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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.

Anomaly detection of network attacks has become a high priority because of the need to guarantee security, privacy and reliability. This work aims to describe both intelligent immunological approaches and traditional monitoring systems for anomaly detection.

Author investigated different artificial immune system (AIS) theories and proposes how to combine different ideas to solve problems of network security domain. An anomaly detection system that applies those ideas was built and tested in a real time environment, to test the pros and cons of AIS and clarify its applicability. Rather than building a detailed signature based model of intrusion detection system, the scope of this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback.

The natural immune system has its own intelligent mechanisms to detect the foreign bodies and fight them and without it, an individual cannot live, even just for several days. Network attackers evolved new types of attacks. Attacks became more complex, severe and hard to detect. This results in increasing needs for network defense systems, especially those with ability to extraordinary approaches or to face the dynamic nature of continuously changing network threats. KDD CUP'99 dataset are used as a training data to evaluate the proposed hybrid artificial immune principles anomaly detection. The average cost of the proposed model was 0.1195 where that the wining of KDD99 dataset computation had 0.233.

It is original to introduce investigation on the vaccination biological process. A special module was built to perform this process and check its usage and how it could be formulated in artificial life.

The purpose of this paper is to propose a web intrusion detection system (IDS), SensorWebIDS, which applies data mining, anomaly and misuse intrusion detection on web environment.

SensorWebIDS has three main components: the network sensor for extracting parameters from real‐time network traffic, the log digger for extracting parameters from web log files and the audit engine for analyzing all web request parameters for intrusion detection. To combat web intrusions like buffer‐over‐flow attack, SensorWebIDS utilizes an algorithm based on standard deviation (δ) theory's empirical rule of 99.7 percent of data lying within 3δ of the mean, to calculate the possible maximum value length of input parameters. Association rule mining technique is employed for mining frequent parameter list and their sequential order to identify intrusions.

Experiments show that proposed system has higher detection rate for web intrusions than SNORT and mod security for such classes of web intrusions like cross‐site scripting, SQL‐Injection, session hijacking, cookie poison, denial of service, buffer overflow, and probes attacks.

Future work may extend the system to detect intrusions implanted with hacking tools and not through straight HTTP requests or intrusions embedded in non‐basic resources like multimedia files and others, track illegal web users with their prior web‐access sequences, implement minimum and maximum values for integer data, and automate the process of pre‐processing training data so that it is clean and free of intrusion for accurate detection results.

Web service security, as a branch of network security, is becoming more important as more business and social activities are moved online to the web.

Existing network IDSs are not directly applicable to web intrusion detection, because these IDSs are mostly sitting on the lower (network/transport) level of network model while web services are running on the higher (application) level. Proposed SensorWebIDS detects XSS and SQL‐Injection attacks through signatures, while other types of attacks are detected using association rule mining and statistics to compute frequent parameter list order and their maximum value lengths.

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.

With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.

With the rapid growth of e‐commerce, governmental and corporate agencies are taking extra precautions when it comes to protecting information. The development of e‐security as a discipline has enabled organisations to discover a wider array of similarities between attacks occurring across their security environment and develop appropriate countermeasures. To further improve the security of information, there is a need for conceptualising the interrelationships between e‐security and the major elements involved in changing a company's infrastructure. Organisations should act in an ethical manner, especially when it comes to e‐security and e‐privacy policies, procedures, and practices. The consequential theory of utilitarianism is used and applied to a conceptual model to help explain how organisations may develop better secured information in an information‐sharing and globally networked environment.

Security is one of the major challenges in the design and implementation of protocols for mobile ad hoc networks (MANETs). In such systems, the cooperation between nodes is one of the important principles being followed in the current research works to formulate various security protocols. Many existing works assume that mobile nodes will follow prescribed protocols without deviation. However, this is not always the case, because these networks are subjected to a variety of malicious attacks. Since there are various models of attack, trust routing scheme can guarantee security and trust of the network. The purpose of this paper is to propose a novel trusted routing model for mitigating attacks in MANETs.

The proposed model incorporates the concept of trust into the MANETs and applies grey relational analysis theory combined with fuzzy sets to calculate a node’s trust level based on observations from neighbour nodes’ trust level, these trust levels are then used in the routing decision-making process.

