Multiple Network Types Research Articles

Research on the application of improved V-detector algorithm in network intrusion detection

Abstract Network intrusion detection has been widely discussed and studied as an important part of protecting network security. Therefore, this paper presents an in-depth study of the application of an improved V-detector algorithm in network intrusion detection. In this paper, we construct a V-detector intrusion detection model, adopt the “self-oriented” identification principle, and randomly generate detectors with large differences from the health library. A smaller number of detectors are used to compare the data information generated by the computer, and if they are similar, they are judged as intrusions. Intrusion detection experiments are performed on multiple types of networks by using classifiers to determine whether the access to be detected is an attack access. The experimental results show that the model has the lowest false alarm rate for mixed feature networks, with a false alarm rate of only 13% and a detection rate of 89%, with a sample size of 25,987. After the improvement of the V-detector intrusion detection model, the error correction output problem leads to a network intrusion with a miss rate of only 11% and a protection rate of 85%. The experimental data proved that the model has the advantages of large data size and comprehensive intrusion attack types.

Automating the design and development of gradient descent trained expert system networks

Prior work introduced a gradient descent trained expert system that conceptually combines the learning capabilities of neural networks with the understandability and defensible logic of an expert system. This system was shown to be able to learn patterns from data and to perform decision-making at levels rivaling those reported by neural network systems. The principal limitation of the approach, though, was the necessity for the manual development of a rule-fact network (which is then trained using backpropagation). This paper proposes a technique for overcoming this significant limitation, as compared to neural networks. Specifically, this paper proposes the use of larger and denser-than-application need rule-fact networks which are trained, pruned, manually reviewed and then re-trained for use. Multiple types of networks are evaluated under multiple operating conditions and these results are presented and assessed. Based on these individual experimental condition assessments, the proposed technique is evaluated. The data presented shows that error rates as low as 3.9% (mean, 1.2% median) can be obtained, demonstrating the efficacy of this technique for many applications.

Financial Reporting Quality and Auditor Dismissal Decisions at Companies with Common Directors and Auditors*

ABSTRACTWe examine the effects of corporate networks involving common directors and auditors (i.e., connections creating single or double ties between companies) on two important monitoring roles: financial reporting quality and auditor dismissal decisions. We also investigate how shocks to the networks, in the form of the audit failing to detect misstatements, affect these networks' structure. The investigations are important because these networks can have significant effects on firm governance and may have different effects when they overlap. We have three primary findings about double‐tie networks: (i) there is no evidence that they improve overall financial reporting quality beyond the effect of single‐tie networks; (ii) they lower directors' willingness to dismiss the auditor, even when there is a signal of an audit failure within the network; and (iii) they allow audit‐quality problems to spread between companies. Our results demonstrate the importance of investigating multiple types of networks and how shocks travel through them. Our findings also lend credence to concerns that “cozy” relationships between directors and auditors diminish the link between poor audit quality and market‐imposed reputation penalties—specifically, auditor dismissals.

Integrated analysis of competing endogenous RNA (ceRNA) networks in subacute stage of spinal cord injury

This study aims to investigate the genetic and epigenetic mechanisms involved in the pathogenesis of subacute stage of spinal cord injury (SCI). Gene-expression datasets associated with SCI were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analyses were performed in order to identify differentially expressed genes (DEGs). Multiple network types were constructed and analyzed, including protein–protein-interaction (PPI) network, miRNA-target network, lncRNA-associated competing endogenous RNA (ceRNA) network, and miRNA-transcription factor (TF)-target network. Cluster analyses were performed to identify significant modules. To verify the prediction accuracy of the in-silico identified molecules, qRT-PCR experiments were conducted. The results depicted the Ywhae gene as the hub gene with the highest degree in the PPI network. The ceRNA network identified specific genes (Flna, ID3, and HK2), miRNAs (miR-16-5p, miR-1958, and miR-185-5p), and lncRNAs (Neat1, Xist, and Malat1) as playing critical regulating roles in the pathological mechanisms of SCI. The miRNA-TF-gene interaction network identified four important TFs (Sp1, Trp53, Jun, and Rela). The miRNA-gene-TF interaction loops from the significant modules indicated that miR-325-3p can interact with the Asah1 gene and TF-Sp1 by forming a closed loop. The qRT-PCR experiments verified four selected genes (Flna, ID3, HK2, and Ywhae) and two selected TFs (Jun, and Sp1) as significantly up-regulated following SCI. The results indicated that four genes (Flna, ID3, HK2, and Ywhae), four transcription factors (Sp1, Trp53, Jun, and RelA), two miRNAs (miR-16-5p and miR-325-3p), and three lncRNAs (Neat1, Xist, and Malat1) are likely to be involved in the molecular mechanisms underlying the subacute stage of SCI. These findings uncover putative pathogenic mechanisms involved in SCI and might bear translation significance for future research towards therapeutic development.

