Search results

Assessing project criticality in a project portfolio (PP) is of great practical significance to improve robustness from damage. While project criticality assessment has increased diversity in approaches, the understanding of vulnerable project impacts is still limited. To promote a better understanding of assessing project criticality, a vulnerability measurement model is constructed.

First, integrating the tasks, projects and corresponding relationships among them, a project portfolio network (PPN) is constructed. Second, the project's vulnerability is measured by combining the topological structure and functional attributes. Third, project criticality is assessed by the vulnerability measurement results. Lastly, the proposed model is applied in a numerical example to illustrate its suitability and effectiveness.

For academia, this study provides a novel perspective on project vulnerability measurement and expands project criticality assessment tools. For practitioners, the straightforward model provides an effective tool for assessing project criticality and contributes to enhancing project portfolio management (PPM).

The impact of the task on the project is considered in this study. Topological structure and functional attributes are also integrated for measuring project vulnerability due to the impact of random attacks in an uncertain environment, providing a new perspective on the requirements of project criticality assessment and the measurement of project vulnerability.

Abnormal behaviors such as rework, backlog, changes and claims generated by project organizations are unavoidable in complex projects. When abnormal behaviors emerge, the previously normal state of interactions between organizations will be altered to some extent. However, previous studies have ignored the associations and interactions between organizations in the context of abnormal organizational behaviors (AOBs), making this challenging to cope with AOBs. As a result, the objective of this paper is to explore how to reduce AOBs in complex projects at the organizational level from a network perspective.

To overcome the inherent limitations of a single case study, this research integrated two data collection methods: questionnaire survey and expert scoring method. The questionnaire survey captured the universal data on the influence possibility of AOBs between complex project organizations and the expert scoring method got the influence probability scores of AOBs between organizations in the case. Using these data, four organizational influence network models of AOBs based on a case were developed to demonstrate how to destroy AOBs networks in complex projects using network attack theory (NAT).

First, the findings show that controlling AOBs generated by key organizations preferentially and improving the ability of key organizations can weaken AOBs network, enabling more effective coping strategies. Second, the owners, government, material suppliers and designers are identified as key organizations across all four influence networks of AOBs. Third, change and claim behaviors are more manageable from the organizational level.

Project managers can target specific organizations for intervention, weaken the AOBs network by applying NAT and achieve better project outcomes through coping strategies. Additionally, by taking a network perspective, this research provides a novel approach to comprehending the associations and interactions between organizations in the context of complex projects.

This paper proposes a new approach to investigating AOBs in complex projects by simultaneously examining rework, backlog, change and claim. Leveraging NAT as a novel tool for managing the harmful effects of influence networks, this study extends the knowledge body in the field of organizational behavior (OB) management and complex project management.

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.

The construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of accidents. The neglect of the interactions may lead to serious underestimation of safety risks. This research aims to analyze the cascading vulnerability of unsafe behaviors of construction workers from the perspective of network modeling.

An unsafe behavior network of construction workers and a cascading vulnerability analysis model were established based on 296 actual accident cases. The cascading vulnerability of each unsafe behavior was analyzed based on the degree attack strategy.

Complex network with 85 unsafe behavior nodes is established based on the collected accidents in total. The results showed that storing in improper location, does not wear a safety helmet, working with illness and working after drinking are unsafe behaviors with high cascading vulnerability. Coupling analysis revealed that differentiated management strategies of unsafe behaviors should be applied. Besides, more focus should be put on high cascading vulnerability behaviors.

This research proposed a method to construct the cascading failure model of unsafe behavior for individual construction workers. The key parameters of the cascading failure model of unsafe behaviors of construction workers were determined, which could provide a reference for the research of cascading failure of unsafe behaviors. Additionally, a dynamic vulnerability research framework based on complex network theory was proposed to analyze the cascading vulnerability of unsafe behaviors. The research synthesized the results of dynamic and static analysis and found the key control nodes to systematically control unsafe construction behaviors.

The purpose of this paper is to attempt to realize the optimization of cascading failure process of urban transit network based on Load‐Capacity model, for better evaluating and improving the operation of transit network.

Robustness is an essential index of stability performance for urban transit systems. In this paper, firstly, the static robustness of transit networks is analyzed based on the complex networks theory. Aiming at random and intentional attack, a concrete algorithm process is proposed on the basis of Dijstra algorithm. Then, the dynamic robustness of the networks, namely cascading failure, is analyzed, and the algorithm process is presented based on the Load‐Capacity model. Finally, the space‐of‐stations is adopted to build the network topology of Foshan transit network, and then the simulation analyses of static and dynamic robustness are realized.