In order to prove the applicability of the proposed solution, extensive experiments were conducted to evaluate the efficiency of the proposed model, aiming at improving the network interaction quality, malicious node mitigation and enhancements of the system’s security.

The proposed solution in this paper is a new approach combining the fundamental basics of fuzzy sets with the grey theory, where establishment of trust relationships among participating nodes is critical in order to enable collaborative optimisation of system metrics. Experimental results indicate that the proposed method is useful for reducing the effects of malicious nodes and for the enhancements of system’s security.

Malicious attacks extremely traumatize knowledge networks due to increasing interdependence among knowledge elements. Therefore, exposing the damage of malicious attacks to knowledge networks has important theoretical and practical significance. Despite the insights being offered by the growing research stream, few studies discuss the diverse responses of knowledge networks’ robustness to different target-attacks, and the authors lack sufficient knowledge of which forms of malicious attacks constitute greater disaster when knowledge networks evolve to different stages. Given the irreversible consequences of malicious attacks on knowledge networks, this paper aims to examine the impacts of different malicious attacks on the robustness of knowledge networks.

On the basic of dividing malicious attacks into six forms, the authors incorporate two important aspects of robustness of knowledge networks – structure and function – in a research framework, and use maximal connected sub-graphs and network efficiency, respectively, to measure structural and functional robustness. Furthermore, the authors conceptualize knowledge as a multi-dimensional structure to reflect the heterogeneous nature of knowledge elements, and design the fundamental rules of simulation. NetLogo is used to simulate the features of knowledge networks and their changes of robustness as they face different malicious attacks.

First, knowledge networks gradually form more associative integrated structures with evolutionary progress. Second, various properties of knowledge elements play diverse roles in mitigating damage from malicious attacks. Recalculated-degree-based attacks cause greater damage than degree-based attacks, and structure of knowledge networks has higher resilience against ability than function. Third, structural robustness is mainly affected by the potential combinatorial value of high-degree knowledge elements, and the combinatorial potential of high-out-degree knowledge elements. Forth, the number of high in-degree knowledge elements with heterogeneous contents, and the inverted U-sharp effect contributed by high out-degree knowledge elements are the main influencers of functional robustness.

The authors use the frontier method to expose the detriments of malicious attacks both to structural and functional robustness in each evolutionary stage, and the authors reveal the relationship and effects of knowledge-based connections and knowledge combinatorial opportunities that contribute to maintaining them. Furthermore, the authors identify latent critical factors that may improve the structural and functional robustness of knowledge networks.

First, from the dynamic evolutionary perspective, the authors systematically examine structural and functional robustness to reveal the roles of the properties of knowledge element, and knowledge associations to maintain the robustness of knowledge networks. Second, the authors compare the damage of six forms of malicious attacks to identify the reasons for increased robustness vulnerability. Third, the authors construct the stock, power, expertise knowledge structure to overcome the difficulty of knowledge conceptualization. The results respond to multiple calls from different studies and extend the literature in multiple research domains.

Nowadays, more interest is found among the researchers in MANETs in practical and theoretical areas and their performance under various environments. WSNs have begun to combine with the IoT via the sensing capability of Internet-connected devices and the Internet access ability of sensor nodes. It is essential to shelter the network from attacks over the Internet by keeping the secure router.

This paper plans to frame an effective literature review on diverse intrusion detection and prevention systems in Wireless Sensor Networks (WSNs) and Mobile Ad hoc NETworks (MANETs) highly suitable for security in Internet of Things (IoT) applications. The literature review is focused on various types of attacks concentrated in each contribution and the adoption of prevention and mitigation models are observed. In addition, the types of the dataset used, types of attacks concentrated, types of tools used for implementation, and performance measures analyzed in each contribution are analyzed. Finally, an attempt is made to conclude the review with several future research directions in designing and implementing IDS for MANETs that preserve the security aspects of IoT.

It observed the different attack types focused on every contribution and the adoption of prevention and mitigation models. Additionally, the used dataset types, the focused attack types, the tool types used for implementation, and the performance measures were investigated in every contribution.

This paper presents a literature review on diverse contributions of attack detection and prevention, and the stand of different machine learning and deep learning models along with the analysis of types of the dataset used, attacks concentrated, tools used for implementation and performance measures on the network security for IoT applications.

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.