Showing papers in "International Journal of Information Technology and Web Engineering in 2019"

A Systems Overview of Commercial Data Centers: Initial Energy and Cost Analysis

Abstract: Data center facilities play a vital role in present and forthcoming information and communication technologies. Internet giants, such as IBM, Microsoft, Google, Yahoo, and Amazon hold large data centers to provide cloud computing services and web hosting applications. Due to rapid growth in data center size and complexity, it is essential to highlight important design aspects and challenges of data centers. This article presents market segmentation of the leading data center operators and discusses the infrastructural considerations, namely energy consumption, power usage effectiveness, cost structure, and system reliability constraints. Moreover, it presents data center network design, classification of the data center servers, recent developments, and future trends of the data center industry. Furthermore, the emerging paradigm of mobile cloud computing is debated with respect to the research issues. Preliminary results for the energy consumption of task scheduling techniques are also provided.

An SVM-Based Ensemble Approach for Intrusion Detection

Abstract: The objective of this article is to develop an intrusion detection model aimed at distinguishing attacks in the network. The aim of building IDS relies on upon preprocessing of intrusion data, choosing most relevant features and in the plan of an efficient learning algorithm that properly groups the normal and malicious examples. In this experiment, the detection model uses an ensemble approach of supervised (SVM) and unsupervised (K-Means) to detect the patterns. This technique first divides the data and forms two clusters as per K-Means and labels the clusters using the Support Vector Machine (SVM). The parameters of K-Means and SVM are tuned and optimized using an intrusion dataset. The SVM provides up to 88%, and K-Means provides up to 83% accuracy individually. However, the ensemble of K-Means and SVM provides more than 99% on three benchmarked datasets in less time. The SVM only classifies three instances of each cluster randomly and labels them as per a majority voting approach. The proposed approach outperforms compared to earlier ensemble approaches on intrusion datasets.

An Approach for Load Balancing in Cloud Computing Using JAYA Algorithm

Abstract: Cloud computing is a jargon in the era of information technology. It acts as a metaphor for the internet. Still, it possesses several challenges related to automated resource provisioning, security, event content dissemination, server consolidation, virtual machine migration. Load balancing is one of the critical challenges in the cloud that are faced by the enterprises. Here the basic objective of load balancing is to minimize response time, data center service request time, and improve the overall performance of the system. In this article, a JAYA algorithm is used for load balancing in a cloud which uses less control parameters and provides a better optimized result. Comparisons are made with other evolutionary approaches to observe the efficiency of the proposed algorithm.

A Customized Quality Model for Software Quality Assurance in Agile Environment

Abstract: The agile approach grew dramatically over traditional approaches. The methodology focuses more on rapid development, quick evaluation, quantifiable progress and continuous delivery satisfying the customer desire. In view of this, there is a need for measurement of the agile development process. In this respect, the present research work investigates the inter-relationships and inter-dependencies between the identified quality factors (QF), thereby outlining which of these QF have high driving power and dependence power, working indirectly towards the success of agile development process. This paper proposes a new agile quality model, utilizing an interpretive structural modeling (ISM) approach and the identified factors are classifies using Matriced' Impacts Croise's Multiplication Applique´e a UN Classement (MICMAC) approach. The research findings can significantly impact agile development process by understanding how these QF related to each other and how they can be adopted.

GANDIVA: Temporal Pattern Tree for Similarity Profiled Association Mining

Abstract: In this research, the authors propose a novel tree structure called GANDIVA which computes true supports of all temporal itemsets by performing a tree-based scan and eliminating the database scan which is required for SPAMINE, G-SPAMINE, MASTER, and Z-SPAMINE approaches. The idea is to construct the tree called GANDIVA which determines support of all time-stamped temporal itemsets from the constructed tree. Another important advantage of the proposed approach is that it does not require the original database to be retained in the memory after a time profiled pattern tree (GANDIVA) is constructed from the original database. The significant advantage of GANDIVA over SPAMINE, G-SPAMINE, Z-SPAMINE, and MASTER is that GANDIVA requires zero database scans after the tree construction. GANDIVA is the pioneering research to propose a novel tree-based framework for seasonal temporal data mining. KEyWoRDS Distance Bounds, Interest Measure, Membership Value, Pattern Pruning, Support, Temporal Itemset, Temporal Pattern, Time Profiled, Time Stamp Transaction Database

An Improved Task Scheduling Mechanism Using Multi-Criteria Decision Making in Cloud Computing

Abstract: In this work, the authors propose Multi-criteria Decision-making to schedule deadline based tasks in cloud computing. The existing backfilling task scheduling algorithm could not handle similar tasks for scheduling. In backfilling algorithm, tasks are backfilled to provide ideal resources to schedule other deadline sensitive tasks. However, the task to be backfilled is selected on first come, first serve (FCFS) basis from scheduling queue. The scheduling performances require to be improved when, there are similar tasks. In this proposed work, the authors propose to implement MCDM technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to improve the performance of the backfilling algorithm through scheduling deadline sensitive tasks in cloud computing. It resolves the conflicts among the similar tasks that is used as a decision support system. The work is simulated with synthetic data sets based on slack values of the tasks. The performance results affirm the task completion and reduction in task rejection compared to the existing backfilling algorithm.

