Analysis of Software Implemented Low Entropy Masking Schemes

Low Entropy Masking Schemes (LEMS) are countermeasure techniques to mitigate the high performance overhead of masked hardware and software implementations of symmetric block ciphers by reducing the entropy of the mask sets. The security of LEMS depends on the choice of the mask sets. Previous research mainly focused on searching balanced mask sets for hardware implementations. In this paper, we find that those balanced mask sets may have vulnerabilities in terms of absolute difference when applied in software implemented LEMS. The experiments verify that such vulnerabilities certainly make the software LEMS implementations insecure. To fix the vulnerabilities, we present a selection criterion to choose the mask sets. When some feasible mask sets are already picked out by certain searching algorithms, our selection criterion could be a reference factor to help decide on a more secure one for software LEMS

Hydrodynamical simulations of cluster formation with central AGN heating

We analyse a hydrodynamical simulation model for the recurrent heating of the central intracluster medium (ICM) by active galactic nuclei (AGN). Besides the self-gravity of the dark matter and gas components, our approach includes the radiative cooling and photoheating of the gas, as well as a subresolution multiphase model for star formation and supernova feedback. Additionally, we incorporate a periodic heating mechanism in the form of hot, buoyant bubbles, injected into the intragalactic medium (IGM) during the active phases of the accreting central AGN. We use simulations of isolated cluster halos of different masses to study the bubble dynamics and the heat transport into the IGM. We also apply our model to self-consistent cosmological simulations of the formation of galaxy clusters with a range of masses. Our numerical schemes explore a variety of different assumptions for the spatial configuration of AGN-driven bubbles, for their duty cycles and for the energy injection mechanism, in order to obtain better constraints on the underlying physical picture. We argue that AGN heating can substantially affect the properties of both the stellar and gaseous components of clusters of galaxies. Most importantly, it alters the properties of the central dominant (cD) galaxy by reducing the mass deposition rate of freshly cooled gas out of the ICM, thereby offering an energetically plausible solution to the cooling flow problem. At the same time, this leads to reduced or eliminated star formation in the central cD galaxy, giving it red stellar colours as observed.Comment: 22 pages, 15 figures, minor revisions, MNRAS accepte

Perceptually-Driven Video Coding with the Daala Video Codec

The Daala project is a royalty-free video codec that attempts to compete with the best patent-encumbered codecs. Part of our strategy is to replace core tools of traditional video codecs with alternative approaches, many of them designed to take perceptual aspects into account, rather than optimizing for simple metrics like PSNR. This paper documents some of our experiences with these tools, which ones worked and which did not. We evaluate which tools are easy to integrate into a more traditional codec design, and show results in the context of the codec being developed by the Alliance for Open Media.Comment: 19 pages, Proceedings of SPIE Workshop on Applications of Digital Image Processing (ADIP), 201

Feasibility and performances of compressed-sensing and sparse map-making with Herschel/PACS data

The Herschel Space Observatory of ESA was launched in May 2009 and is in operation since. From its distant orbit around L2 it needs to transmit a huge quantity of information through a very limited bandwidth. This is especially true for the PACS imaging camera which needs to compress its data far more than what can be achieved with lossless compression. This is currently solved by including lossy averaging and rounding steps on board. Recently, a new theory called compressed-sensing emerged from the statistics community. This theory makes use of the sparsity of natural (or astrophysical) images to optimize the acquisition scheme of the data needed to estimate those images. Thus, it can lead to high compression factors. A previous article by Bobin et al. (2008) showed how the new theory could be applied to simulated Herschel/PACS data to solve the compression requirement of the instrument. In this article, we show that compressed-sensing theory can indeed be successfully applied to actual Herschel/PACS data and give significant improvements over the standard pipeline. In order to fully use the redundancy present in the data, we perform full sky map estimation and decompression at the same time, which cannot be done in most other compression methods. We also demonstrate that the various artifacts affecting the data (pink noise, glitches, whose behavior is a priori not well compatible with compressed-sensing) can be handled as well in this new framework. Finally, we make a comparison between the methods from the compressed-sensing scheme and data acquired with the standard compression scheme. We discuss improvements that can be made on ground for the creation of sky maps from the data.Comment: 11 pages, 6 figures, 5 tables, peer-reviewed articl

Achieving Obfuscation Through Self-Modifying Code: A Theoretical Model

With the extreme amount of data and software available on networks, the protection of online information is one of the most important tasks of this technological age. There is no such thing as safe computing, and it is inevitable that security breaches will occur. Thus, security professionals and practices focus on two areas: security, preventing a breach from occurring, and resiliency, minimizing the damages once a breach has occurred. One of the most important practices for adding resiliency to source code is through obfuscation, a method of re-writing the code to a form that is virtually unreadable. This makes the code incredibly hard to decipher by attackers, protecting intellectual property and reducing the amount of information gained by the malicious actor. Achieving obfuscation through the use of self-modifying code, code that mutates during runtime, is a complicated but impressive undertaking that creates an incredibly robust obfuscating system. While there is a great amount of research that is still ongoing, the preliminary results of this subject suggest that the application of self-modifying code to obfuscation may yield self-maintaining software capable of healing itself following an attack

Identifying Program Entropy Characteristics with Symbolic Execution

The security infrastructure underpinning our society relies on encryption, which relies on the correct generation and use of pseudorandom data. Unfortunately, random data is deceptively hard to generate. Implementation problems in PRNGs and the incorrect usage of generated random data in cryptographic algorithms have led to many issues, including the infamous Debian OpenSSL bug, which exposed millions of systems on the internet to potential compromise due to a mistake that limited the source of randomness during key generation to have 2^15 different seeds (i.e. 15 bits of entropy).It is important to automatically identify if a given program applies a certain cryptographic algorithm or uses its random data correctly.This paper tackles the very first step of this problem by extracting an understanding of how a binary program generates or uses randomness. Specifically, we set the following problem: given a program (or a specific function), can we estimate bounds on the amount of randomness present in the program or function's output by determining bounds on the entropy of this output data? Our technique estimates upper bounds on the entropy of program output through a process of expression reinterpretation and stochastic probability estimation, related to abstract interpretation and model counting

Circuit-Variant Moving Target Defense for Side-Channel Attacks on Reconfigurable Hardware

With the emergence of side-channel analysis (SCA) attacks, bits of a secret key may be derived by correlating key values with physical properties of cryptographic process execution. Power and Electromagnetic (EM) analysis attacks are based on the principle that current flow within a cryptographic device is key-dependent and therefore, the resulting power consumption and EM emanations during encryption and/or decryption can be correlated to secret key values. These side-channel attacks require several measurements of the target process in order to amplify the signal of interest, filter out noise, and derive the secret key through statistical analysis methods. Differential power and EM analysis attacks rely on correlating actual side-channel measurements to hypothetical models. This research proposes increasing resistance to differential power and EM analysis attacks through structural and spatial randomization of an implementation. By introducing randomly located circuit variants of encryption components, the proposed moving target defense aims to disrupt side-channel collection and correlation needed to successfully implement an attac