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The need for advanced detection methods has become more critical in light of the increasing prevalence of network security incidents. This study proposes a novel approach to network security detection using a fuzzy data mining algorithm, addressing the rising challenges in big data processing and network security. The paper outlines the evolution of big data analytics by exploring the integration of network security detection, data mining, and structural feature analysis. Data for this research was collected using a sniffer device and underwent extensive preprocessing to ensure diversity and applicability. To overcome the limitations of traditional data mining, such as the issue of sharp boundaries, this method combines fuzzy logic with data mining techniques, enhancing conventional network security protocols. Simulation experiments demonstrate the efficacy of this fuzzy mining-based approach, with results showing 987,238 predicted positive cases, 93,951 of which were accurate. The method achieves an impressive 93.65% accuracy and 92.55% recall rate, proving its capability to promptly identify and mitigate suspicious network activities.

This paper embarks on a detailed exploration of biochip technology, starting with its foundational definition and progressing to its application in the realm of network security within commercial environments. Utilizing advanced micron/nanofabrication techniques, we elaborate on the development of biochips tailored for security purposes. The innovative algorithms designed for privacy protection and encrypted communication across computer networks are central to our investigation. For the safeguarding of privacy, we implement homomorphic encryption algorithms renowned for their ability to perform computations on encrypted data without needing to decrypt it. Simultaneously, we enhance encrypted communication through the adoption of refined Elliptic Curve Cryptography (ECC) digital signature algorithms, providing a robust framework against potential cyber threats. To empirically validate the efficacy of biochip technology in this context, we design a prototype encryption chip based on biochip principles, followed by rigorous simulation experiments. Our findings reveal that the biochip-enhanced encryption chip exhibits encryption and decryption speeds of 165.27Mb/s and 128.29Mb/s, respectively. Furthermore, it demonstrates superior resistance to power consumption attacks, with a transient current variance of [0.25mA,0.75mA] across 1000-2000 power samples, underscoring the technology's potential to significantly elevate the data processing speed of encrypted communication in commercial networks. The incorporation of biochip technology thus presents a promising avenue for bolstering privacy protection and secure communication, effectively mitigating the threat landscape in the digital era.

As a major focus of global Internet security, peripheral equipment security is getting more and more attention. Due to the serious asymmetry between the high access level and the low protection level of the public security network and its peripheral equipment, it is very easy to be attacked by hackers. This paper briefly describes the Internet of things access technology, introduces the public security terminal network interconnection access mode and part of the network topology. Based on the preliminary analysis of the security of the software and hardware of the public security terminal network and its edge equipment, this paper analyzes the security of the public security terminal network and its peripheral equipment for the subsequent security design, to ensure the reliability of the public security terminal network and its peripheral equipment, and to strengthen the network security of the public security terminal network.

In many real life network-based applications such as social relation analysis, Web analysis, collaborative network, road network and bioinformatics, the discovery of components with high connectivity is an important problem. In particular, k-edge connected component (k-ECC) has recently been extensively studied to discover disjoint components. Yet many real scenarios present more needs and challenges for overlapping components. In this paper, we propose a k-vertex connected component (k-VCC) model, which is much more cohesive, and thus supports overlapping between components very well. To discover k-VCCs, we propose three frameworks including top-down, bottom-up and hybrid frameworks. The top-down framework is first developed to find the exact k-VCCs by dividing the whole network. To further reduce the high computational cost for input networks of large sizes, a bottom-up framework is then proposed to locally identify the seed subgraphs, and obtain the heuristic k-VCCs by expanding and merging these seed subgraphs. Finally, the hybrid framework takes advantages of the above two frameworks. It exploits the results of bottom-up framework to construct the well-designed mixed graph and then discover the exact k-VCCs by contracting the mixed graph in a top-down way. Because the size of mixed graph is smaller than the original network, the hybrid framework runs much faster than the top-down framework. Comprehensive experimental are conducted on large real and synthetic networks and demonstrate the efficiency and effectiveness of the proposed exact and heuristic approaches.

