for some other planets beside Jupiter : only one Mars Trojan has been found.

Solar System objects observed with TESS -- First data release: bright main-belt and Trojan asteroids from the Southern Survey

Abstract: Compared with previous space-borne surveys, the Transiting Exoplanet Survey Satellite (TESS) provides a unique and new approach to observe Solar System objects. While its primary mission avoids the vicinity of the ecliptic plane by approximately six degrees, the scale height of the Solar System debris disk is large enough to place various small body populations in the field-of-view. In this paper we present the first data release of photometric analysis of TESS observations of small Solar System Bodies, focusing on the bright end of the observed main-belt asteroid and Jovian Trojan populations. This data release, named TSSYS-DR1, contains 9912 light curves obtained and extracted in a homogeneous manner, and triples the number of bodies with unambiguous fundamental rotation characteristics, namely where accurate periods and amplitudes are both reported. Our catalogue clearly shows that the number of bodies with long rotation periods are definitely underestimated by all previous ground-based surveys, by at least an order of magnitude.

Detecting AI Trojans Using Meta Neural Analysis

Abstract: In machine learning Trojan attacks, an adversary trains a corrupted model that obtains good performance on normal data but behaves maliciously on data samples with certain trigger patterns. Several approaches have been proposed to detect such attacks, but they make undesirable assumptions about the attack strategies or require direct access to the trained models, which restricts their utility in practice.This paper addresses these challenges by introducing a Meta Neural Trojan Detection (MNTD) pipeline that does not make assumptions on the attack strategies and only needs black-box access to models. The strategy is to train a meta-classifier that predicts whether a given target model is Trojaned. To train the meta-model without knowledge of the attack strategy, we introduce a technique called jumbo learning that samples a set of Trojaned models following a general distribution. We then dynamically optimize a query set together with the meta-classifier to distinguish between Trojaned and benign models.We evaluate MNTD with experiments on vision, speech, tabular data and natural language text datasets, and against different Trojan attacks such as data poisoning attack, model manipulation attack, and latent attack. We show that MNTD achieves 97% detection AUC score and significantly outperforms existing detection approaches. In addition, MNTD generalizes well and achieves high detection performance against unforeseen attacks. We also propose a robust MNTD pipeline which achieves around 90% detection AUC even when the attacker aims to evade the detection with full knowledge of the system.

Detection of a Trailing (L5) Neptune Trojan

Abstract: The orbits of small Solar System bodies record the history of our Solar System. Here, we report the detection of 2008 LC18, which is a Neptune Trojan in the trailing (L5) Lagrangian region of gravitational equilibrium within Neptune's orbit. We estimate that the leading and trailing Neptune Trojan regions have similarly sized populations and dynamics, with both regions dominated by high-inclination objects. Similar populations and dynamics at both Neptune Lagrangian regions indicate that the Trojans were likely captured by a migrating, eccentric Neptune in a dynamically excited planetesimal population.