A Cognitive Framework to Secure Smart Cities

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms

Reliable scientific service compositions

Abstract. Distributed service oriented architectures (SOAs) are increas-ingly used by users, who are insufficiently skilled in the art of distributed system programming. A good example are computational scientists who build large-scale distributed systems using service-oriented Grid comput-ing infrastructures. Computational scientists use these infrastructure to build scientific applications, which are composed from basic Web ser-vices into larger orchestrations using workflow languages, such as the Business Process Execution Language. For these users reliability of the infrastructure is of significant importance and that has to be provided in the presence of hardware or operational failures. The primitives avail-able to achieve such reliability currently leave much to be desired by users who do not necessarily have a strong education in distributed sys-tem construction. We characterise scientific service compositions and the environment they operate in by introducing the notion of global scien-tific BPEL workflows. We outline the threats to the reliability of such workflows and discuss the limited support that available specifications and mechanisms provide to achieve reliability. Furthermore, we propose a line of research to address the identified issues by investigating auto-nomic mechanisms that assist computational scientists in building, exe-cuting and maintaining reliable workflows.

A Review of Frailty Syndrome and Its Physical, Cognitive and Emotional Domains in the Elderly

Peer reviewedPublisher PD

Proactive software rejuvenation solution for web enviroments on virtualized platforms

The availability of the Information Technologies for everything, from everywhere, at all times is a growing requirement. We use information Technologies from common and social tasks to critical tasks like managing nuclear power plants or even the International Space Station (ISS). However, the availability of IT infrastructures is still a huge challenge nowadays. In a quick look around news, we can find reports of corporate outage, affecting millions of users and impacting on the revenue and image of the companies. It is well known that, currently, computer system outages are more often due to software faults, than hardware faults. Several studies have reported that one of the causes of unplanned software outages is the software aging phenomenon. This term refers to the accumulation of errors, usually causing resource contention, during long running application executions, like web applications, which normally cause applications/systems to hang or crash. Gradual performance degradation could also accompany software aging phenomena. The software aging phenomena are often related to memory bloating/ leaks, unterminated threads, data corruption, unreleased file-locks or overruns. We can find several examples of software aging in the industry. The work presented in this thesis aims to offer a proactive and predictive software rejuvenation solution for Internet Services against software aging caused by resource exhaustion. To this end, we first present a threshold based proactive rejuvenation to avoid the consequences of software aging. This first approach has some limitations, but the most important of them it is the need to know a priori the resource or resources involved in the crash and the critical condition values. Moreover, we need some expertise to fix the threshold value to trigger the rejuvenation action. Due to these limitations, we have evaluated the use of Machine Learning to overcome the weaknesses of our first approach to obtain a proactive and predictive solution. Finally, the current and increasing tendency to use virtualization technologies to improve the resource utilization has made traditional data centers turn into virtualized data centers or platforms. We have used a Mathematical Programming approach to virtual machine allocation and migration to optimize the resources, accepting as many services as possible on the platform while at the same time, guaranteeing the availability (via our software rejuvenation proposal) of the services deployed against the software aging phenomena. The thesis is supported by an exhaustive experimental evaluation that proves the effectiveness and feasibility of our proposals for current systems

Modeling operating system crash behavior through multifractal analysis, long range dependence and mining of memory usage patterns

Software Aging is a phenomenon where the state of the operating systems degrades over a period of time due to transient errors. These transient errors can result in resource exhaustion and operating system hangups or crashes.;Three different techniques from fractal geometry are studied using the same datasets for operating system crash modeling and prediction. Holder Exponent is an indicator of how chaotic a signal is. M5 Prime is a nominal classification algorithm that allows prediction of a numerical quantity such as time to crash based on current and previous data. Hurst exponent measures the self similarity and long range dependence or memory of a process or data set and has been used to predict river flows and network usage.;For each of these techniques, a thorough investigation was conducted using crash, hangup and nominal operating system monitoring data. All three approaches demonstrated a promising ability to identify software aging and predict upcoming operating system crashes. This thesis describes the experiments, reports the best candidate techniques and identifies the topics for further investigation

