A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems

The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE's hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.This work was made possible by NPRP Grant # NPRP9-005-1-002 from the Qatar National Research Fund (a member of Qatar Foundation).Scopu

Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning

Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-demand gap, and then providing efficient incentives to encourage participant movements to maintain the desired balance. In this paper, we propose Deep-Gap, a deep learning approach based on residual learning to predict the gap between mobile crowdsourced service supply and demand at a given time and space. The prediction can drive the incentive model to achieve a geographically balanced service coverage in order to avoid the case where some areas are over-supplied while other areas are under-supplied. This allows anticipating the supply-demand gap and redirecting crowdsourced service providers towards target areas. Deep-Gap relies on historical supply-demand time series data as well as available external data such as weather conditions and day type (e.g., weekday, weekend, holiday). First, we roll and encode the time series of supply-demand as images using the Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF) and the Recurrence Plot (REC). These images are then used to train deep Convolutional Neural Networks (CNN) to extract the low and high-level features and forecast the crowdsourced services gap. We conduct comprehensive comparative study by establishing two supply-demand gap forecasting scenarios: with and without external data. Compared to state-of-art approaches, Deep-Gap achieves the lowest forecasting errors in both scenarios.Comment: Accepted at CloudCom 2019 Conferenc

Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation

Effective management of urban traffic is important for any smart city initiative. Therefore, the quality of the sensory traffic data is of paramount importance. However, like any sensory data, urban traffic data are prone to imperfections leading to missing measurements. In this paper, we focus on inter-region traffic data completion. We model the inter-region traffic as a spatiotemporal tensor that suffers from missing measurements. To recover the missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach that considers the urban and temporal aspects of the traffic. To derive the urban characteristics, we divide the area of study into regions. Then, for each region, we compute urban feature vectors inspired from biodiversity which are used to compute the urban similarity matrix. To mine the temporal aspect, we first conduct an entropy analysis to determine the most regular time-series. Then, we conduct a joint Fourier and correlation analysis to compute its periodicity and construct the temporal matrix. Both urban and temporal matrices are fed into a modified CP-completion objective function. To solve this objective, we propose an alternating least square approach that operates on the vectorized version of the inputs. We conduct comprehensive comparative study with two evaluation scenarios. In the first one, we simulate random missing values. In the second scenario, we simulate missing values at a given area and time duration. Our results demonstrate that our approach provides effective recovering performance reaching 26% improvement compared to state-of-art CP approaches and 35% compared to state-of-art generative model-based approaches

Towards NLP-based Semi-automatic Preparation of Content for Language Learning using LingoSnacks m-Learning Platform

Vocabulary growth is an important element for language learning but it requires repeated and varied exposure to the new words and their usage in different context. However preparing suitable learning content for effective language learning remains a challenging and time-consuming task. This paper reports the experience of designing and developing a m-Learning platform (named LingoSnacks) for semi-automatic preparation of content for language learning using Natural Language Processing (NLP) services. LingoSnacks Authoring Tools provide an environment of assisted authoring of learning content and delivering it to the learner in game-like interactive learning activities. Empirical testing results from teachers who used LingoSnacks indicate that the participants were able to ease their lessons preparation tasks. Also the resulting learning packages helped learners in vocabulary acquisition as the number of new vocabulary that they can recognize, recall and retain was significantly higher that participants who just used conventional lessons in a classroom

Cyber LOPA: An Integrated Approach for the Design of Dependable and Secure Cyber Physical Systems

Safety risk assessment is an essential process to ensure a dependable Cyber-Physical System (CPS) design. Traditional risk assessment considers only physical failures. For modern CPS, failures caused by cyber attacks are on the rise. The focus of latest research effort is on safety-security lifecycle integration and the expansion of modeling formalism for risk assessment to incorporate security failures. The interaction between safety and security and its impact on the overall system design, as well as the reliability loss resulting from ignoring security failures are some of the overlooked research questions. This paper addresses these research questions by presenting a new safety design method named Cyber Layer Of Protection Analysis (CLOPA) that extends existing LOPA framework to include failures caused by cyber attacks. The proposed method provides a rigorous mathematical formulation that expresses quantitatively the trade-off between designing a highly-reliable versus a highly-secure CPS. We further propose a co-design lifecycle process that integrates the safety and security risk assessment processes. We evaluate the proposed CLOPA approach and the integrated lifecycle on a practical case study of a process reactor controlled by an industrial control testbed, and provide a comparison between the proposed CLOPA and current LOPA risk assessment practice.Comment: Main Content: Title adjusted, Related work moved to end, added references, Sec IV (prev. sec V): expanded discussion, design and Alg. 1 updated | Sec V (prev. sec VI): Expanded discussion, Table V Expanded. Editorial: Fig 1 redrawn horiz., Eq (4)(5) math notation changed, same content. Eq (25) expanded, Page-wide eq. not ref as fig (shift by 1 of fig num), Fig 4 iterative design values show

Adaptive prediction models for data center resources utilization estimation

Accurate estimation of data center resource utilization is a challenging task due to multi-tenant co-hosted applications having dynamic and time-varying workloads. Accurate estimation of future resources utilization helps in better job scheduling, workload placement, capacity planning, proactive auto-scaling, and load balancing. The inaccurate estimation leads to either under or over-provisioning of data center resources. Most existing estimation methods are based on a single model that often does not appropriately estimate different workload scenarios. To address these problems, we propose a novel method to adaptively and automatically identify the most appropriate model to accurately estimate data center resources utilization. The proposed approach trains a classifier based on statistical features of historical resources usage to decide the appropriate prediction model to use for given resource utilization observations collected during a specific time interval. We evaluated our approach on real datasets and compared the results with multiple baseline methods. The experimental evaluation shows that the proposed approach outperforms the state-of-the-art approaches and delivers 6% to 27% improved resource utilization estimation accuracy compared to baseline methods.This work is partially supported by the European Research Council (ERC) under the EU Horizon 2020 programme (GA 639595), the Spanish Ministry of Economy, Industry and Competitiveness (TIN2015-65316-P and IJCI2016-27485), the Generalitat de Catalunya (2014-SGR-1051), and NPRP grant # NPRP9-224-1-049 from the Qatar National Research Fund (a member of Qatar Foundation) and University of the Punjab, Pakistan.Peer ReviewedPostprint (published version

Towards model-based management of database fragmentation

The performance of a database can significantly deteriorate due to the fragmentation of data/index files. Manual database defragmentation and performance optimization remain time consuming and even infeasible as it requires knowledge of the complicated behavior of fragmentation and its relationships with system parameters. We propose a model-based detection and management framework for the database fragmentation which can automatically optimize database performance, detect the fault existence, estimate its future impact on system performance and recover the system back to normal. A predictive controller is designed to take proper actions to guarantee the QoS and remedy faults. Experimental studies on a realistic test-bed show the applicability and effectiveness of our approach. 8th International Workshop on Feedback Computing 2013. All rights reserved.This paper was made possible by NPRP grant # NPRP 09-778-2299 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Applying and evaluating architectural patterns on a stock trading case study

Teaching architectural and design patterns is a difficult task and not easy to understand by the students as patterns are expressed at a higher abstraction level than programming language constructs or graphical UML notations. Also there is a lack of detailed examples of how patterns are applied to realistic projects and how the impact of using such patterns is evaluated. To address this issue, this paper presents detailed application and evaluation of two architectural patterns to a real Stock Trading application. The presented examples were helpful to students to get a deeper understanding of the topic. 2012 IEEE.Scopu