BIG DATA – Machine learning & artificial intellegence

Introduction

Data Science is a field that deals with everything relating to data which includes data cleaning, preparation, sorting, mining and analysis. It is a broad term for every scientific method, processes which includes mathematics, statistics.

Data science attempts to manipulate with huge chucks of data by finding patterns and relationship using existing technology, mathematics and statistical techniques.

Data science is helping the enterprise cyber security to succeed in all aspect.

Big data originated from data science and is a large volumes of data (both structured and unstructured) of various type that cannot be processed effectively by using the traditional processing method and software.

Big data technology, when properly analyze will provide foundation for business to control cyber-attacks.

Big data can be analyst in 4 ways:  Predictive, Prescriptive Diagnostic & Descriptive.

Many of this analytics models, methods and tools are of great values to cyber security experts. Big data analytics are also found in establishment such as banking, insurance sector, social media, educations, agriculture and so on.

Big data is been characterized as: Volume, Variety, Volume, Veracity, and Complexity

The word artificial intelligence is composed of two individual words ‘artificial’, and ‘intelligence’, artificial means something made by human or non-natural thing while intelligence means ability to understand, precept or think. Therefore Artificial intelligence is a branch in computer science that deals with creation of intelligent machines and tools that work and reacts like human.

Machine Learning is application of artificial intelligence based on the idea that systems can learn from data, identify patterns and relations between old data and make decisions based on the relationship with minimal or no human intervention. It is the learning which involves machine learning by its own without being explicitly programmed. It is a method of data analysis that automates analytical model building. Basically, Machine learning is categorized into supervised and unsupervised learning.

history of Big data & Big data today 

The first remarkable data project was created in 1937 in USA during the Franklin D. Roosevelt’s administration. The concept of Big data became common during 1960s and 70s,when the systems of data was just getting started, and first data centers relational database was developed. The first data processing machine was invented in 1943 and was developed specifically for World War II, it is named colossus and it is used to search for patterns in intercepted messages at a rate of 5000 character per seconds.

In recent years, great research have has reviled complicated problems involving data in which multi-target prediction are required. Such problems arises diverse application domains which includes document categorization, recommended systems, images processing , signal processing, videos and music information retrieval ,natural languages processing, fraud detection, Marketing, health care and so on .

A new method of security analysis has emerged in past few years, which is used to collect, store and process high volume of security data in a real time.

Today, complex structured and unstructured data is becoming available from different sources, organizations are making attempts to utilize these resources specifically to enhance innovation, make decision, improve on security and make more profit.

Few of the sectors in which big data has been applied are:

  • Government
  • Social Media Analytics
  • Technology
  • Business
  • Fraud detection
  • Cyber-security
  • Banking
  • Call Centre
  • Marketing
  • Smart phones

 

Big Data: A Solution to Enterprise Security 

A Big Data solution will show promising value when applied to cyber security, by improving on risk management and other intelligence aspect of the organization which can be achieved with provision of tool and good automation to analyses big data problems. Establishing patterns between historical data will help to identity what is normal and what is not normal, most importantly, giving a quick response when security architecture deviate from what is not normal.

In recent time, solutions to big data problem have allowed organizations to observe anomalous behavior in security even in a real-time by using different sources of database. Big data tools, software and method has been designed to handle complex and massive IP Network data effectively which can be used to provide adequate cyber security to organizations.

A variety of data analysis can be performed in order to expose security visions by using huge data sets both structured and unstructured.

Mining available information and data in real world can be used to reduce security vulnerability; the following are the sources of big data for cyber security:

  • Facebook
  • Twitter
  • Google
  • Cloud
  • YouTube
  • Web content
  • Logs From Application
  • IOT and so on

Devising a critical strategy to access information and data from any of the aforementioned sources will provide a high standard of security to organization.

Organizations are collecting big data and building analysis to make decision based on the solution provided to the big data problem in order improve security.

 

Big Data with Machine Learning and Artificial Intelligence

Artificial intelligence has the ability to handle big data in its varying shapes, sizes and forms. Recent technology which includes self-driving vehicle, language translation is been driven by data analysis and processing from the available Big Data.

Machine Learning is dynamic and many new techniques has been developed for the past few years, one of the most adaptive techniques that used is the deep leaning which involves the learning of complex neural network that can be applied to solve problems involving big data.

Machine Learning provide an infrastructure that manage and process huge chucks of data both structured and unstructured in real time. It gives data privacy and security.

Machine learning algorithms can extract information from large volumes of data with adequate computational resources and technical skills.

With great computational inputs, machine learning system help businesses manage, analyze, and use their big data far more successfully than ever before.

Proposed a framework that envisaged the broad picture of machine learning to deal with problem associated with big data.

It started by presenting a multi- structured input varies from different sources, which then follow by pipeline processing phase and implementation of machine learning.

Modern system knows that big data is powerful, and will find great usefulness when as when it is paired with intelligent automations. ML as a method in data analytics has aid the management big data and also has the ability to help interconnecting machines with large databases to learn new things by predicting from the correction of the big data.

The leveraging of ML and traditional algorithms to analyze the big data for organizations can provide solution to problems in multiple verticals and forecast the business future.ML algorithms for classification and prediction can be implemented on both private and government enterprises for observation and identification of abnormal behavior very quickly.

Implementing deep machine learning as a tool can help to solidify defense against cyber-attacks.

Big data and deep machine learning can be merging together to improve and strengthen cyber security.

Big Data, Artificial intelligence and machine Learning are becoming a great tool of analyzing business, it is used by enterprises, organizations in the public and private sectors. All aforementioned institution must provide the framework that is capable to test the assessment before implementation.

Consider a scenario where it is necessary to collect huge volume of data which is cumbersome and time- consuming process. Data are being correlated by diving deep, crunching numbers and understanding patterns and relationship between the data. The data obtained assists business to take quick decisions in management, customer service, sales management, advertisement and so on.

Analyzing big data using machine learning algorithms helps organizations forecast future trends