Simulating Windows-Based Cyber Attacks Using Live Virtual Machine Introspection

Static memory analysis has been proven a valuable technique for digital forensics. However, the memory capture technique halts the system causing the loss of important dynamic system data. As a result, live analysis techniques have emerged to complement static analysis. In this paper, a compiled memory analysis tool for virtualization (CMAT-V) is presented as a virtual machine introspection (VMI) utility to conduct live analysis during simulated cyber attacks. CMAT-V leverages static memory dump analysis techniques to provide live system state awareness. CMAT-V parses an arbitrary memory dump from a simulated guest operating system (OS) to extract user information, network usage, active process information and registry files. Unlike some VMI applications, CMAT-V bridges the semantic gap using derivation techniques. This provides increased operating system compatibility for current and future operating systems. This research demonstrates the usefulness of CMAT-V as a situational awareness tool during simulated cyber attacks and measures the overall performance of CMAT-V

Does a Keyword Mnemonics Intervention Have an Effect on the Components of the Working Memory System?

Working memory is a memory system described as a person\u27s ability to simultaneously store, manipulate, and process information over a brief period of time (Baddeley & Hitch, 1974); it is the active processing of information in the here and now. As working memory moves to the forefront of research studies, it becomes apparent that there is a paucity of research addressing ecologically valid interventions which can be conducted in the classroom and interventions\u27 direct impact on the working memory system. This paper addresses the development and research regarding the working memory system, demonstrating a current gap in the available research. It then examines the effects of Keyword Mnemonics intervention on the components of fourth graders\u27 working memory systems by assessing each component individually both pre- and post-intervention. Pretest and posttest data from 55 fourth grade students (25 males; 30 females) was collected, with 27 participants in the intervention group and 28 participants in the control group. Results of Multivariate Analysis of Covariance (MANCOVA) reveal that there were no differences in the working memory components between the intervention group and the no-intervention control group following the intervention. Using pretest scores as covariates, group membership did not have an effect on posttest performance. These results are discussed within the context of available literature. Finally, limitations of this project and directions for future research are considered

Active experiencing in postdramatic performance : affective memory and quarantine theatre’s wallflower

Postdramatic approaches to performance and Stanislavsky’s methodology seemingly occupy divergent performance traditions. Nonetheless, both traditions often require performers to mine their own lives (albeit to different ends) and operate in an experiential realm that demands responsiveness to and within the live moment of performing. It is this realm that I explore in this paper, through an analysis of Quarantine Theatre’s Wallflower (2015). I argue that Wallflower represents an example of postdramatic practice that blends a poetics of failure with a psychophysical dramaturgical approach that can be aligned with Stanislavsky’s concepts of Affective Memory and Active Analysis. I also adopt Carnicke’s use of the term ‘active experiencing’ to describe Wallflower’s dramaturgical process. I argue that Wallflower provides a useful case study of practice that challenges the binary opposition between dramatic and postdramatic that is still prevalent in theatre and performance studies scholarship, and suggest that the application of aspects of Stanislavsky’s System, nuanced by cognitive neuroscience, can expand the theorization of postdramatic theatre, which in turn generates techniques that can prove valuable in the rehearsal of dramatic theatre itself

A real-time active database for high transaction loads and moderate deadlines

A large class of real-time database problems has very high transaction loads and moderate deadlines. Traditional approaches have not been designed to handle such problems. A model based on the use of encapsulated events and rule objects has been developed. The model describes an active, real-time, object-oriented, memory-resident database environment (REACT). A system based on the model has been designed and implemented. A concurrency control algorithm was developed that uses the extra information available from the object-oriented and active features of REACT to pre-process the database and speed up concurrency control. Analysis was done for both single and multiple processor systems. For multiprocessor analysis a simulator was developed to verify the performance of REACT on a multiprocessor system. Examples of all the features needed for an actual system are given along with examples of how REACT can be used to solve real-world control and monitoring problems. Algorithms have been developed to allow users to test that the properties termination, confluence, and observable determinism hold for a target REACT database

Two-stage code acquisition in wireless optical CDMA communications using optical orthogonal codes