Graph is a ubiquitous data structure that can model many real-world problems. There are several important problems in graph analysis, such as ranking, community detection and densest subgraph detection. The global versions of these problems have been extensively studied. Recently, the graphs are becoming larger and larger and may contain billions of nodes. The large scale of graphs makes it prohibitive to apply the existing global algorithms. In many circumstances, the entire graph is unavailable, such as the Twitter social network. On the other hand, in many applications, the user is only interested in the local patterns. Thus, the local versions of these problems and algorithms have attracted intense research interests. Ideally, the local algorithms are preferred for solving these problems efficiently without searching the entire graph.In this dissertation, we focus on the local pattern mining problems and related local search algorithms. We study the top-k proximity query problem, the local community detection problem, and the densest connected subgraph problem in dual networks. For the top-k proximity query problem, we devise an efficient and unified local search algorithm, which can be applied for a variety of random walk based proximity measures. For the local community detection problem, we discover that the existing local community detection methods suffer from a serious free rider effect, and develop the query biased node weighting scheme to mitigate the free rider effect. The densest connected subgraph problem is motivated from real applications and is a natural extension of the densest subgraph problem from a single network to dual networks. For each problem, we perform comprehensive experiments to evaluate the effectiveness and efficiency of the proposed method on large real and synthetic networks. The results demonstrate that, for each problem, the proposed method outperforms the state-of-the-art methods in terms of effectiveness and efficiency.

Finding meaningful communities - subnetworks of interest within a large scale network - is a problem with a variety of applications. Most existing work towards community detection focuses on a single network. However, many real-life applications naturally yield what we refer to as Triple Networks. Triple Networks are comprised of two networks, and the network of bipartite connections between their nodes. In this paper, we formulate and investigate the problem of finding Connected-Dense-Connected subgraph (CDC), a subnetwork which has the largest density in the bipartite network and whose sets of end points within each network induce connected subnetworks. These patterns represent communities based on the bipartite association between the networks. To our knowledge, such patterns cannot be detected by existing algorithms for a single network or heterogeneous networks. We show that finding CDC subgraphs is NP-hard and develop novel heuristics to obtain feasible solutions, the fastest of which is O(nlogn+m) with n nodes and m edges. We also study different variations of the CDC subgraphs. We perform experiments on a variety of real and synthetic Triple Networks to evaluate the effectiveness and efficiency of the developed methods. Employing these heuristics, we demonstrate how to identify communities of similar opinions and research interests, and factors influencing communities.

The darknet markets are notorious black markets in cyberspace, which involve selling or brokering drugs, weapons, stolen credit cards, and other illicit goods. To combat illicit transactions in the cyberspace, it is important to analyze the behaviors of participants in darknet markets. Currently, many studies focus on studying the behavior of vendors. However, there is no much work on analyzing buyers. The key challenge is that the buyers are anonymized in darknet markets. For most of the darknet markets, We only observe the first and last digits of a buyer's ID, such as ``a**b''. To tackle this challenge, we propose a hidden buyer identification model, called UNMIX, which can group the transactions from one hidden buyer into one cluster given a transaction sequence from an anonymized ID. UNMIX is able to model the temporal dynamics information as well as the product, comment, and vendor information associated with each transaction. As a result, the transactions with similar patterns in terms of time and content group together as the subsequence from one hidden buyer. Experiments on the data collected from three real-world darknet markets demonstrate the effectiveness of our approach measured by various clustering metrics. Case studies on real transaction sequences explicitly show that our approach can group transactions with similar patterns into the same clusters.

Background This study employs systematic review and meta-analysis to explore the incidence and characteristics of spinal cord injury (SCI) between 2000 and 2021, aiming to provide the most recent and comprehensive data support for the prevention, diagnosis, treatment, and care of SCI. Methods Systematic searches were conducted on epidemiological studies of SCI published between January 1, 2000, and March 29, 2024. Meta-analysis, subgroup analysis, meta-regression, publication bias detection, and literature quality assessment were extensively utilized. Results The pooled results from 229 studies indicated that the overall incidence rate of SCI was 23.77 (95% CI, 21.50–26.15) per million people, with traumatic spinal cord injuries (TSCI) at a rate of 26.48 (95% CI, 24.15–28.93) per million people, and non-traumatic spinal cord injuries (NTSCI) at a rate of 17.93 (95% CI, 13.30-23.26) per million people. The incidence of TSCI exhibited a marked age-related increase and was significantly higher in community settings compared to hospital and database sources. Males experienced TSCI at a rate 3.2 times higher than females. Between 2000 and 2021, the incidence of TSCI remained consistently high, between 20 and 45 per million people, whereas NTSCI incidence has seen a steady rise since 2007, stabilizing at a high rate of 25–35 per million people. Additionally, the incidence of TSCI in developing countries was notably higher than that in developed countries. There were significant differences in the causes of injury, severity, injury segments, gender, and age distribution among the TSCI and NTSCI populations, but the proportion of male patients was much higher than that of female patients. Moreover, study quality, country type, and SCI type contributed to the heterogeneity in the meta-analysis. Conclusions The incidence rates of different types of SCI remain high, and the demographic distribution of SCI patients is changing, indicating a serious disease burden on healthcare systems and affected populations. These findings underscore the necessity of adopting targeted preventive, therapeutic, and rehabilitative measures based on the incidence and characteristics of SCI.

