Forthcoming articles


International Journal of Innovative Computing and Applications


These articles have been peer-reviewed and accepted for publication in IJICA, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.


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International Journal of Innovative Computing and Applications (8 papers in press)


Regular Issues


  • Dual Neighborhood Discrete Artificial Bee Colony Algorithm for the Convex Polygons Packing Problem with Constraints of Equilibrium   Order a copy of this article
    by Zhendong Huang, Renbin Xiao 
    Abstract: For the optimal packing problem of convex polygons in a circular container with performance constraint, a hybrid algorithm which combines an improved ripple exploratory heuristic algorithm (IREHA) with dual-neighbourhood discrete artificial bee colony algorithm (DDABC) is proposed. IREHA improves search efficiency of the original REHA. Because of the excellent performance of artificial bee colony algorithm, which can not be directly used to optimize the discrete packing order, DDABC based on discrete dual neighbourhood structure is designed to combine with IREHA to form hybrid algorithm. The numerical experiments show that the hybrid algorithm is very effective.
    Keywords: Equilibrium constraints; Packing optimization; Heuristic; Discrete artificial bee colony algorithm; Dual neighbourhood.

  • RAJIVE: Restricting the Abuse of JavaScript Injection Vulnerabilities on Cloud Data Centre by Sensing the Violation in Expected Workflow of Web Applications   Order a copy of this article
    by Shashank Gupta, Brij Gupta 
    Abstract: This article introduces a novel defensive framework that detects and obstructs the exploitation of malicious JavaScript (JS) injection by spotting the violation in the expected workflow of web applications deployed on the Cloud data centers. The framework initially generates some categories of axioms by examining the strings of HTTP request and response. Likewise, it detects the deviation in the intended workflow of web application by examining the violation in such generated axioms. For the detection of suspicious JavaScript injection, our framework parses HTML and JavaScript document for retrieving the script content and injects the context-sensitive sanitization routines in such content during online mode on Cloud data centers. Conventional context-sensitive sanitization is renovated into our novel nested context-aware sanitization that not only determines the outer context but also find out the nested context of malicious attributes of JavaScript code. The prototype of our work was developed in Java development framework and installed on the virtual machines of Cloud data centers located at the core of network. Susceptible web applications were utilized for evaluating the workflow violation detection capability in order to obstruct the execution of XSS worms on the Cloud data centers. Real time evaluation results revealed that our work detects the injection of XSS worms with high precision rate and lesser rate of false positives and false negatives.
    Keywords: Cloud Security; XSS attack; JavaScript Worms; Workflow Violation (WV) Attacks; AJAX; JavaScript.

  • Blind Hyperspectral Unmixing by Nonparametric Non-Gaussianity Measure   Order a copy of this article
    by Fasong WANG 
    Abstract: For linear mixing model (LMM) of hyperspectral unmixing (HU) in hyperspectral images processing problem, the endmember fractional abundances satisfy the sum-to-one constraint, which makes the well-known independent component analysis (ICA) based blind source separation (BSS) algorithms not well suited to blind hyperspectral unmixing (bHU). A novel nonparametric bHU approach consulting dependent component analysis (DCA) is presented in this paper. The novel cost function is derived based on the cumulative density function (CDF) and order statistics (OS) instead of traditional probability density function (PDF). By executing the stochastic gradient rule of constrained optimization method, the efficient dependent sources separation algorithm for bHU is obtained to fulfill the endmember signatures extraction and abundances estimation tasks. Simulations based on the synthetic data are performed to evaluate the validity of the proposed nonpNG-bHU algorithm.
    Keywords: independent component analysis (ICA); blind source separation (BSS); blind hyperspectral unmixing (bHU); dependent component analysis (DCA).

  • Hybrid algorithms for 3-SAT optimization using MapReduce on clouds   Order a copy of this article
    by Sergio Nesmachnow, Marcos Barreto, Andrei Tchernykh 
    Abstract: This article presents the advances on applying a MapReduce approach for solving optimization problems using Hadoop on cloud computing systems. The main advantages and limitations of the proposed strategy are presented and commented. A concrete case study is reported, analyzing several algorithmic approaches to solve the 3-SAT, a well-known version of the Boolean satisfiability problem. Several variants of the MapReduce 3-SAT solver are designed and evaluated to demonstrate that the collaborative approach is a promising option for solving optimization problems in the cloud.
    Keywords: MapReduce; Hadoop; Optimization; Cloud; 3-SAT.

