Co-Check: Collaborative Outsourced Data Auditing in Multicloud Environment

With the increasing demand for ubiquitous connectivity, wireless technology has significantly improved our daily lives. Meanwhile, together with cloud-computing technology (e.g., cloud storage services and big data processing), new wireless networking technology becomes the foundation infrastructure of emerging communication networks. Particularly, cloud storage has been widely used in services, such as data outsourcing and resource sharing, among the heterogeneous wireless environments because of its convenience, low cost, and flexibility. However, users/clients lose the physical control of their data after outsourcing. Consequently, ensuring the integrity of the outsourced data becomes an important security requirement of cloud storage applications. In this paper, we present Co-Check, a collaborative multicloud data integrity audition scheme, which is based on BLS (Boneh-Lynn-Shacham) signature and homomorphic tags. According to the proposed scheme, clients can audit their outsourced data in a one-round challenge-response interaction with low performance overhead. Our scheme also supports dynamic data maintenance. The theoretical analysis and experiment results illustrate that our scheme is provably secure and efficient

Characterization of Neuraminidases from the Highly Pathogenic Avian H5N1 and 2009 Pandemic H1N1 Influenza A Viruses

To study the precise role of the neuraminidase (NA), and its stalk region in particular, in the assembly, release, and entry of influenza virus, we deleted the 20-aa stalk segment from 2009 pandemic H1N1 NA (09N1) and inserted this segment, now designated 09s60, into the stalk region of a highly pathogenic avian influenza (HPAI) virus H5N1 NA (AH N1). The biological characterization of these wild-type and mutant NAs was analyzed by pseudotyped particles (pseudoparticles) system. Compared with the wild-type AH N1, the wild-type 09N1 exhibited higher NA activity and released more pseudoparticles. Deletion/insertion of the 09s60 segment did not alter this relationship. The infectivity of pseudoparticles harboring NA in combination with the hemagglutinin from HPAI H5N1 (AH H5) was decreased by insertion of 09s60 into AH N1 and was increased by deletion of 09s60 from 09N1. When isolated from the wild-type 2009H1N1 virus, 09N1 existed in the forms (in order of abundance) dimer>>tetramer>monomer, but when isolated from pseudoparticles, 09N1 existed in the forms dimer>monomer>>>tetramer. After deletion of 09s60, 09N1 existed in the forms monomer>>>dimer. AH N1 from pseudoparticles existed in the forms monomer>>dimer, but after insertion of 09s60, it existed in the forms dimer>>monomer. Deletion/insertion of 09s60 did not alter the NA glycosylation pattern of 09N1 or AH N1. The 09N1 was more sensitive than the AH N1 to the NA inhibitor oseltamivir, suggesting that the infectivity-enhancing effect of oseltamivir correlates with robust NA activity

Visual object recognition using probabilistic kernel subspace similarity

Probabilistic subspace similarity-based face matching is an efficient face recognition algorithm proposed by Moghaddam et al. It makes one basic assumption: the intra-class face image set spans a linear space. However, there are yet no rational geometric interpretations of the similarity under that assumption. This paper investigates two subjects. First, we present one interpretation of the intra-class linear subspace assumption from the perspective of manifold analysis, and thus discover the geometric nature of the similarity. Second, we also note that the linear subspace assumption does not hold in some cases, and generalize it to nonlinear cases by introducing kernel tricks. The proposed model is named probabilistic kernel subspace similarity (PKSS). Experiments on synthetic data and real visual object recognition tasks show that PKSS can achieve promising performance, and outperform many other current popular object recognition algorithms. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved

Probabilistic Tangent Subspace: A Unified View

Tangent Distance (TD) is one classical method for invariant pattern classification. However, conventional TD need pre-obtain tangent vectors, which is difficult except for image objects. This paper extends TD to more general pattern classification tasks. The basic assumption is that tangent vectors can be approximately represented by the pattern variations. We propose three probabilistic subspace models to encode the variations: the linear subspace, nonlinear subspace, and manifold subspace models. These three models are addressed in a unified view, namely Probabilistic Tangent Subspace (PTS). Experiments show that PTS can achieve promising classification performance in non-image data sets. 1

A Compatible OpenFlow Platform for Enabling Security Enhancement in SDN

Software-defined networking (SDN) is a representative next generation network architecture, which allows network administrators to programmatically initialize, control, change, and manage network behavior dynamically via open interfaces. SDN is widely adopted in systems like 5G mobile networks and cyber-physical systems (CPS). However, SDN brings new security problems, e.g., controller hijacking, black-hole, and unauthorized data modification. Traditional firewall or IDS based solutions cannot fix these challenges. It is also undesirable to develop security mechanisms in such an ad hoc manner, which may cause security conflict during the deployment procedure. In this paper, we propose OSCO (Open Security-enhanced Compatible OpenFlow) platform, a unified, lightweight platform to enhance the security property and facilitate the security configuration and evaluation. The proposed platform supports highly configurable cryptographic algorithm modules, security protocols, flexible hardware extensions, and virtualized SDN networks. We prototyped our platform based on the Raspberry Pi Single Board Computer (SBC) hardware and presented a case study for switch port security enhancement. We systematically evaluated critical security modules, which include 4 hash functions, 8 stream/block ciphers, 4 public-key cryptosystems, and key exchange protocols. The experiment results show that our platform performs those security modules and SDN network functions with relatively low computational (extra 2.5% system overhead when performing AES-256 and SHA-256 functions) and networking performance overheads (73.7 Mb/s TCP and 81.2Mb/s UDP transmission speeds in 100Mb/s network settings)

