Posted: February 19th, 2022

Predictive analytics denotes an approach used to forecast a cyberattack’s probability

Cybersecurity
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Cybersecurity
Predictive analytics denotes an approach used to forecast a cyberattack’s probability, thus allowing an organization to reinforce its defenses against looming attempts even before they surface. Like a radar that illustrates the approaching enemy, these analytics helps a company know where cybercriminals are likely to attack next, pinpoint where weak points are situated, and establish how well the organization is prepared to counter an attack before it is too late.
Behavior analytics refers to the use of software tools to identify data transmission patterns in an unusual network. The theory behind behavior analytics is that the analytics tool would identify the irregularity and alert managers of IT, who would bring the cyberattack or odd behavior to a stop (Carrascosa, Kalutarage & Huang, 2017). Organizations utilize behavior analytics to identify intrusions that escape preventive technologies like antivirus software, firewalls, and intrusion-prevention systems. Those traditional tools match signatures or fingerprints in previous attacks, whereas tools of behavior analytics study and report abnormalities that are judged against a baseline of normal behavior.
Some organizations are discovering ways to obtain insights into the stock-keeping unit optimization by applying analytic tools to their data-rich records. Algorithms pertaining to analytics tools provide a powerful way for organizations to recognize and quantify intuitive and non-intuitive product associations in their stock data. In turn, these insights make it possible to make better decisions regarding the products they carry and their optimization of inventory (Strickland, 2015). Also, algorithms can be utilized to find and measure how possible it is that a particular item set will be found in a basket containing some other set of items. The outputs of these algorithms, in turn, Help in the stock-keeping unit optimization.

References
Carrascosa, I. P., Kalutarage, H. K., & Huang, Y. (2017). Data analytics and decision support for cybersecurity: Trends, methodologies and applications. Springer.
Strickland, J. (2015). Data science and analytics for ordinary people. Lulu.com.

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