Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1195
Scrutable Mobile Client-side Personalization
Статья научная
Personalization has become an essential feature of mobile services of different domains. On the other hand, users have conflicting needs of personalized experience and privacy. This leads to the question of how to maximize the user’s experience of personalized mobile services while keeping privacy. One possible solution is to provide user’s control of their personal data by keeping their user model on their personal mobile devices. In this way, a user can scrutinize the data while sharing with service providers depending on her/his requirements. The client-side personalization approach can shift the control of privacy to the users and can involve them in personalization process. Transparency and user control can increase the user’s trust. In this paper, we have proposed a solution with the objective of scrutable client-side personalization while keeping the user in control of both privacy and personalization process.
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Secluding Efficient Geographic Multicast Protocol against Multicast Attacks
Статья научная
A Mobile Ad-hoc Network (MANETs) is composed of Mobile Nodes without any infrastructure. The network nodes in MANETs, not only act as ordinary network nodes but also as the routers for other peer devices. The dynamic topology, lack of a fixed infrastructure and the wireless nature make MANETs susceptible to the security attacks. To add to that, due to the inherent, severe constraints in power, storage and computational resources in the MANET nodes, incorporating sound defense mechanisms against such attacks is also non-trivial. Therefore, interest in research of Mobile Ad-hoc NETworks has been growing since last few years. Security is a big issue in MANETs as they are infrastructure-less and autonomous. The main objective of this paper is to address some basic security concerns in EGMP protocol which is a multicast protocol found to be more vulnerable towards attacks like blackhole, wormhole and flooding attacks. The proposed technique uses the concepts of certificate to prevent these attacks and to find the malicious node. These attacks are simulated using NS2.28 version and the proposed proactive technique is implemented. The following metrics like packet delivery ratio, control overhead, total overhead and End to End delay are used to prove that the proposed solution is secure and robust.
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Secure Hajj Permission Based on Identifiable Pilgrim’s Information
Статья научная
Event management of large international events is attracting interest from researchers, not least due to the potential use of technology to provide support throughout the different stages of the event. Some events, such as major sports or religious events, can involve millions of people from different countries, and require active management to control access (e.g. many popular events can be oversubscribed) and to reduce risks for the participants, local communities and environment. This paper explores the context of a large event - the Hajj pilgrims in Saudi Arabia - which involves up to three million pilgrims, many of whom are international. The paper presents a novel identification system - the Identification Wristband Hajj Permission (IWHP) - which uses encryption technologies and biometric attributes to identify pilgrims, whilst remaining sensitive to the context of the Hajj. The suggested solution has many attributes of relevance that could support its use in other large-crowd events.
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Secured and Optimized AODV for Wireless Sensor Networks
Статья научная
Similar to conventional wireless networks, WSNs are based on multi hop routing to ensure connectivity and data forwarding which makes the routing service a challenging task due to the nature of sensors usually limited in memory, battery and computing capacities as well as the nature of the environment which is hostile and unpredictable making the routing protocols developed for conventional wireless network useless for WSNs without modifications and adaptations for the new context of WSNs. Thus in this paper we present an optimized version of AODV protocol for WSNs which takes into consideration the traffic pattern of WSNs and sensors’ constraints. In the proposed protocol we affect the task of route discovery to the base station which periodically informs sensors about its location instead of letting this task to sensors which consumes the network resources due to the broadcasting nature of the route discovery. We have also proposed a key distribution scheme destined to establish a symmetric encrypting key between each sensor and the base station, the proposed key management scheme uses the underlying routing requests to execute handshakes and key update which have greatly saved the network resources and ensured a good threshold of security.
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Secured biometric identification: hybrid fusion of fingerprint and finger veins
Статья научная
The goal of this work is the improvement of the performance of a multimodal biometric identification system based on fingerprints and finger vein recognition. This system has to authenticate the person identity using features extracted from his fingerprints and finger veins by multimodal fusion. It is already proved that multimodal fusion improves the performance of biometric recognition, basically the fusion at feature level and score level. However, both of them showed some limits and in order to enhance the overall performance, a new fusion method has been proposed in this work; it consists on using both features and scores fusion at the same time. The main contribution of investigation this technique of fusion is the reduction of the template size after fusion without influencing the overall performance of recognition. Experiments were performed on a real multimodal database SDUMLA-HMT and obtained results showed that as expected multimodal fusion strategies achieved the best performances versus uni-modal ones, and the fusion at feature level was better than fusion at score level in recognition rate (100%, 95.54% respectively) but using more amounts of data for identification. The proposed hybrid fusion strategy has overcome this limit and clearly preserved the best performance (100% as recognition rate) and in the same time it has reduced the proportion of essential data necessary for identification.
