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Comprehensive Exploration of the Role of Graph Databases like Neo4j in Cyber Security
... Graph neural networks (GNNs) [31] [35] implemented in PyTorch [20][1] are currently the prime ML methodology applied on graphs [28] [42]. Neo4j [2] is a graph database which has been applied to problems such as processing PubMed documents [13], cyber security analysis [4], and data science [5]. GSP is a cross-disciplinary field [43] encompassing topics such as graph spectral wavelets [33], graph Fourier transform [41], graph Kalman filter [6], gradient graph Laplacian [7], and variational Bayesian estimation [8]. ...
Credit card transactions, especially when linked to smart devices and the IoT ecosystem in general, are one of the drivers of contemporary digital economy as well as a major indicator of the overall financial activity. As such as well as for a plethora of other reasons it is imperative that fraudulent transactions be efficiently and reliably discovered. Because of their interconnected and time-dependent nature, a graphic representation not only is convenient, but also lends itself to machine learning strategies. To this end one viable approach is to construct a framework consisting of three steps. First, at each vertex a vector containing first and higher order attributes is embedded, then vertices are clustered, and finally vertex classification is done. As a concrete example three graph partitioning algorithms were selected, namely kNN, DBSCAN, and spectral clustering, whereas vertex clustering has been performed through logistic regression. The experimental results corroborate the efficiency of the abovementioned framework and are encouraging for the development of more higher order fraudulent transaction methods towards a more robust and highly reliable digital economy.
With the evolvement of the Internet over the last few years, the need for security has been rising with it mainly due to the openness and connectivity nature of the web, people and organizations are faced with more challenges every day to secure their data and all other assets of value to them. No system is totally secure. Any security procedures should be undertaken with that in mind. There will always be threats and actual intrusions. The ultimate goal should be minimizing the risk and not eliminating it. This paper describes a system for detecting intrusions, introducing technologies to provide protection for electronic information exchange over public networks.
With the rapid development of the Internet, network security situation awareness has attracted tremendous attention. In large-scale complex networks, network security situation awareness data presents the characteristics of large-scale, multi-source, and heterogeneous. Recently, much research work have been done on network security situation awareness. However, most of the existing methods store different types of data in different ways, which makes data query and analysis inefficient. To solve this problem, we propose a graph database-based hierarchical multi-domain network security situation awareness data storage method. In our scheme, we build a hierarchical multi-domain network security situation awareness model to divide the network into different domains, which can collect and dispose the awareness data more efficiently. Meanwhile, to unify our storage mode, we also define network security situation awareness data storage rules and methods based on graph database. Finally, extensive experiments on real datasets show that our proposed method is efficient compared to state-of-the-art storage models.
The limitations of traditional databases, in particular the relational model, to cover the requirements of current applications has lead the development of new database technologies. Among them, the Graph Databases are calling the attention of the database community because in trendy projects where a database is needed, the extraction of worthy information relies on processing the graph-like structure of the data. In this paper we present a systematic comparison of current graph database models. Our review includes general features (for data storing and querying), data modeling features (i.e., data structures, query languages, and integrity constraints), and the support for essential graph queries.
The aim of this study is to simulate a network traffic ana-lyzer that is part of an Intrusion Detection System -IDS, the main focus of research is data mining and for this type of application the steps that precede the data mining : data preparation (possibly involving clean-ing data, data transformations, selecting subsets of records, data nor-malization) are considered fundamental for a good performance of the classifiers during the data mining stage. In this context, this paper dis-cusses and presents as a contribution not only the classifiers that were used in the problem of intrusion detection, but also the initial stage of data preparation. Therefore, we tested the performance of three clas-sifiers on the KDDCUP'99 benchmark intrusion detection dataset and selected the best classifiers. We initially tested a Decision Tree and a Neural Network using this dataset, suggesting improvements by reduc-ing the number of attributes from 42 to 27 considering only two classes of detection, normal and intrusion. Finally, we tested the Decision Tree and Bayesian Network classifiers considering five classes of attack: Nor-mal, DOS, U2R, R2L and Probing. The experimental results proved that the algorithms used achieved high detection rates (DR) and significant reduction of false positives (FP) for different types of network intrusions using limited computational resources.