A Framework for Preserving the Privacy of Online Users Against XSS Worms on Online Social Network

Abstract: This article presents a hybrid framework i.e. OXSSD (Online Social Network-Based XSS-Defender) that explores cross-site scripting (XSS) attack vectors at the vulnerable points in web applications of social networks. Initially, during training phase, it generates the views for each request and formulates the access control list (ACL) which encompasses all the privileges a view can have. It also ascertains all possible injection points for extracting malicious attack vectors. Secondly, during recognition phase, after action authentication XSS attack vectors are retrieved from the extracted injection points followed by the clustering of these attack vectors. Finally, it sanitizes the compressed clustered template in a context-aware manner. This context-aware sanitization ensures efficient and accurate alleviation of XSS attacks from the OSN-based web applications. The authors will evaluate the detection capability of OXSSD on a tested suite of real world OSN-based web applications (Humhub, Elgg, WordPress, Drupal and Joomla). The performance analysis revealed that OXSSD detects injection of illicit attack vectors with very low false positives, false negatives and acceptable performance overhead.

An Evolutionary Feature Clustering Approach for Anomaly Detection Using Improved Fuzzy Membership Function: Feature Clustering Approach for Anomaly Detection

Abstract: Traditionally, IDS have been developed by applying machine learning techniques and followed single learning mechanisms or multiple learning mechanisms. Dimensionality is an important concern which affects classification accuracies and eventually the classifier performance. Feature selection approaches are widely studied and applied in research literature. In this work, a new fuzzy membership function to detect anomalies and intrusions and a method for dimensionality reduction is proposed. CANN could not address R2L and U2R attacks and have completely failed by showing these attack accuracies almost zero. Following CANN, the CLAPP approach has shown better classifier accuracies when compared to classifiers kNN, and SVM. This research aims at improving the accuracy achieved by CLAPP, CANN, and kNN. Experimental results show accuracies obtained using proposed approach is better when compared to other existing approaches. In particular, the detection of U2R and R2L attacks to user accuracies are recorded to be very much promising.

Query Optimization in Crowd-Sourcing Using Multi-Objective Ant Lion Optimizer

Abstract: Nowadays, query optimization is a biggest concern for crowd-sourcing systems, which are developed for relieving the user burden of dealing with the crowd. Initially, a user needs to submit a structured query language (SQL) based query and the system takes the responsibility of query compiling, generating an execution plan, and evaluating the crowd-sourcing market place. The input queries have several alternative execution plans and the difference in crowd-sourcing cost between the worst and best plans. In relational database systems, query optimization is essential for crowd-sourcing systems, which provides declarative query interfaces. Here, a multi-objective query optimization approach using an ant-lion optimizer was employed for declarative crowd-sourcing systems. It generates a query plan for developing a better balance between the latency and cost. The experimental outcome of the proposed methodology was validated on UCI automobile and Amazon Mechanical Turk (AMT) datasets. The proposed methodology saves 30%-40% of cost in crowd-sourcing query optimization compared to the existing methods.

Scalable l-Diversity: An Extension to Scalable k-Anonymity for Privacy Preserving Big Data Publishing

Abstract: Privacy preserving data publishing is one of the most demanding research areas in the recent few years. There are more than billions of devices capable to collect the data from various sources. To preserve the privacy while publishing data, algorithms for equivalence class generation and scalable anonymization with k-anonymity and l-diversity using MapReduce programming paradigm are proposed in this article. Equivalence class generation algorithms divide the datasets into equivalence classes for Scalable k-Anonymity (SKA) and Scalable l-Diversity (SLD) separately. These equivalence classes are finally fed to the anonymization algorithm that calculates the Gross Cost Penalty (GCP) for the complete dataset. The value of GCP gives information loss in input dataset after anonymization.

An Adaptive Approach Towards Computation Offloading for Mobile Cloud Computing

Abstract: The widespread acceptability of mobile devices in present times have caused their applications to be increasingly rich in terms of the functionalities they provide to the end users. Such applications might be very prevalent among users but the execution results in dissipating many of the device end resources. Mobile cloud computing (MCC) has a solution to this problem by offloading certain parts of the application to cloud. At the first place, one might find computation offloading quite promising in terms of saving device end resources but eventually may result in being the other way around if performed in a static manner. Frequent changes in device end resources and computing environment variables may lead to a reduction in the efficiency of offloading techniques and even cause a drop in the quality of service for applications involving the use of real-time information. In order to overcome this problem, the authors propose an adaptive computation offloading framework for data stream applications wherein applications are partitioned dynamically followed by being offloaded depending upon the device end parameters, network conditions, and cloud resources. The article also talks about the proposed algorithm that depicts the workflow of the offloading model. The proposed model is simulated using the CloudSim simulator. In the end, the authors illustrate the working of the proposed system along with the simulated results.