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Статья научная
Industry heavyweights like Microsoft, Amazon, and Google are at the forefront of the development and provision of cutting-edge and affordable cloud computing solutions, contributing to the widespread recognition of cloud computing. Without requiring direct human control, this technology provides network services, including data storage and computational power. But security becomes apparent as a major issue, hindering widespread adoption. The present study performs an extensive investigation to investigate security concerns related to cloud computing at several infrastructure levels, including application, network, host, and data. It examines significant issues that could impact the business model for cloud computing and discuss ways to solve security issues at every level that have been documented in the literature. The study identifies open problems, especially when considering cloud capabilities like elasticity, flexibility, and multi-tenancy, which create new problems at every infrastructure tier. Notably, it is found that multi-tenancy has a significant influence, contributing to security issues at all levels including abuse, unavailability, data loss, and privacy violations. The research ends with practical recommendations for additional studies targeted at improving overall cloud computing security. The results highlight the necessity of concentrated effort on mitigating security vulnerabilities resulting from multi-tenancy. This study makes a valuable contribution to the wider discussion on cloud security by identifying particular issues and supporting focused initiatives to strengthen the resilience of cloud infrastructure.
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Статья научная
An increase in cyber threats directed at interconnected devices has resulted from the proliferation of the Internet of Things (IoT), which necessitates the implementation of comprehensive defenses against evolving attack vectors. This research investigates the utilization of machine learning (ML) prediction models to identify and defend against cyber-attacks targeting IoT networks. Central emphasis is placed on the thorough examination of the CIC-IoT2023 dataset, an extensive collection comprising a wide range of Distributed Denial of Service (DDoS) assaults on diverse IoT devices. This ensures the utilization of a practical and comprehensive benchmark for assessment. This study develops and compares four distinct machine learning models Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) to determine their effectiveness in detecting and preventing cyber threats to the Internet of Things (IoT). The comprehensive assessment incorporates a wide range of performance indicators, such as F1-score, accuracy, precision, and recall. Significantly, the results emphasize the superior performance of DT and RF, demonstrating exceptional accuracy rates of 0.9919 and 0.9916, correspondingly. The models demonstrate an outstanding capability to differentiate between benign and malicious packets, as supported by their high precision, recall, and F1 scores. The precision-recall curves and confusion matrices provide additional evidence that DT and RF are strong contenders in the field of IoT intrusion detection. Additionally, KNN demonstrates a noteworthy accuracy of 0.9380. On the other hand, LR demonstrates the least accuracy with a value of 0.8275, underscoring its inherent incapability to classify threats. In conjunction with the realistic and diverse characteristics of the CIC-IoT2023 dataset, the study's empirical assessments provide invaluable knowledge for determining the most effective machine learning algorithms and fortification strategies to protect IoT infrastructures. Furthermore, this study establishes ground-breaking suggestions for subsequent inquiries, urging the examination of unsupervised learning approaches and the incorporation of deep learning models to decipher complex patterns within IoT networks. These developments have the potential to strengthen cybersecurity protocols for Internet of Things (IoT) ecosystems, reduce the impact of emergent risks, and promote robust defense systems against ever-changing cyber challenges.
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Статья научная
The rise of the Internet accelerates the creation of various large-scale online social networks, which can be described the relationships and activities between human beings. The online social networks relationships in real world are too big to present with useful information to identify the criminal or cyber-attacks. This research proposed new information security analytic model for online social networks, which called Security Visualization Analytics (SVA) Model. SVA Model used the set of algorithms (1) Graph-based Structure algorithm to analyze the key factors of influencing nodes about density, centrality and the cohesive subgroup to identify the influencing nodes of anomaly and attack patterns (2) Supervised Learning with oneR classification algorithm was used to predict new links from such influencing nodes in online social networks on discovering surprising links in the existing ones of influencing nodes, which nodes in online social networks will be linked next from the attacked influencing nodes to monitor the risk. The results showed 42 influencing nodes of anomaly and attack patterns and can be predict 31 new links from such nodes were achieved by SVA Model with the accuracy of confidence level 95.0%. The new proposed model and results illustrated SVA Model was significance analysis. Such understanding can lead to efficient implementation of tools to links prediction in online social networks. They could be applied as a guide to further investigate of social networks behavior to improve the security model and notify the risk, computer viruses or cyber-attacks for online social networks in advance.
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Security in Fog Computing through Encryption
Статья научная
Cloud computing is considered as one of the most exciting technology because of its flexibility and scalability. The main problem that occurs in cloud is security. To overcome the problems or issues of security, a new technique called fog-computing is evolved. As there are security issues in fog even after getting the encrypted data from cloud, we implemented the process of encryption using AES algorithm to check how it works for the fog. So far, to our analysis AES algorithm is the most secured process of encryption for security. Three datasets of different types are considered and applied the analysed encryption technique over those datasets. On validation, entire data over datasets is being accurately encrypted and decrypted back as well. We took android mobile as an edge device and deployed the encryption over datasets into it. Further, performance of encryption is evaluated over selected datasets for accuracy if the entire data is correctly encrypted and decrypted along with the time, User load, Response time, Memory Utilization over file size. Further best and worst cases among the datasets are analysed thereby evaluating the suitability of AES in fog.