In the current world that is run by technology and network connections, it is crucial to know what cyber security is and to be able to use it effectively. Systems, important files, data, and other important virtual things are at risk if there is no security to protect it. Whether it is an IT firm not, every company has to be protected equally. With the development of the fresh technology in cyber security, the attackers similarly do not collapse behind. They are consuming better and enhanced hacking techniques and aim the weak points of many businesses out there. Cyber security is essential because military, government, financial, medical and corporate organizations accumulate, practise, and stock unprecedented quantities of data on PCs and other devices. An important quota of that data can be sensitive information, whether that be financial data, intellectual property, personal information, or other various kinds of data for which illegal access or acquaintance could ensure negative concerns.
Databases are an integral part of a computing system and users heavily rely on the services they provide. When interact with a computing system, we expect that data be stored for future use, that the data is able to be looked up fastly, and we can perform complex queries against the data stored in the database. Many different emerging database types available for use such as relational databases, object databases, keyvalue databases, graph databases, and RDF databases. Each type of database provides unique qualities that have applications in certain domains. Our work aims to investigate and compare the performance and scalability of relational databases to graph databases in terms of handling multilevel queries such as finding the impact of a particular subject with the working area of pass out students. MySQL was chosen as the relational database, Neo4j as the graph database.
In this workshop we provide a hands-on introduction to the popular open source graph database Neo4j [1] through fixing a series of increasingly sophisticated, but broken, test cases each of which highlights an important graph modeling or API affordance.
Relational databases have been around for many decades and are the database technology of choice for most traditional data-intensive storage and retrieval applications. Retrievals are usually accomplished using SQL, a declarative query language. Relational database systems are generally efficient unless the data contains many relationships requiring joins of large tables. Recently there has been much interest in data stores that do not use SQL exclusively, the so-called NoSQL movement. Examples are Google's BigTable and Facebook's Cassandra. This paper reports on a comparison of one such NoSQL graph database called Neo4j with a common relational database system, MySQL, for use as the underlying technology in the development of a software system to record and query data provenance information.
In this paper, three case studies of neural networks in
engineering applications are presented. First a functional link
artificial neural network is constructed for closed shape modelling to
extract invariant features to recognise the pattern. The second case
deals with a single layer network for the parameter estimation of a
transfer function with the frequency response data available. The method
is applied to velocity sensor modelling as an application. Finally, a
thinning algorithm is realised by a neural network approach
A New Representation of WordNet® using Graph Databases
- K Nagi
Nagi, K. (2013). A New Representation of WordNet® using Graph
Databases. The Fifth International Conference on Advances in
Databases, Knowledge, and Data Applications, (pp. 1-8). Seville,
Spain.
Comparative Analysis of Relational and Graph Databases
- C T Shalini
- Batra
Shalini Batra, C. T. (2012). Comparative Analysis of Relational and
Graph Databases. International Journal of Soft Computing and
Engineering (IJSCE), Vol. 2(Issue 2), 509-512.
GRAPH DATA SCIENCE USE CASES: FRAUD AND DETECTION
- J Chung
Literature review about Ne04j graph database
- Felix Melchor
- Santos Lopez
Research Paper OnComplexity Reduction In Relational Database Using Neo4j Graph Database
- T P Deshmukh
T.P.Deshmukh, B. (2020). Research Paper OnComplexity Reduction
In Relational Database Using Neo4j Graph Database. International
Journal of Advances in Engineering and Management (IJAEM), Vol.
2(Issue 11), 140-159.
Neo4j as an Embedded Database: The Key Use Cases of Graph Databases
- D Packer
Packer, D. (2022). Neo4j as an Embedded Database: The Key Use
Cases of Graph Databases. https://neo4j.com/blog/.
Design And Deploy Secure Azure Environment
- P Udaykumar
Udaykumar, P. (2023). Design And Deploy Secure Azure
Environment. Apress.
Graph Database Applications and Concepts with Neo4j
- J Miller
Miller, J. (2013). Graph Database Applications and Concepts with
Neo4j.
A Knowledge-Based Intrusion Detection Engine to detect attacks
- . P Mrs
- M S Bhalekar
Mrs. P. Bhalekar, M. S. (2014). A Knowledge-Based Intrusion
Detection Engine to detect attacks. The International Journal Of
Engineering And Science (IJES), Vol. 3(Issue 3), 30-36.
Engineering applications of neural networks
- S Yue