Not getting high with a little help from your friends: Social versus drug network correlates of marijuana use among YMSM

Substantial evidence has documented the importance of social connections in shaping health and drug use behaviors among adolescents and young adults. The current study extends previous research into the associations between network characteristics and drug use behavior among young men who have sex with men (YMSM) by 1) examining multiple network characteristics, 2) simultaneously assessing multiple network types (i.e., social and drug use), and 3) examining change in network characteristics and drug use behavior over time. Data for the current study comes from RADAR, a longitudinal cohort study of YMSM. Latent growth curve models examined the change in frequency of marijuana use across four observations and individual and network correlates of this change including: demographics, drug network size, drug network density, social network size, and social network density. Baseline frequency of marijuana use was positively associated with drug network size and density, while it was inversely related to social network size and density. In addition, increasing frequency of marijuana use was associated with increases in drug network size and density, while it was associated with decreases in social network size. These findings highlight the complexity of multiple network types (e.g., drug and social) and network structures (e.g., size and density) in understanding drug use behavior among YMSM. Furthermore, as changes in drug and social networks may be indicative of changes in marijuana use, peer relationships may be especially important in understanding an individual's trajectories of marijuana use.

The structure of the Mississippian world: A social network approach to the organization of sociopolitical interactions

The structure and organization of macroregional networks have long captured the attention of archaeologists working in eastern North America, especially in regard to the overall character and spread of Mississippian culture across the midwestern and southeastern United States. In this paper, we use distributions of marine shell gorgets to evaluate the organization of relationships across the Mississippian world and to understand how social capital was accumulated and reciprocal relationships were established in the context of emerging organizational complexity. We use data on 1980 shell gorgets from 165 sites across eastern North America to investigate the social networks related to the distribution of these materials and, indirectly, to the types of social capital that were produced through regional social relationships. We employ a framework that articulates social network topologies with different forms of social capital to explicate the relational structure of Mississippian sociopolitics and to identify the sociospatial scales at which different forms of social capital, reciprocity, and relationships underwrote these structures. We conclude that resources (both material and immaterial) were likely drawn from community, local, regional, macroregional, and continental-scale networks while multiple types of networks were maintained across these multiscalar relational fields.

The good, the bad and the implications of profiling mobile broadband coverage

Pervasive coverage and continuous connectivity of Mobile Broadband (MBB) networks are common goals for regulators and operators. Given the increasing heterogeneity of technologies in the last mile of MBB networks, further support for seamless connectivity across multiple network types relies on understanding the prevalent network coverage profiles that capture different available technologies in an area. Correlating these coverage profiles with network performance metrics is of great importance in order to forestall disturbances for applications running on top of MBB networks. In this paper, we aim to profile MBB coverage and its performance implications from the end-user’s perspective along critical transport infrastructures (i.e., railways in Norway). For this, we deploy custom measurement nodes on-board five Norwegian inter-city trains and we collect a unique geo-tagged dataset along the train routes. We then build a coverage mosaic, where we divide the routes into segments and analyze the coverage of individual operators in each segment. We propose and evaluate the use of hierarchical clustering to describe prevalent coverage profiles of MBB networks along the train routes and classify each segment accordingly. We further analyze the areas we classify with each profile and assess the packet-loss and HTTP download performance of the networks in those areas.

The Use of Mobile Devices To Improve Alarm Systems

The Joint Commission 2007 Annual Report stated, “Inadequate communication between care providers, or between care providers and patients/families, is consistently the main root cause of sentinel events.”1 Critical components of communication regarding the health status of a patient are the alerts and notifications originating from the various patient monitors; yet according to the American Journal of Emergency Medicine, as many as 99.4% of alarms are false and less than 1% result in change to patient management.2 Looking to 2011, alarm hazards remain an ongoing challenge, listed at number two on the ECRI Institute’s Top 10 Health Technology Hazards for this year.3 This paper addresses how the use of mobile devices such as the iPhone, the Android smart phone, and the Blackberry with intuitive interfaces and messaging coupled with an integrated alarm management platform will improve clinical satisfaction and operational workflow and decrease risk at the point of care.

Evolución social y networking en la comunidad biblio-documental

Our society is increasingly interconnected through multiple types of networks, and humankind is changing their habits accordingly, adapting to the use of new technologies. We explore the psycho-social characteristics that determine people’s behavior through the communication channels. We discuss some structures of networking in the field of Library and Information Science: mailing lists, directories, etc. We conclude that the work of the information professional fits perfectly in the current wave 2.0 of information sharing.

Microbial regulatory and metabolic networks

Reconstruction of transcriptional regulatory and metabolic networks is the foundation of large-scale microbial systems and synthetic biology. An enormous amount of information including the annotated genomic sequences and the genomic locations of DNA-binding regulatory proteins can be used to define metabolic and regulatory networks in cells. In particular, advances in experimental methods to map regulatory networks in microbial cells have allowed reliable data-driven reconstruction of these networks. Recent work on metabolic engineering and experimental evolution of microbes highlights the key role of global regulatory networks in controlling specific metabolic processes and the need to consider the integrated function of multiple types of networks for both scientific and engineering purposes.

Using MPLS to unify multiple network types

There is an increasing push to unify data-, voice-, and other multimedia-based network operations on single IP networks. This approach saves network operators money and simplifies administrative tasks. However, unifying network functions on IP can be complex, particularly for communications that must function across various networking technologies. Users are thus turning to a technology: multiprotocol label switching, that promises to make the task easier and less expensive.