In this paper, we analyze the performance of code acquisition system in atmospheric optical code division multiple access (OCDMA) communications using optical orthogonal codes. Memory introduced by temporal correlation of optical fading process precludes us from using the Markov chain model for a code acquisition analysis. By considering this issue, we discuss how to extend the applicability of the Markov chain model to the atmospheric OCDMA communications. We analyze and compare the performance of correlator and chip level detector (CLD) structures in the acquisition system. In our analysis, we consider the effects of free space optical channel impairments, multiple access interference, and receiver thermal noise in the context of semi-classical photon-counting approach. Furthermore, we evaluate the performance of various two stage schemes that utilize different combinations of active correlator, matched filter, and CLD in search and verification stages, and we find the optimum acquisition scheme among them. Numerical results show significant improvement in reducing the acquisition time and required power for synchronization using our optimum scheme in the wireless OCDMA communications

Working memory efficacy and aging

Abstract The aim of this thesis is to examine the effects of age on visuo-spatial sketchpad (VSSP) slave system processes and central executive working memory processes within the context of the multicomponent working memory model originally proposed by Baddeley & Hitch (1974). Previous cognitive aging research has tended to use general measures of working memory and little evidence has examined the effects of age specifically within the context of the multicomponent model. A series of seven studies was undertaken utilising a quasi-experimental design. Data was collected from convenience samples of young and old adults for each study, using a range of tasks and measures designed to make demands on VSSP and central executive processes. Effects of age were examined independently of speed of processing and intelligence by using these as covariates in the statistical analysis. Data was analysed using a series of ANOVA and ANCOVA analyses. Findings indicated that old adults were equivalent to young adults in their performance on the VSSP slave system tasks. However they showed an impaired performance on some measures of central executive processing, but not others. In particular, older adults showed a decline in the executive processes of task switching, which cannot be explained by speed of processing; whereas other putative executive processes, such as inhibitory processes, did not show an age-related decline. Results indicated that the age-related decline in task switching at the specific switch point is only evident when the demands for active memory processing are high. An age-related decline in the ability to co-ordinate the two tasks during task switching was also evident, and this age difference was not dependent on the active memory demands. These findings suggest that there are a number of separable executive processes, not all of which decline with age. The findings are discussed in relation to models of cognitive aging and theoretical models of working memory

Improving performance of blackboard systems

In this thesis, we deal with blackboard system performance issues. We show that blackboard system performance can be improved using parallel processing strategies and a novel blackboard architecture.We study traditional blackboard architectures using a novel performance frame¬ work. This is a useful tool for directing system optimisation efforts. We present the analysis of four blackboard systems present in the literature.nalysis of four blackboard systems present in the literature. Besides localised optimisation efforts, one of the most promising approaches for improving blackboard system performance is the use of parallel processing techniques. However, traditional blackboard architectures present both data and control contention when implemented in parallel.In this thesis we present a novel blackboard architecture, the Active Blackboard Architecture (ABB). We based ABB on a novel variation of the traditional "Blackboard and Experts" metaphor, called "Blackboard, Experts and Desks". This new metaphor introduces a new element, the desks, used by the experts to perform their work.The ABB architecture is based on an active blackboard, capable of processing on its own, and a decentralised control model. This avoids control contention and bottlenecks. We describe this architecture using the Z specification language, and implemented and evaluated in the EPCC Meiko Computing Surface, a multi-transputer distributed memory parallel machine.The ABB Parallel prototype is an object oriented implementation of the ABB model that overcomes both data and control bottlenecks by having a distributed blackboard and using the ABB control model. Based on a series of experiments, we show that the new architecture allows to achieve much greater effective parallelism in a blackboard system. We also present some ways in which the system can be tailored to specific application needs, improving in this way its overall performance

Extensive Analysis of a Real-Time Dense Wired Sensor Network Based on Traffic Shaping

XDense is a novel wired 2D mesh grid sensor network system for application scenarios that benefit from densely deployed sensing (e.g., thousands of sensors per square meter). It was conceived for cyber-physical systems that require real-time sensing and actuation, like active flow control on aircraft wing surfaces. XDense communication and distributed processing capabilities are designed to enable complex feature extraction within bounded time and in a responsive manner. In this article, we tackle the issue of deterministic behavior of XDense. We present a methodology that uses traffic-shaping heuristics to guarantee bounded communication delays and the fulfillment of memory requirements. We evaluate the model for varied network configurations and workload, and present a comparative performance analysis in terms of link utilization, queue size, and execution time. With the proposed traffic-shaping heuristics, we endow XDense with the capabilities required for real-time applications

Artificial intelligence driven anomaly detection for big data systems

The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources. The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology. The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naïve anomaly detection training. The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces