Abstract
Analyzing attacks on computer networks is complex given the volume of data considered and the large number of machines, even in small networks. The volume of data is large and the time to process and analyze it is short. The goal is to extract and analyze information about network attacks that has been obtained from open sources. Using a robust, elastic and scalable architecture that makes use of processing techniques with the use of Hadoop so that the information is available in a timely manner. With the proposed architecture implemented all the desired characteristics were obtained allowing the processing of the data in near real time. The system provides intelligence information about large-scale attacks with agility and efficiency.
Highlights
The mastery of information is a fundamental point for the evolution of humanity since its inception
Between the months of August and December 2017, about 14 million records of attacks were internalized. Even with this large number of entries, the queries in the tool did not vary in their response time, displaying the result almost immediately after the filters were selected
This behavior is related to the fact that the indexing of the data is carried out at the moment of their insertion
Summary
The mastery of information is a fundamental point for the evolution of humanity since its inception. Being able to analyze data on threats to systems is essential for structuring protection and recovery plans. Big Data applies information that cannot be processed or analyzed using traditional processes or tools, with the focus of the systems on obtaining the greatest amount of data on transactions and their users. According to [7], three characteristics are inherent to Big Data: volume, variety and velocity. The volume refers to what has already been addressed about the large amount of data that are and will be generated in the future, making centralized systems unable to process the entire data set. The variety refers to the diversity of sources from which the data originates, low-level access records, location information, accelerometers, writing patterns, among others, form the origin of the information. The speed at which this data cannot be processed in the background, in an increasingly dynamic environment a few seconds can mean the loss of brand value and a financial loss
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