  • POND: Polishing the Execution of Nested Context-Familiar Runtime Dynamic Parsing and Sanitization of XSS Worms on Online Edge Servers of Fog Computing   Order a copy of this article
    by Shashank Gupta, Brij Gupta 
    Abstract: This article presents an enhanced duplex context-wise sanitization generator and dynamic parser on the hierarchical distributed structure of Cloud data centers and edge (Fog) servers for obstructing the execution of XSS worms that was recently found on HTML5 and Twitter-based web applications. Such data centers statically explores the diverse nested context of variables of script code and consequently generates the sanitized version of HTML5 documents corresponding to their contexts. Such sanitized templates of HTML5 documents are injected in the edge servers of Fog nodes. The online HTTP response generated by such edge servers undergoes through the phase of dynamic runtime parsing. This phase finds out the nested context of variables of script code that cannot be statically determined during the determination of nested context of such variables in a static manner. Finally, sanitized version of templates of HTML5 web pages are generated as an HTTP response and redirected to the network of smart devices. Cloud data centers and edge servers of Fog nodes are utilized for integrating the infrastructure settings of our prototype framework that was developed in Java developed framework. Numerous tested open source platforms of OSN were utilized for assessing the performance of runtime nested context determination and sanitization of suspicious JavaScript strings. Performance evaluation outcomes revealed that the proposed work experienced better response time at online phase and tolerable performance overhead caused due to the runtime nested context-wise parsing and sanitization of XSS worms on the edge servers of Fog nodes as compared to our existing work on the Cloud data centers.
    Keywords: Cloud Security; Fog Computing; Edge Servers; XSS Worms; Online Social Network (OSN) Security; Context-Familiar Sanitization; HTML5.

Special Issue on: Secure Data Storage in Cloud Computing

  • An Access Control Framework for Multi-level Security in Cloud Environments   Order a copy of this article
    by Hongbin Zhang, Junshe Wang, Jiang Chang, Ning Cao 
    Abstract: Access control is important for the security in cloud environments while increasing demands for secure interactions in the cloud bring new challenges to access control technologies. As a cloud always consists of many domains, in this paper we design an access control framework which provides rigorous multilevel security in single domain and a multilevel mapping method between domains. In each domain, a policy processing method is designed to handle the multilevel policies and creates a DAG model that describes the access control relationship between all entities in the domain. The DAG model can be converted to a hierarchical access control structures that ensure rigorous multilevel security in intra domains. And between domains, the mapping method based on quantized subject attributes is proposed to determine the subjects security level in its target domain. Credibility is used in the framework to adjust the mapping value in order to achieve flexible multilevel inter-domain access control. Experimental results from simulations show that proposal in this paper can realize accurate inter-domain mapping and achieve multilevel security access control in inter-domain.
    Keywords: multi-level security; cloud; access control; multi-attributes quantization; inter-domain mapping.

Special Issue on: Social Media Analysis From Misinformation to Valuable Data Source

  • Multi-level Privacy Preserving Data Publishing   Order a copy of this article
    by Zhiqiang Gao 
    Abstract: Policedata is an important source of social media data and can be regarded as a technical assistance to increase government accountability and transparency. Notably, it contains large amounts of personal private information that should be preserved deliberately. However, sharing and publishing policedata through private or public cloud infrastructure are still faced with tremendously potential threats and challenges recently: (1) with little constant vigilance of data privacy, neither data curators nor insiders of police department pay adequate attention on the process of data publishing, holding the mind that policedata can be published, transferred, data-mined and shared within inner network safely as well as conveniently. (2) multi-sourced policedata from social media, such as tabular data and web pages, result in high complexity of privacy preserving data publishing. (3) privacy preservation in policedata is still problematic and vulnerable in the complex situation of multi-level data publishing. Unfortunately, existing researches regarding privacy preserving data publishing (PPDP) fail to cope with the aforementioned problems. Our work aims to propose a systematic multi-level privacy preserving data publishing (ML-PPDP) architecture. Moreover, a personalized multi-level privacy preserving (pML-PPDP) mechanism that developed from the combination of the state-of-the-art methods including k-anonymity, l-diversity, t-closeness and differential privacy is designed for policedata publishing. Our solution authorized users with different privileges to different privacy-preserving levels. Experimental results of pML-PPDP mechanism on datasets collected from policedata website are implemented under our proposed ML-PPDP architecture with satisfactory trade-off between privacy and utility.
    Keywords: Social media policedata; Privacy preserving data publishing; Attribute-based Encryption; k-anonymity; l-diversity; t-closeness; Differential privacy.

  • The spread pattern on Ebola and the control schemes   Order a copy of this article
    by Jiangjie Sun 
    Abstract: This paper aims to study a reasonable system that includes viral transmission and disease cure of Ebola. Firstly, we utilize the logistic model to simulate the change of number of patients. Secondly, we establish the infection dynamics model to evaluate the probability of the outbreak of related region according to some close-related indexes. Finally, we make use of nonlinear programming and 0-1 integer programming to achieve the maximum efficiency for the whole system in spite of the minimum costs.
    Keywords: Reasonable system; logistic model; 0-1 integer programming.