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Segmentation of Isolated and Touching Characters in Offline Handwritten Gurmukhi Script Recognition
Статья научная
Segmentation of a word into characters is one of the important challenges in optical character recognition. This is even more challenging when we segment characters in an offline handwritten document. Touching characters make this problem more complex. In this paper, we have applied water reservoir based technique for identification and segmentation of touching characters in handwritten Gurmukhi words. Touching characters are segmented based on reservoir base area points. We could achieve 93.51% accuracy for character segmentation with this method. If the characters are neither broken nor overlapping, then this technique shall produce even better results.
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Selecting Appropriate Requirements Management Tool for Developing Secure Enterprises Software
Статья научная
This paper discusses about the significance of selecting right requirements management tools. It’s no secret that poorly understood user requirements and uncontrolled scope creep to many software project failures. Many of the application development professionals buy wrong tools for the wrong reasons. To avoid purchasing the more complex and expensive tool, the organization needs to be realistic about the particular problem for which they opt. Software development organizations are improving the methods, they use to gather, analyze, trace, document, prioritize and manage their requirements. This paper considers four leading Requirements Management tools; Analyst Pro, CORE, Cradle and Caliber RM, the focus is to select the appropriate tool according to their capabilities and customers need.
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Selecting appropriate metrics for evaluation of recommender systems
Статья научная
The abundance of information on the web makes it difficult for users to find items that meet their information need effectively. To deal with this issue, a large number of recommender systems based on different recommender approaches were developed which have been used successfully in a wide variety of domains such as e-commerce, e-learning, e-resources, and e-government among others. Moreover, in order for a recommender system to generate good quality of recommendations, it is essential for a researcher to find the most suitable evaluation metric which best matches a given recommender algorithm and a recommender's task. However, with the availability of several recommender tasks, recommender algorithms, and evaluation metrics, it is often difficult for a researcher to find their best combination. This paper aims to discuss various evaluation metrics in order to help researchers to select the most appropriate metric which matches a given task and an algorithm so as to provide good quality of recommendations.
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Статья научная
The process of health insurance policy selection is a critical decision with far–reaching financial implications. The complexity of health insurance policy selection necessitates a structured approach to facilitate informed decision-making amidst numerous criteria and provider options. This study addresses the health insurance policy selection problem by employing a comprehensive methodology integrating Spherical Fuzzy Analytic Hierarchy Process (SF–AHP) and Combined Compromise Solution (CoCoSo) Algorithm. Eight experienced experts, four from academia and industry each, were engaged, and eleven critical factors were identified through literature review, survey, and expert opinions. SF–AHP was utilized to assign weights to these factors, with Claim settlement ratio (C9) deemed the most significant. Subsequently, CoCoSo Algorithm facilitated the ranking of insurance service providers, with alternative A6 emerging as the superior choice. The research undertakes sensitivity analysis, confirming the stability of the model across various scenarios. Notably, alternative A6 consistently demonstrates superior performance, reaffirming the reliability of the decision-making process. The study’s conclusion emphasizes the efficacy of the joint SF–AHP and CoCoSo approach in facilitating informed health insurance policy selection, considering multiple criteria and their interdependencies. Practical implications of the research extend to individuals, insurance companies, and policymakers. Individuals benefit from making more informed choices aligned with their healthcare needs and financial constraints. Insurance companies can tailor policies to customer preferences, enhancing competitiveness and customer satisfaction. Policymakers gain insights to inform regulatory decisions, promoting fair practices and consumer protection in the insurance market. This study underscores the significance of a structured approach in navigating the intricate health insurance landscape, offering practical insights for stakeholders and laying a foundation for future research advancements.
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Selection of Optimum Rule Set of Two Dimensional Cellular Automata for Some Morphological Operations
Статья научная
The cellular automaton paradigm is very appealing and its inherent simplicity belies its potential complexity. Two dimensional cellular automata are significantly applying to image processing operations. This paper describes the application of cellular automata (CA) to various morphological operations such as thinning and thickening of binary images. The description about the selection of the optimum rule set of two dimensions cellular automata for thinning and thickening of binary images is illustrated by this paper. The selection of the optimum rule set from large search space has been performed on the basis of sequential floating forward search method. The misclassification error between the images obtained by the standard function and the one obtained by cellular automata rule is used as the fitness function. The proposed method is also compared with some standard methods and found suitable for the purpose of morphological operations.
