Performance evaluation of VM-level record-and-replay techniques and applications

Virtual machine level record and replay can be used for complex system debugging and analysis, fault-tolerance replication and forensic analysis. Previous work on performance evaluation of RnR frameworks are not complete enough due to their narrow focuses. RnR related projects either focus on performance evaluation of plain record and replay mechanisms or specifically target the effectiveness of the functionality RnR supports. In order to identify the performance bottlenecks in the complicated RnR system and its various applications, this thesis conducts a thorough evaluation and analysis on 3 different modes of RnR, that is, record, replay with checkpointing and replay with VMI analysis. Both RnR system developer and users can benefit from our work. With our evaluation results, system developer can propose more efficient design accordingly, and RnR users can configure the system properly to achieve expected performance

Penetrating Shields: A Systematic Analysis of Memory Corruption Mitigations in the Spectre Era

This paper provides the first systematic analysis of a synergistic threat model encompassing memory corruption vulnerabilities and microarchitectural side-channel vulnerabilities. We study speculative shield bypass attacks that leverage speculative execution attacks to leak secrets that are critical to the security of memory corruption mitigations (i.e., the shields), and then use the leaked secrets to bypass the mitigation mechanisms and successfully conduct memory corruption exploits, such as control-flow hijacking. We start by systematizing a taxonomy of the state-of-the-art memory corruption mitigations focusing on hardware-software co-design solutions. The taxonomy helps us to identify 10 likely vulnerable defense schemes out of 20 schemes that we analyze. Next, we develop a graph-based model to analyze the 10 likely vulnerable defenses and reason about possible countermeasures. Finally, we present three proof-of-concept attacks targeting an already-deployed mitigation mechanism and two state-of-the-art academic proposals.Comment: 14 page

The Adoption of Blockchain Technologies in Data Sharing: A State of the Art Survey

In the big data era, it is a significant need for data sharing in various industries. However, there are many weaknesses in the traditional centralized way of data sharing. It is easy to attack the centralized data storage center. As the process of data asset transactions is not transparent, there is a lack of trust in the percipients of data sharing. Blockchain technology offers a possibility to solve these problems in data sharing, as the blockchain can provide a decentralized, programmable, tamperproof, and anonymous data sharing environment. In this paper, we compare the blockchain-based data sharing with the traditional ways of data sharing, and analyze the scenarios in major industry applications. We survey the state of the art of the adoption of blockchain technologies in data sharing, and provide a summary about their technical frameworks and schemes

Electrochemical hydrogenation of mixed-phase TiO₂ nanotube arrays enables remarkably enhanced photoelectrochemical water splitting performance

We first report that photoelectrochemical (PEC) performance of electrochemically hydrogenated TiO2 nanotube arrays (TNTAs) as high-efficiency photoanodes for solar water splitting could be well tuned by designing and adjusting the phase structure and composition of TNTAs. Among various TNTAs annealed at different temperature ranging from 300 to 700 °C, well-crystallized single anatase (A) phase TNTAs-400 photoanode shows the best photoresponse properties and PEC performance due to the favorable crystallinity, grain size and tubular structures. After electrochemical hydrogenation (EH), anatase-rutile (A-R) mixed phase EH-TNTAs-600 photoanode exhibits the highest photoactivity and PEC performance for solar water splitting. Under simulated solar illumination, EH-TNTAs-600 achieves the best photoconversion efficiency of up to 1.52% and maximum H2 generation rate of 40.4 µmol h−1 cm−2, outstripping other EH-TNTAs photoanodes. Systematic studies reveal that the signigicantly enhanced PEC performance for A-R mixed phaes EH-TNTAs-600 photoanode could be attributed to the synergy of A-R mixed phases and intentionally introduced Ti3+ (oxygen vacancies) which enhances the photoactivity over both UV and visible-light regions, and boosts both charge separation and transfer efficiencies. These findings provide new insight and guidelines for the construction of highly efficient TiO2-based devices for the application of solar water splitting.This work was supported by the National Natural Science Foundation of China (51402078, 21702041, and 11674354), the National Basic Research Program of China (2014CB660815), and the Fundamental Research Funds for the Central Universities (JZ2016HGTB0711, JZ2016HGTB0719, and JZ2017HGPA0167)

Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints

As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject. Even those that deploy neural networks embedded in physical devices may wish to keep the inner working of their designs hidden -- either to protect their intellectual property or as a form of protection from adversarial inputs. The specific problem we address is how, through heavy system stack, given noisy and imperfect memory traces, one might reconstruct the neural network architecture including the set of layers employed, their connectivity, and their respective dimension sizes. Considering both the intra-layer architecture features and the inter-layer temporal association information introduced by the DNN design empirical experience, we draw upon ideas from speech recognition to solve this problem. We show that off-chip memory address traces and PCIe events provide ample information to reconstruct such neural network architectures accurately. We are the first to propose such accurate model extraction techniques and demonstrate an end-to-end attack experimentally in the context of an off-the-shelf Nvidia GPU platform with full system stack. Results show that the proposed techniques achieve a high reverse engineering accuracy and improve the one's ability to conduct targeted adversarial attack with success rate from 14.6\%$\sim$25.5\% (without network architecture knowledge) to 75.9\% (with extracted network architecture)

The light chains of kinesin-1 are autoinhibited

The light chains (KLCs) of the microtubule motor kinesin-1 bind cargoes and regulate its activity. Through their tetratricopeptide repeat domain (KLCTPR), they can recognize short linear peptide motifs found in many cargo proteins characterized by a central tryptophan flanked by aspartic/glutamic acid residues (W-acidic). Using a fluorescence resonance energy transfer biosensor in combination with X-ray crystallographic, biochemical, and biophysical approaches, we describe how an intramolecular interaction between the KLC2TPR domain and a conserved peptide motif within an unstructured region of the molecule, partly occludes the W-acidic binding site on the TPR domain. Cargo binding displaces this interaction, effecting a global conformational change in KLCs resulting in a more extended conformation. Thus, like the motor-bearing kinesin heavy chains, KLCs exist in a dynamic conformational state that is regulated by self-interaction and cargo binding. We propose a model by which, via this molecular switch, W-acidic cargo binding regulates the activity of the holoenzyme

Photoelastic plasmonic metasurfaces with ultra-large near infrared spectral tuning

Metasurfaces, consisting of artificially fabricated sub-wavelength meta-atoms with pre-designable electromagnetic properties, provide novel opportunities to a variety of applications such as light detectors/sensors, local field imaging and optical displays. Currently, the tuning of most metasurfaces requires redesigning and reproducing the entire structure, rendering them ineligible for post-fabrication shape-morphing or spectral reconfigurability. Here, we report a photoelastic metasurface with an all-optical and reversible resonance tuning in the near infrared range. The photoelastic metasurface consists of hexagonal gold nanoarrays deposited on a deformable substrate made of a liquid crystalline network. Upon photo-actuation, the substrate deforms, causing the lattice to change and, as a result, the plasmon resonance to shift. The centre wavelength of the plasmon resonance exhibits an ultra-large spectral tuning of over 245 nm, from 1490 to 1245 nm, while the anisotropic deformability also endows light-switchable sensitivity in probing polarization. The proposed concept establishes a light-controlled soft platform that is of great potential for tunable/reconfigurable photonic devices, such as nano-filters, -couplers, -holograms, and displays with structural colors.publishedVersionPeer reviewe