Applications of Dweck's Model of Implicit Theories to Teachers' Self-Efficacy and Emotional Experiences

The current study explored Dweck's (1999; Dweck & Leggett, 1988) model of implicit theories in the context of teaching in order to establish its usefulness for describing teachers' beliefs about students' ability and social behavior. Further it sought to explain the connections between teachers' implicit beliefs and their efficacy for instruction and classroom management, and their positive and negative emotional experiences. The factor structure of survey data for teachers in mid-Atlantic school districts was examined to test for classes reflecting implicit and entity beliefs, or beliefs that student attributes are malleable or fixed and unchangeable. Given that previous work in other populations has reflected important connections between individuals' implicit theories, their cognitive and emotional functioning, and their interactions with others, the current study explored whether implicit theories have similar implications for teaching. The categorical distinction between entity and incremental theories was not supported in the analyses. Further analyses were conducted using structural equation models for implicit theories, efficacy, and emotional outcomes, including symptoms of burnout. Implicit theories were associated with efficacy such that tendencies toward incremental beliefs correlated with higher efficacy in well-fitting models. Although implicit theories predicted emotional outcomes in some models such that incremental beliefs were associated with positive emotional outcomes, the effect of the implicit theory variable was not significant in models that included the efficacy variable. In these models, only efficacy was a significant predictor of emotions such that higher efficacy was associated with positive outcomes. Finally, the interaction between implicit theory and efficacy was not significant. These findings fail to support the theoretical connections between the two variables in the implicit theory framework, where low efficacy is expected to predict negative emotional outcomes in the presence of entity but not incremental theories. Instead, with respect to emotional outcomes, teaching self-efficacy appeared to be a more salient predictor than student-directed implicit theories of teachers' emotional experiences overall. Keywords: teachers, teaching motivation, implicit theories, teaching self-efficacy, emotions, affect, burnout

IPv6 Diffusion Milestones: Assessing the Quantity and Quality of Adoption

There are currently two versions of Internet Protocol (IP) in use today, IP version 4 (IPv4) and IP version 6 (IPv6). The original version, IPv4, was standardized in the early 1980s as part of the Defense Advanced Research Project Agency Internet program and became the official Internet protocol in 1983 (Kleinrock, 2010). IPv6 was standardized in 1995 as its successor to provide enhanced capabilities and address IPv4 technological limitations, most notable of which was the anticipated exhaustion of address space (Deering & Hinden, 1995). While the two protocols have some functional similarities, they are distinct and not backward compatible; IPv4-only devices cannot communicate directly with IPv6-only devices and vice-versa. Consequently, organizations wishing to take full advantage of the enhanced features of IPv6 must upgrade their entire network infrastructure and end devices to support IPv6, while at the same time maintaining IPv4 support for legacy systems that will not or cannot be upgraded. The costs and risks associated with upgrading an entire network to support a new protocol with no intrinsic return on investment has acted as a disincentive for IPv6 adoption. To be sure, the transition of the Internet to IPv6 has certainly taken a leisurely pace over the past twenty years. Given the slow pace of adoption, it is understandable that many doubted, and may still doubt that IPv6 will ever become the dominant Internet protocol and replace IPv4. However, in line with diffusion of innovations theory, it is the case with many innovations that potential adopters do not perceive any relative advantage, thus leading to a particularly slow adoption take-up rate. This is especially true with communications technologies that have high interdependence and require a critical mass of users before adoption becomes self-sustaining and rapidly accelerates (Rogers 2003). The goal of this paper is to provide empirical evidence showing that IPv6 adoption has reached critical mass and is now in a phase of accelerating adoption projected to continue. A methodology for monitoring the quality of IPv6 enablement and global IPv6 support is also provided so that the user experience over IPv6 can be assessed against the IPv4 baseline