Phishing page detection via learning classifiers from page layout feature

Abstract The web technology has become the cornerstone of a wide range of platforms, such as mobile services and smart Internet-of-things (IoT) systems. In such platforms, users’ data are aggregated to a cloud-based platform, where web applications are used as a key interface to access and configure user data. Securing the web interface requires solutions to deal with threats from both technical vulnerabilities and social factors. Phishing attacks are one of the most commonly exploited vectors in social engineering attacks. The attackers use web pages visually mimicking legitimate web sites, such as banking and government services, to collect users’ sensitive information. Existing phishing defense mechanisms based on URLs or page contents are often evaded by attackers. Recent research has demonstrated that visual layout similarity can be used as a robust basis to detect phishing attacks. In particular, features extracted from CSS layout files can be used to measure page similarity. However, it needs human expertise in specifying how to measure page similarity based on such features. In this paper, we aim to enable automated page-layout-based phishing detection techniques using machine learning techniques. We propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. We prototype our solution and evaluate four popular machine learning classifiers on their accuracy and the factors affecting their results

CEPC Technical Design Report -- Accelerator

International audienceThe Circular Electron Positron Collider (CEPC) is a large scientific project initiated and hosted by China, fostered through extensive collaboration with international partners. The complex comprises four accelerators: a 30 GeV Linac, a 1.1 GeV Damping Ring, a Booster capable of achieving energies up to 180 GeV, and a Collider operating at varying energy modes (Z, W, H, and ttbar). The Linac and Damping Ring are situated on the surface, while the Booster and Collider are housed in a 100 km circumference underground tunnel, strategically accommodating future expansion with provisions for a Super Proton Proton Collider (SPPC). The CEPC primarily serves as a Higgs factory. In its baseline design with synchrotron radiation (SR) power of 30 MW per beam, it can achieve a luminosity of 5e34 /cm^2/s^1, resulting in an integrated luminosity of 13 /ab for two interaction points over a decade, producing 2.6 million Higgs bosons. Increasing the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons, facilitating precise measurements of Higgs coupling at sub-percent levels, exceeding the precision expected from the HL-LHC by an order of magnitude. This Technical Design Report (TDR) follows the Preliminary Conceptual Design Report (Pre-CDR, 2015) and the Conceptual Design Report (CDR, 2018), comprehensively detailing the machine's layout and performance, physical design and analysis, technical systems design, R&D and prototyping efforts, and associated civil engineering aspects. Additionally, it includes a cost estimate and a preliminary construction timeline, establishing a framework for forthcoming engineering design phase and site selection procedures. Construction is anticipated to begin around 2027-2028, pending government approval, with an estimated duration of 8 years. The commencement of experiments could potentially initiate in the mid-2030s

CEPC Technical Design Report -- Accelerator

International audienceThe Circular Electron Positron Collider (CEPC) is a large scientific project initiated and hosted by China, fostered through extensive collaboration with international partners. The complex comprises four accelerators: a 30 GeV Linac, a 1.1 GeV Damping Ring, a Booster capable of achieving energies up to 180 GeV, and a Collider operating at varying energy modes (Z, W, H, and ttbar). The Linac and Damping Ring are situated on the surface, while the Booster and Collider are housed in a 100 km circumference underground tunnel, strategically accommodating future expansion with provisions for a Super Proton Proton Collider (SPPC). The CEPC primarily serves as a Higgs factory. In its baseline design with synchrotron radiation (SR) power of 30 MW per beam, it can achieve a luminosity of 5e34 /cm^2/s^1, resulting in an integrated luminosity of 13 /ab for two interaction points over a decade, producing 2.6 million Higgs bosons. Increasing the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons, facilitating precise measurements of Higgs coupling at sub-percent levels, exceeding the precision expected from the HL-LHC by an order of magnitude. This Technical Design Report (TDR) follows the Preliminary Conceptual Design Report (Pre-CDR, 2015) and the Conceptual Design Report (CDR, 2018), comprehensively detailing the machine's layout and performance, physical design and analysis, technical systems design, R&D and prototyping efforts, and associated civil engineering aspects. Additionally, it includes a cost estimate and a preliminary construction timeline, establishing a framework for forthcoming engineering design phase and site selection procedures. Construction is anticipated to begin around 2027-2028, pending government approval, with an estimated duration of 8 years. The commencement of experiments could potentially initiate in the mid-2030s