Intrusion detection system

An intrusion detection system (IDS) is a device or software application that monitors network or system activities for malicious activities or policy violations and produces electronic reports to a management station. IDS come in a variety of "flavors" and approach the goal of detecting suspicious traffic in different ways. There are network based (NIDS) and host based (HIDS) intrusion detection systems. NIDS is a network security system focusing on the attacks that come from the inside of the network (authorized users). Some systems may attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system. Intrusion detection and prevention systems (IDPS) are primarily focused on identifying possible incidents, logging information about them, and reporting attempts. In addition, organizations use IDPSes for other purposes, such as identifying problems with security policies, documenting existing threats and deterring individuals from violating security policies. IDPSes have become a necessary addition to the security infrastructure of nearly every organization.

Data

Data (/ˈdeɪtə/ DAY-tə, /ˈdætə/ DA-tə, or /ˈdɑːtə/ DAH-tə) is a set of values of qualitative or quantitative variables; restated, pieces of data are individual pieces of information. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs or images. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing.

Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.

The Latin word "data" is the plural of "datum", and still may be used as a plural noun in this sense. Nowadays, though, "data" is most commonly used in the singular, as a mass noun (like "information", "sand" or "rain").

Data mining

Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets ("big data") involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.

The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.

Intrusive rock

An intrusion is an igneous rock body that forms from crystallized magma under Earth's surface. Magma slowly pushes up from deep within the earth into any cracks or spaces it can find, sometimes pushing existing country rock out of the way, a process that can take millions of years. As the rock slowly cools into a solid, the different parts of the magma crystallize into minerals. Many mountain ranges, such as the Sierra Nevada in California, are formed mostly by intrusive rock, large granite (or related rock) formations.

Intrusions are one of the two ways igneous rock can form; the other is extrusive, that is, a volcanic eruption or similar event. Technically speaking, an intrusion is any formation of intrusive igneous rock; rock formed from magma that cools and solidifies within the crust of the planet. In contrast, an extrusion consists of extrusive rock; rock formed above the surface of the crust.

Intrusions vary widely, from mountain-range-sized batholiths to thin veinlike fracture fillings of aplite or pegmatite. When exposed by erosion, these cores called batholiths may occupy huge areas of Earth's surface. Large bodies of magma that solidify underground before they reach the surface of the crust are called plutons.

Machine learning

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Machine learning is closely related to and often overlaps with computational statistics; a discipline which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR),search engines and computer vision. Machine learning is sometimes conflated with data mining, where the latter sub-field focuses more on exploratory data analysis and is known as unsupervised learning.