Big data analysis and identification of behavioral patterns and security threats

Big data analysis and identification of behavioral patterns and security threats

Researcher : Dr. Nouradin Jafari Hezarany 

IR6364587064880288 

ORCID 0009-0687-7755 

Dr.n.jafary@gmail.com 

Abstract

In the contemporary world where big data are known as the main drivers of security developments, the ability to analyze and extract meaningful information from this huge volume of data has become vitally important. This paper explores the depth of big data and uses sophisticated machine learning techniques to identify behavioral patterns and security threats. Using advanced algorithms and rigorous analytical models, we seek to identify signals that can act as precursors to potential security events. Using real data sets and detailed simulations, this research examines how big data affects security decision-making processes and shows how to design more effective strategies to prevent and deal with security threats using these data.






key words

- Big data analysis
- Advanced machine learning
- Identification of behavioral patterns
- Predicting security threats
- National Security
- Complex statistical analyses
- Preventive strategies
- Threat modeling

Introduction

In recent decades, significant advances in information and communication technology have led to the generation of huge amounts of data. These big data, which are collected from diverse sources such as social networks, telecommunication systems, and sensor devices, have the potential to transform many fields, including national security. However, analyzing and extracting useful information from these data is challenging due to their volume, speed, variety, and accuracy. This article examines how big data can be used to identify behavioral patterns and security threats. We use advanced machine learning algorithms and statistical analysis to process this data and seek to identify signatures that can serve as key components in predicting and countering security threats. This research, focusing on new approaches in big data analysis, provides new perspectives in the field of national and global security and suggests strategies for the effective use of these data to increase security.

Statement of the problem

In a world where we see the production of astronomical amounts of data every day, the ability to analyze and use this data to identify and prevent security threats has become a necessity. However, big data analysis has its own difficulties due to its specific characteristics such as volume, speed, variety, and accuracy. This issue becomes more important, especially in the field of national security, where timely detection of suspicious behavior patterns and security threats can prevent unfortunate incidents. This research seeks to answer the question of how big data can be used for more accurate analysis and prediction of security threats.




Research purposes

1. Identification and analysis of behavioral patterns: Using machine learning techniques to identify behavioral patterns that can indicate security threats.

2. **Forecasting security threats:** Developing predictive models to detect security threats before they occur.

3. **Evaluation of big data analysis tools:** Review and evaluation of existing tools and methods for big data analysis in the field of security.

4. Designing coping strategies: Providing practical solutions and strategies for using big data to increase national and global security.

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