An In-Depth Analysis of Contemporary Security Breaches using Time Series Analysis

DOI

10.2991/978-94-6463-471-6_68How to use a DOI?

Keywords

Time-Series Analysis; Cybersecurity; Security breaches; Machine Learning; Regression models; Trend analysis; Organizational breaches; Behavioral patterns

Abstract

In the evolving cybersecurity landscape, security breaches have become a significant concern, leading to unauthorized disclosure of personal information. Hackers engage in illicit activities, fostering the trade of sensitive data on darkweb platforms like AlphaBay Market, Hansa, and Dream Market. To address this escalating threat, understanding the intricacies of security breaches is imperative. This project aims to unravel contemporary breach dynamics through a rigorous analytical approach. It involves scrutinizing breach types, identifying susceptible organizational sectors, and probing root causes. The investigation also quantifies compromise scales, assessing affected records and discerning patterns across different years. Employing Time-Series Analysis, specifically utilizing the Autoregressive Integrated Moving Average (ARIMA) model, the chosen methodology is rooted in proven forecasting accuracy, robust statistical foundation, and adaptability to diverse datasets. Within this project, ARIMA emerges as a superior choice, showcasing its significance and outperforming other regression models. The overarching objective of time series analysis is not only to decipher immediate breach implications but also to uncover behaviors, discern trends, and identify recurrent patterns. This analytical approach provides a proactive means for organizations to fortify defenses against evolving cybersecurity challenges in a rapidly changing digital landscape.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - A. Sree Rama Chandra Murthy
AU  - Muthyala Sravani
AU  - Gajaganti Ruthmani
AU  - Vegineti Umesh Chandra
PY  - 2024
DA  - 2024/07/30
TI  - An In-Depth Analysis of Contemporary Security Breaches using Time Series Analysis
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 701
EP  - 709
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_68
DO  - 10.2991/978-94-6463-471-6_68
ID  - Murthy2024
ER  -