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Self-organized Detection of Relationships in a Network
Статья научная
Multistate operations within a network result in high-dimensional, multivariate temporal data, and are useful for systems, which monitor access to network entities like resources, objects, etc. Efficient self organization of such multistate network operations stored in databases with respect to relationships amongst users or between a user and a data object is an important and a challenging problem. In this work, a layer is proposed where discovered relationship patterns amongst users are classified as clusters. This information along with attributes of involved users is used to monitor and extract existing and growing relationships. The correlation is used to help generate alerts in advance due to internal user-object interactions or collaboration of internal as well as external entities. Using an experimental setup, the evolving relationships are monitored, and clustered in the database.
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Semantic Indexing of Web Documents Based on Domain Ontology
Статья научная
The first phase of reverse engineering of web-oriented applications is the extraction of concepts hidden in HTML pages including tables, lists and forms, or marked in XML documents. In this paper, we present an approach to index semantically these two sources of information (HTML page and XML document) using on the one hand, domain ontology to validate the extracted concepts and on the other hand the similarity measurement between ontology concepts with the aim of enrichment the index. This approach will be conceived in three steps (modeling, attaching and Enrichment) and thereafter, it will be realized and implemented by examples. The obtained results lead to better re-engineering of web applications and subsequently a distinguished improvement in the web structuring.
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Semantic Multi-granular Lock model for Object Oriented Distributed Systems
Статья научная
In object oriented distributed systems (OODS), the objects are viewed as resources. Concurrency control techniques are usually applied on the database tier. This has the limitations of lack of support of legacy files and requirement of separate concurrency control mechanisms for each database model. Hence concurrency control on the objects at server tier is explored. To implement concurrency control on the objects participating in a system, the impact of method types, properties and class relationships namely inheritance, association and aggregation are to be analyzed. In this paper, the types and properties of classes and attributes are analysed. The semantics of the class relationships are analysed to ascertain their lock modes, granule sizes for defining concurrency control in OODS. It is also intended to propose compatibility matrix among all these object relationships.
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Sentiment Analysis of RSS Feeds on Sports News – A Case Study
Статья научная
With the advent of online social media, such as articles, websites, blogs, messages, posts, news channels, and by and large web content has drastically changed the way individuals take a glimpse at different things around them. Today, it's an everyday practice for some individuals to read the news on the web. Sentiment analysis (also called opinion mining) alludes to the utilization of natural language processing, content investigation, and computational linguistics to distinguish and separate subjective data in source materials. Sentiment analysis is broadly applied to online reviews, news feeds and social networking for a wide variety of applications, ranging from marketing to client services. Sentiment analysis emphasizes on the classification of textual data into positive, negative and neutral categories. This research is an endeavor to the case study that calculates news polarity or emotions on different sports feeds which may influence changes in sports news development patterns. The interest of this approach is to generate various text analytics that computes feelings from all pertinent ongoing sports news accessible out in the public domain. The significance and application value of sentiment analysis of RSS feeds in this study is to distinguish between positive feeds and negative feeds on sports that could affect readers or users minds in order to improve RSS feeds messaging broadcast among folks. The methodology utilizes the sentiment analysis techniques using two different online open-source sentiment analysis tools in Rich Site Summary (RSS) news feeds that have an influence on sports-related broadcast esteems.
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Service Based Cooperation Patterns to Support Flexible Inter-Organizational Workflows
Статья научная
Service Oriented Architecture (SOA) is a paradigm that provides important advantages like interoperability, reusability and flexibility, particularly beneficial for B2B applications. In the current paper, we consider specific architectures of inter-organizational workflows (IOWF) fairly widespread in the B2B area and implementing different cooperation schemas. Our aim is to propose new generic IOWF-architectures by using the SOA paradigm in order to obtain IOWF models flexible enough to ease their adaptation, evolution and reuse. For that, we introduce the concept of Service-Based Cooperation Pattern (SBCP) that supports the definition of IOWF models based on services. A SBCP is defined by three main dimensions: the distribution of services, the control of execution and the structure of interaction between services. Also, we define a concept of composite cooperation pattern based on the combination of elementary patterns. We illustrate our approach by a general description of our cooperation framework called “S-IOFLOW” that supports the implementation of IOWF models obeying to the described SBCP. Three main points characterize our approach: (i) the use of a pattern-based approach; (ii) the definition of composite patterns by reusing elementary ones and (iii) the support of several cooperation schemas with different types of control.
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Статья научная
This paper articulates how Service Oriented Architecture (SOA) and cloud computing together can facilitate technology setup in Telemetry (TM) processing with a case study from the Egyptian space program (ESP) and a comparative study with space situational awareness (SSA) program in European space agency (ESA), Moreover, this paper illustrates how cloud computing services and deployment models enable software and hardware decoupling and making flexible TM data analysis possible. The large amount of available computational resources facilitates a shift in approaches to software development, deployment and operations.
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