Data mining is a set of techniques for removing randomness from large and complicated databases and uncovering hidden patterns. The extraction of new knowledge from large databases is known as datamining (DM), sometimes known as knowledge discovery from databases (KDD). Data mining is the process of discovering previously undiscovered, valid patterns and relationships in big data sets using advanced data analysis techniques. Data mining techniques can estimate future trends and actions to help individuals make better decisions. Datamining has a range of applications. Identifying trends and patterns is a powerful tool for businesses across all sectors and industries.
Modern intrusion detection systems must deal with a number of difficulties. These applications must be dependable, expandable, controllable, and cost-effective to maintain. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalisation to novel types of intrusion, and consistent behaviour in a changing environment. In order to find the optimum neural network, the number of hidden layers in various neural network topologies is compared. Misuse detection is a method of attempting to detect instances of network attacks by comparing current behaviour to the expected activities of an intruder. Artificial neural networks can detect and classify network activity even when the input is sparse, imperfect, and nonlinear.
The major goal of this research is to investigate privacy and security concerns among cloud computing users and consumers in a dispersed setting. Machine learning, natural language processing (NLP), and data mining techniques are used in conjunction to automatically detect and uncover patterns in a variety of sources. Both continuous and discontinuous changes can be dealt with using predictive analytics. Predictive analytics uses classification, prediction, and, to some extent, affinity analysis as analytical tools.
The semantic context and syntactic components are the focus of current text or document mining research. We investigated a mining model to categorise documents based on the Order of Context, Concept, and Semantic Relations in order to accomplish this, and with the inspiration garnered from our previous research efforts (OCCSR). Users will be able to get valuable information from virtually connected data warehouses using data mining techniques based on Cloud computing, cutting infrastructure and storage expenses. From the cloud, data mining can extract useful and potentially helpful information. The 3Vs are three features that are commonly used to define big data (Volume, Velocity and Variety). The report examines Big Data analytics methodologies, settings, and technologies in critical domains, as well as how they contribute in the creation of analytics solutions for Clouds.
Clustering is a type of unsupervised learning approach that is used to find a new set of categories. The processing time for grid-based clustering is typically determined by the size of the grid rather than the data. Three clustering algorithms are compared: hierarchical clustering, density-based clustering, and K Means clustering.
The majority of current approaches to identifying misuse rely on rule-based expert systems to identify indicators of previously detected attacks. We give a quick review of the numerous Artificial Intelligence techniques used in the design, development, and deployment of Intrusion Detection Systems (IDS) for defending computer and communication networks from intruders, as well as their improvements. Knowledge Discovery in Data (KDD) aims to extract information that isn't immediately apparent through meticulous and detailed analysis and interpretation. Analytics uses KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation tools to drive choices and actions.Author(s) Details
E. Kesavulu Reddy
Department of Computer Science, S. V. University College of CM & CS, Tirupati, Andhra Pradesh-517502, India.
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