An Attribute-Based Assured Deletion Scheme in Cloud Computing

Abstract: With the rapid development of cloud computing, it has been increasingly attractive for individuals and groups to store and share data via cloud storage. Once stored in the third-party cloud storage service providers, the privacy and integrity of outsourced data should be attached with more attention as a challenging task. This article presents the attribute-based assured deletion scheme (AADS) which aims to protect and assuredly delete outsourced data in cloud computing. It encrypts outsourced data files with standard cryptographic techniques to guarantee the privacy and integrity, and assuredly deletes data upon revocations of attributes. AADS could be applied to solve important security problems by supporting fine-grained attribute-based policies and their combinations. According to the comparison and analysis, AADS provides efficient data encryption and flexible attribute-based assured deletion for cloud-stored data with an acceptable concession in performance cost.

A Novel Scalable Signature Based Subspace Clustering Approach for Big Data

Abstract: “Big data” as the name suggests is a collection of large and complicated data sets which are usually hard to process with on-hand data management tools or other conventional processing applications. A scalable signature based subspace clustering approach is presented in this article that would avoid identification of redundant clusters. Various distance measures are utilized to perform experiments that validate the performance of the proposed algorithm. Also, for the same purpose of validation, the synthetic data sets that are chosen have different dimensions, and their size will be distributed when opened with Weka. The F1 quality measure and the runtime of these synthetic data sets are computed. The performance of the proposed algorithm is compared with other existing clustering algorithms such as CLIQUE.INSCY and SUNCLU.

Feature Optimization in Sentiment Analysis by Term Co-occurrence Fitness Evolution (TCFE)

Abstract: The opinion of a target audience is a major objective for the assessing state of efficacy pertaining to reviews, business decisions surveys, and such factors that require decision making. Feature selection turns out to be a critical task for developing robust and high levels of classification while decreasing training time. Models are required for stating the scope for depicting optimal feature selection for escalating feature selection strategies to escalate maximal accuracy in opinion mining. Considering the scope for improvement, an n-gram feature selection approach is proposed where optimal features based on term co-occurrence fitness is proposed in this article. Genetic algorithms focus on determining the evolution and solution to attain deterministic and maximal accuracy having a minimal level of computational process for reflecting on the sentiment scope for sentiment. Evaluations reflect that the proposed solution is capable, which outperforms the separate filter-oriented feature selection models of sentiment classification.

Enhanced Event Detection in Twitter Through Feature Analysis

Abstract: The Digital era has the benefits in unearthing a large amount of imperative material. One such digital document is social media data, which when processed can give rise to information which can be helpful to our society. One of the many things that we can unearth from social media is events. Twitter is a very popular microblog that encompasses fruitful and rich information on real world events and popular topics. Event detection in view of situational awareness for crisis response is an important need of the current world. The identification of tweets comprising information that may assist in help and rescue operation is crucial. Most pertinent features for this process of identification are studied and the inferences are given in this article. The efficiency and practicality of the features are discussed here. This article also presents the results of experimentation carried out to assess the most relevant combination of features for improved performance in event detection from Twitter.

Time-Based QoS Prediction and Rank Aggregation of Web Services

Abstract: Everyday activities are equipped with smart intellectual possessions in the modern Internet domain for which a wide range of web services are deployed in business, health-care systems, and environmental solutions. Entire services are accessed through web applications or hand-held computing devices. The recommender system is more prevalent in commercial applications. This research predicts the preference of consumers and lists the recommended services in order of ranking for consumers to choose services in a short time span. This proposed approach aims to offer the exact prediction of missing QoS (quality of service) value of web services at a specified time slice. The uncertainty of QoS value has been predicted using the cloud model theory. The focus is to give the global ranking using the aggregated ranking of the consumer's ranking list, which has been obtained through the Kemeny optimal aggregation algorithm. In this work, multidimensional QoS data of web services have experimented and given an accurate prediction and ranking in the web environment.