The mining depth of most coal mines in North China has exceeded 1 km. The high seepage water pressure caused by high ground stress leads to the increasingly serious threat of water disaster in the mine. To explore the relationship and mechanism between water inrush from deep mining floor and grouting prevention, single-level grouting and multi-level cooperative grouting methods were carried out in Ordovician limestone confined aquifer of coal seam floor in Xingdong coal mine. Meanwhile, the temporal and spatial characteristics of rock fracture in the floor of deep mining face are revealed through the law of rock fracture microseismic. It is noteworthy that the occurrence time of water inrush and microseismic events have lag characteristics, that is, the occurrence time of microseismic events is earlier than that of water inrush. The multi-level cooperative grouting method can effectively control the non-uniform dissolution Ordovician limestone aquifer in-plane and vertical plane. Furthermore, single-level grouting and multi-level cooperative grouting methods are assumed to be unstable grouting and stable grouting. Besides, the flow pattern transformation characteristics of Ordovician limestone water in unstable to stable grouting are simulated by the finite element method. The results show that the energy inoculation level of fracture expansion around the aquifer decreases after stable grouting reinforcement. In other words, the multi-level cooperative grouting method can effectively strengthen and fill the water inrush channel and reduce the damage of high osmotic pressure to the aquiclude. It is of great significance to reduce the probability of water inrush in deep coal seam and ensure the mining safety of deep coal seam.HighlightsThe Ordovician limestone rock mass damage and dissolution are characterized by layered failure and non-uniform dissolution in horizontal and vertical directions.The occurrence time of water inrush and microseismic events in Xingdong mining area have the lag characteristics.A continuous multi-level collaborative grouting method in karst aquifer is proposed through segmenting, sequencing, and grouting pressure strengthening.Multi-level cooperative grouting method in Ordovician limestone aquifer with high water pressure and uneven dissolution can effectively strengthen and fill the water inrush channel and reduce the probability of water inrush.

Grasses often establish multiple simultaneous symbiotic associations with endophytic fungi and arbuscular mycorrhizal fungi (AMF). Many studies have examined pair-wise interactions between plants and endophytic fungi or between plants and AMF, overlooking the interplays among multiple endosymbionts and their combined impacts on hosts. Here, we examined both the way in which each symbiont affects the other symbionts and the tripartite interactions between leaf endophytic fungi, AMF, and Leymus chinensis. As for AMF, different species (Glomus etunicatum, GE; Glomus mosseae, GM; Glomus claroideum, GC; and Glomus intraradices, GI) and AMF richness (no AMF, single AMF taxa, double AMF mixtures, triple AMF mixtures, and all four together) were considered. Our results showed that significant interactions were observed between endophytes and AMF, with endophytes interacting antagonistically with GM but synergistically with GI. No definitive interactions were observed between the endophytes and GE or GC. Additionally, the concentration of endophytes in the leaf sheath was positively correlated with the concentration of AMF in the roots under low AMF richness. The shoot biomass of L. chinensis was positively related to both endophyte concentration and AMF concentration, with only endophytes contributing to shoot biomass more than AMF. Endophytes and AMF increased shoot growth by contributing to phosphorus uptake. The interactive effects of endophytes and AMF on host growth were affected by the identity of AMF species. The beneficial effect of the endophytes decreased in response to GM but increased in response to GI. However, no influences were observed with other GC and GE. In addition, endophyte presence can alter the response of host plants to AMF richness. When leaf endophytes were absent, shoot biomass increased with higher AMF richness, only the influence of AMF species identity outweighed that of AMF richness. However, when leaf endophytes were present, no significant association was observed between AMF richness and shoot biomass. AMF species identity rather than AMF richness promoted shoot growth. The results of this study demonstrate that the outcomes of interspecific symbiotic interactions are very complex and vary with partner identity such that the effects of simultaneous symbioses cannot be generalized and highlight the need for studies to evaluate fitness response of all three species, as the interactive effects may not be the same for each partner.