Mobile Underwater Sensor Networks for Protection and Security: Field Experience at the UAN11 Experiment

The EU‐funded project UAN (Underwater Acoustic Network) was aimed at conceiving, developing, and testing at sea an innovative and operational concept for integrating underwater and above‐water sensors in a unique communication system to protect offshore and coastline critical infrastructures. This work gives details on the underwater part of the project. It introduces a set of original security features and gives details on the integration of autonomous underwater vehicles (AUVs) as mobile nodes of the network and as surveillance assets, acoustically controlled by the command and control center to respond against intrusions. Field results are given of the final UAN project sea trial, UAN11, held in May 2011 in Norway. During the experimental activities, a UAN composed of four fixed nodes, two AUVs, and one mobile node mounted on the supporting research vessel was operated continuously and integrated into a global protection system. In this article, the communication performance of the network is reported in terms of round‐trip time, packet loss, and average delivery ratio. The major results of the experiment can be thus summarized: the implemented network structure was successful in continuously operating over five days with nodes seamlessly entering and exiting the network; the performance of the network varied greatly with fluctuations in the acoustic channel; the addition of security features induced a minor degradation in network performance with respect to channel variation; the AUVs were successfully controlled from a remote station through acoustic signals routed by the network. © 2013 Wiley Periodicals, Inc.     

Cryptanalysis of the RCES/RSES image encryption scheme

Recently, a chaos-based image encryption scheme called RCES (also called RSES) was proposed. This paper analyses the security of RCES, and points out that it is insecure against the known/chosen-plaintext attacks: the number of required known/chosen plain-images is only one or two to succeed an attack. In addition, the security of RCES against the brute-force attack was overestimated. Both theoretical and experimental analyses are given to show the performance of the suggested known/chosen-plaintext attacks. The insecurity of RCES is due to its special design, which makes it a typical example of insecure image encryption schemes. A number of lessons are drawn from the reported cryptanalysis of RCES, consequently suggesting some common principles for ensuring a high level of security of an image encryption scheme.     

On measuring network robustness for weighted networks

Knowledge and Information Systems - Network robustness measures how well network structure is strong and healthy when it is under attack, such as vertices joining and leaving. It has been widely...     

Hyperprolactinemia in end‐stage renal disease and effects of frequent hemodialysis

Introduction: End‐stage renal disease is associated with elevations in circulating prolactin concentrations, but the association of prolactin concentrations with intermediate health outcomes and the effects of hemodialysis frequency on changes in serum prolactin have not been examined. Methods: The FHN Daily and Nocturnal Dialysis Trials compared the effects of conventional thrice weekly hemodialysis with in‐center daily hemodialysis (6 days/week) and nocturnal home hemodialysis (6 nights/week) over 12 months and obtained measures of health‐related quality of life, self‐reported physical function, mental health and cognition. Serum prolactin concentrations were measured at baseline and 12‐month follow‐up in 70% of the FHN Trial cohort to examine the associations among serum prolactin concentrations and physical, mental and cognitive function and the effects of hemodialysis frequency on serum prolactin. Findings: Among 177 Daily Trial and 60 Nocturnal Trial participants with baseline serum prolactin measurements, the median serum prolactin concentration was 65 ng/mL (25th–75th percentile 48–195 ng/mL) and 81% had serum prolactin concentrations >30 ng/mL. While serum prolactin was associated with sex (higher in women), we observed no association between baseline serum prolactin and age, dialysis vintage, and baseline measures of physical, mental and cognitive function. Furthermore, there was no significant effect of hemodialysis frequency on serum prolactin in either of the two trials. Discussion: Serum prolactin concentrations were elevated in the large majority of patients with ESRD, but were not associated with several measures of health status. Circulating prolactin levels also do not appear to decrease in response to more frequent hemodialysis over a one‐year period.     