Results show that transit network is robust to random attack when considering static robustness, but somewhat vulnerable to intentional attack. For dynamic robustness analysis, a large‐scale cascade of transit network may be triggered when the tolerance parameter α is less than a value, so that the robustness of transit network can be improved through some reasonable measures.

The results of this study provide useful information for urban transit network robustness optimization.

An effective method for analyzing the static and dynamic robustness of transit network is provided in this paper.

Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites.

A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones.

Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026.

The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection.

The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.

The impact of cyberattacks all over the world has been increasing at a constant rate every year. Performing exploratory analysis helps organizations to identify, manage and safeguard the information that could be vulnerable to cyber-attacks. It encourages to the creation of a plan for security controls that can help to protect data and keep constant tabs on threats and monitor their organization’s networks for any breaches.

The purpose of this experimental study is to state the use of data science in analyzing data and to provide a more detailed view of the most common cybersecurity attacks, what are the most accessed logical ports, visible patterns, as well as the trends and occurrence of attacks. The data to be processed has been obtained by aggregating data provided by a company’s technology department, which includes network flow data produced by nine different types of attacks within every day user activities. This could be insightful for many companies to measure the damage caused by these breaches but also gives a foundation for future comparisons and serves as a basis for proactive measures within industry and organizations.

The most common cybersecurity attacks, most accessed logical ports and their visible patterns were found in the acquired data set. The strategies, which attackers have used with respect to time, type of attacks, specific ports, IP addresses and their relationships have been determined. The statistical hypothesis was also performed to check whether attackers were confined to perform random attacks or to any specific machines with some pattern.

Policies can be suggested such that if an attack is conducted on a specific machine, which can be prevented by identifying the machine, ports and duration of the attacks on which the attacker is targeting and to formulate such policies that the organization should follow to tackle these targeted attacks in the future.

Highlights the common security pitfalls in cryptographic design. States that even strong encryption algorithms can be circumvented by bypassing the algorithms altogether and exploiting errors in the design, implementation, or installation. Examines attacks against passwords, hardware, trust models, failure recovery systems and users. Discusses the importance of attack detection in addition to attack prevention. Concludes that eventually, every system may be successfully attacked so it is essential to limit the damage that can be done and to capture evidence in the form of an audit trail for later use in a prosecution.

Fueled by the rapid growth of internet, steganography has emerged as one of the promising techniques in the communication system to obscure the data. Steganography is defined as the process of concealing the data or message within media files without affecting the perception of the image. Media files, like audio, video, image, etc., are utilized to embed the message. Nowadays, steganography is also used to transmit the medical information or diagnostic reports. The paper aims to discuss these issues.

In this paper, the novel wavelet transform-based steganographic method is proposed for secure data communication using OFDM system. The embedding and extraction process in the proposed steganography method exploits the wavelet transform. Initially, the cost matrix is estimated by the following three aspects: pixel intensity, edge transformation and wavelet transform. The cost estimation matrix provides the location of the cover image where the message is to be entrenched. Then, the wavelet transform is utilized to embed the message into the cover image according to the cost value. Subsequently, in the extraction process, the wavelet transform is applied to the embedded image to retrieve the message efficiently. Finally, in order to transfer the secret information over the channel, the newly developed wavelet-based steganographic method is employed for the OFDM system.

The experimental results are evaluated and performance is analyzed using PSNR and MSE parameters and then compared with existing systems. Thus, the outcome of our wavelet transform steganographic method achieves the PSNR of 71.5 dB which ensures the high imperceptibility of the image. Then, the outcome of the OFDM-based proposed steganographic method attains the higher PSNR of 71.07 dB that proves the confidentiality of the message.

In the authors’ previous work, the embedding and extraction process was done based on the cost estimation matrix. To enhance the security throughout the communication system, the novel wavelet-based embedding and extraction process is applied to the OFDM system in this paper. The idea behind this method is to attain a higher imperceptibility and robustness of the image.

This paper presents a state‐of‐the‐art review of the Web privacy and anonymity enhancing security mechanisms, tools, applications and services, with respect to their architecture, operational principles and vulnerabilities. Furthermore, to facilitate a detailed comparative analysis, the appropriate parameters have been selected and grouped in classes of comparison criteria, in the form of an integrated comparison framework. The main concern during the design of this framework was to cover the confronted security threats, applied technological issues and users' demands satisfaction. GNUnet's Anonymity Protocol (GAP), Freedom, Hordes, Crowds, Onion Routing, Platform for Privacy Preferences (P3P), TRUSTe, Lucent Personalized Web Assistant (LPWA), and Anonymizer have been reviewed and compared. The comparative review has clearly highlighted that the pros and cons of each system do not coincide, mainly due to the fact that each one exhibits different design goals and thus adopts dissimilar techniques for protecting privacy and anonymity.