Trojan

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

The olivine-dominated composition of the Eureka family of Mars Trojan asteroids

Abstract: We have used the XSHOOTER echelle spectrograph on the European Southern Obseratory (ESO) Very Large Telescope (VLT) to obtain UVB-VIS-NIR (ultraviolet-blue (UVB), visible (VIS) and near-infrared (NIR)) reflectance spectra of two members of the Eureka family of L5 Mars Trojans, in order to test a genetic relationship to Eureka. In addition to obtaining spectra, we also carried out VRI photometry of one of the VLT targets using the 2-m telescope at the Bulgarian National Astronomical Observatory - Rozhen and the two-channel focal reducer. We found that these asteroids belong to the olivine-dominated A, or Sa, taxonomic class. As Eureka itself is also an olivine-dominated asteroid, it is likely that all family asteroids share a common origin and composition. We discuss the significance of these results in terms of the origin of the martian Trojan population.

The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models.

Abstract: In this paper, we introduce the TrojAI software framework, an open source set of Python tools capable of generating triggered (poisoned) datasets and associated deep learning (DL) models with trojans at scale. We utilize the developed framework to generate a large set of trojaned MNIST classifiers, as well as demonstrate the capability to produce a trojaned reinforcement-learning model using vector observations. Results on MNIST show that the nature of the trigger, training batch size, and dataset poisoning percentage all affect successful embedding of trojans. We test Neural Cleanse against the trojaned MNIST models and successfully detect anomalies in the trained models approximately $18\%$ of the time. Our experiments and workflow indicate that the TrojAI software framework will enable researchers to easily understand the effects of various configurations of the dataset and training hyperparameters on the generated trojaned deep learning model, and can be used to rapidly and comprehensively test new trojan detection methods.

A unified submodular framework for multimodal IC Trojan detection

Abstract: This paper presents a unified formal framework for integrated circuits (IC) Trojan detection that can simultaneously employ multiple noninvasive measurement types. Hardware Trojans refer to modifications, alterations, or insertions to the original IC for adversarial purposes. The new framework formally defines the IC Trojan detection for each measurement type as an optimization problem and discusses the complexity. A formulation of the problem that is applicable to a large class of Trojan detection problems and is submodular is devised. Based on the objective function properties, an efficient Trojan detection method with strong approximation and optimality guarantees is introduced. Signal processing methods for calibrating the impact of interchip and intra-chip correlations are presented. We propose a number of methods for combining the detections of the different measurement types. Experimental evaluations on benchmark designs reveal the low-overhead and effectiveness of the new Trojan detection framework and provides a comparison of different detection combining methods.

A Study of Trojan Propagation in Online Social Networks

Abstract: Online Social Networks (OSNs) are generally based on real social relations. Hence, malware writers are taking advantage of this fact to propagate their viral code into OSNs. In recent years, major OSNs, such as Facebook, were extensively under malware attacks. These attacks commonly lead to hundreds of thousands of compromised accounts that may bear personal and even confidential information. In this paper, different types of malware in OSNs are discussed. Then, this paper investigates the attacking vector of the Trojan type malware in OSNs. First, the clustering coefficient which is one of the main OSN graph characteristics is examined through simulation. It is shown that the clustering coefficient has a linear effect on the speed of Trojans. Second, the effect of user behavior is studied using different user reactions to malicious posts. Through simulations, we show that, if Trojans try to deceive users by choosing interesting topics, the speed of propagation will be increased exponentially. This effect raises the significance of giving security knowledge to avoid designated social engineered posts. Finally, we suggest adjustment to the current model for malware propagation in scale-free networks to consider the effect of clustering coefficient and the user behaviors.

Creating and transporting Trojan wave packets.

Abstract: Nondispersive localized Trojan wave packets with ${n}_{i}\ensuremath{\sim}305$ moving in near-circular Bohr-like orbits are created and transported to localized near-circular Trojan states of higher $n$, ${n}_{f}\ensuremath{\sim}600$, by driving with a linearly polarized sinusoidal electric field whose period is slowly increased. The protocol is remarkably efficient with over 80% of the initial atoms being transferred to the higher $n$ states, a result confirmed by classical trajectory Monte Carlo simulations.