Solution structure of glutathione bound to human glutathione transferase P1-1: comparison of NMR measurements with the crystal structure

The conformation of the bound glutathione (GSH) in the active site of the human glutathione transferase P1-1 (EC 2.5.1.18) has been studied by transferred NOE measurements and compared with those obtained by X-ray diffraction data. Two-dimensional TRNOESY and TRROESY experiments have been performed under fast-exchange conditions. The family of GSH conformers, compatible with TRNOE distance constraints, shows a backbone structure very similar to the crystal model. Interesting differences have been found in the side chain regions. After restrained energy minimization of a representative NMR conformer in the active site, the sulfur atom is not found in hydrogen-bonding distance of the hydroxyl group of Tyr 7. This situation is similar to the one observed in an "atypical" crystal complex grown at low pH and low temperature. The NMR conformers display also a poorly defined structure of the glutamyl moiety, and the presence of an unexpected intermolecular NOE could indicate a different interaction of this substrate portion with the G-site. The NMR data seem to provide a snapshot of GSH in a precomplex where the GSH glutamyl end is bound in a different fashion. The existence of this precomplex is supported by pre-steady-state kinetic experiments [Caccuri, A. M., Lo Bello, M., Nuccetelli, M., Nicotra, M., Rossi, P., Antonini, G., Federici, G., and Ricci, G. (1998) Biochemistry 37, 3028-3034] and preliminary time-resolved fluorescence data.     

A study of maintenance policies for second-hand products

This paper proposes two maintenance schemes for reused products. In order to reduce the purchasing cost or conform to the concept of environmental protection, reuse is considered as one of the most efficient strategies. However, the initial quality of reused product is often inferior to the new one and then product failures will occur frequently during usage period. Therefore, two periodical preventive maintenance (PM) policies in this paper are developed to decrease the high failure rate of the second-hand products. When a second-hand product with Weibull life time distribution of known age is intended to be used for a pre-specified period of time, the optimal number of PM actions and the corresponding maintenance degree are derived such that the expected total maintenance cost is minimized. The impact of providing preventive maintenance is illustrated through numerical examples.     

A low-order model of biological neural networks

A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.     

The relationship between online game playing motivation and selection of online game characters – the case of Taiwan

The alarming growth rate in the online game industry calls for a need of understanding the determinants of online gamers’ behaviour. Investigating how online gamers choose different online characters assists online game vendors to design characters that match the needs of different online gamers. Recently, numerous online game designers have introduced celebrity figures as characters for certain online games. The popularity of those celebrities has resulted in greater awareness of these games, and hopefully further increases the playability of online games. While different online gamers are motivated by different desires for playing, it is insufficiently researched whether these online celebrity characters have actually altered players’ choice in giving preference to these games. Thus, this study conducts an experimental design to study the reasons behind players’ choice of characters by studying how online games perceive the interpersonal attractiveness of celebrity online characters, and how online gamers with different playing motivations display different behaviours in their choice of online game characters. Results from the survey filled out by participants after the experiments show that the attractiveness of celebrity online characters is significantly higher than that of non-celebrity characters. Further, online game players with a social playing motivation tend to choose celebrities as their online game characters.     

Using ontologies to perform threat analysis and develop defensive strategies for mobile security

Existing studies on the detection of mobile malware have focused mainly on static analyses performed to examine the code-structure signature of viruses, rather than the dynamic behavioral aspects. By contrast, the unidentified behavior of new mobile viruses using the self-modification, polymorphic, and mutation techniques for variants have largely been ignored. The problem of precision regarding malware variant detection has become one of the key concerns in mobile security. Accordingly, the present study proposed a threat risk analysis model for mobile viruses, using a heuristic approach incorporating both malware behavior analysis and code analysis to generate a virus behavior ontology associated with the Protégé platform. The proposed model can not only explicitly identify an attack profile in accordance with structural signature of mobile viruses, but also overcome the uncertainty regarding the probability of an attack being successful. This model is able to achieve this by extending frequent episode rules to investigate the attack profile of a given malware, using specific event sequences associated with the sandbox technique for mobile applications (apps) and hosts. For probabilistic analysis, defense evaluation metrics for each node were used to simulate the results of an attack. The simulations focused specifically on the attack profile of a botnet to assess the threat risk. The validity of the proposed approach was demonstrated numerically by using two malware cyber-attack examples. Overall, the results presented in this paper prove that the proposed scheme offers an effective countermeasure, evaluated using a set of security metrics, for mitigating network threats by considering the interaction between the attack profiles and defense needs.