A hybrid pre-post constraint-based framework for discovering multi-dimensional association rules using ontologies

Abstract: Association rule mining is a very useful knowledge discovery technique to identify co-occurrence patterns in transactional data sets. In this article, the authors proposed an ontology-based framework to discover multi-dimensional association rules at different levels of a given ontology on user defined pre-processing constraints which may be identified using, 1) a hierarchy discovered in datasets; 2) the dimensions of those datasets; or 3) the features of each dimension. The proposed framework has post-processing constraints to drill down or roll up based on the rule level, making it possible to check the validity of the discovered rules in terms of support and confidence rule validity measures without re-applying association rule mining algorithms. The authors conducted several preliminary experiments to test the framework using the Titanic dataset by identifying the association rules after pre- and post-constraints are applied. The results have shown that the framework can be practically applied for rule pruning and discovering novel association rules.

Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing

Abstract: In this article, the authors propose a novel backfilling-based task scheduling algorithm to schedule deadline-based tasks. The existing backfilling algorithm has some performance issues in comparison with the number of task scheduling in OpenNebula cloud platform. A lease could not be scheduled if it is not sorted with respect to its start time. In backfilling, a lease is selected in First Come First Serve (FCFS) to be backfilled from the queue in which some ideal resources can be found out and allocated to other leases. However, the scheduling performance is not better if there are similar types of leases to backfill. It requires a decision maker to resolve conflicts. The proposed approach schedules the number of tasks without any decision maker. An additional queue and the current time of the system is implemented to improve the scheduling performance. It performs quite satisfactorily in terms of number of a leases scheduling, and resource utilization. The performance result is compared with the existing backfilling algorithms.

HBSD: A Hadoop Based Service Discovery Model for Enterprise Cloud Bus

Abstract: Enterprise cloud bus (ECBS) is a multi-agent-based abstraction layer framework, responsible for publishing and discovery of services in an Inter-cloud environment. Our work focuses on the service discovery model (HBSD) using Hadoop that leads to the challenges of automatic web service discovery patterns. It has been observed that the RDBMS can handle only data sizes up to a few Terabytes but fails to scale beyond that, so Apache Hadoop can be used for parallel processing of massive datasets. This article provides a novel Hadoop based Service Discovery (HBSD) approach that can handle vast amount of datasets generated from heterogeneous cloud services. The novelty of the proposed architecture coordinates cloud participants, automate service registration pattern, reconfigure discover services and focus on aggregating heterogeneous services from Inter-cloud environments. Moreover, this particle states a novel and efficient algorithm (HBSDMCA) for finding the appropriate service as per user’s requirements that can provide higher QoS to the user request for web services. KEyWoRDS Enterprise Cloud Bus System, Hadoop, HBSD, Hive, HQL, HUDDI, Service Discovery, XML Schema

An Enhanced and Efficient Multi-View Clustering Trust Inference Approach by GA Model

Abstract: Multi-view affinity propagation (MAP) methods are widely accepted techniques, measure the within-view clustering and clustering consistency. These suffer from similarity and correlation between clusters. The trust and similarity measured was introduced as a new approach to overcome the problem. But these approaches suffer from low accuracy and coverage due to avoidance of implicit trust. So, a framework called multi-view clustering based on gray affinity (MVC-GA) created by integrating both similarity and implicit trust. Similarity between two clusters is obtained by applying the Pearson Correlation Coefficient-based similarity. It utilizes the collaborative filter-based trust evaluation for each clustered view in terms of the similarity based on the gray affinity nn algorithm. Classification of incomplete occurrences is addressed based on GA Function. Experiments on the benchmark data sets have been performed to validate the proposed framework. It is shown that MVC-GA can improve the multi-view clustering accuracy and coverage.

A Multicriteria Group Decision Support System: An Approach Based Agents and Web Services

Abstract: In the present study, the authors propose a group decision support system (Web-GDSS), which allows multi-agents systems and multicriteria analysis systems to help decision-makers in order to obtain a collective decision, using web services. The proposed system operates on two main stages. First, decision-makers are in a different location away from each other. They must store their location in databases and invoke the appropriate web service. Second, in the case of negotiation between decision-makers, monotonic concession protocol will lead to an agreement using CONDORCET and BORDA voting methods.

Toward a More Robust Home Automated System: Introducing a Voice Activation Technique via Pubnub

Abstract: The work reported in this article developed a home automated system using voice activation. This is with a view to providing users complete control over electrical appliances using simple easy to remember voice commands on an Android mobile device. This work was implemented using the Atmega 328 microcontroller, Relays and a Wi-Fi shield. The human voice is first converted to text using a Natural language processing tool from the Android based application. Thereafter, the text is sent over the internet via the PubNub to the microcontroller. The Atmega 328 microcontroller was programmed on an Arduino using C programming language and the Android based application was developed using Android Software Development Kit. Results obtained from the testing show that the implemented system achieves the mean scores of 8, 7.6, and 7.2 for ease of use, learnability and effectiveness respectively justifying the fact that the system is capable of controlling appliances by changing their state (ON/OFF) from remote a location with a response time within the reasonable limit.