Survey of Techniques on Data Leakage Protection and Methods to address the Insider threat

Abstract

Data leakage is a problem that companies and organizations face every day around the world. Mainly the data leak caused by the internal threat posed by authorized personnel to manipulate confidential information. The main objective of this work is to survey the literature to detect the existing techniques to protect against data leakage and to identify the methods used to address the insider threat. For this, a literature review of scientific databases was carried out in the period from 2011 to 2022, which resulted in 42 relevant papers. It was obtained that from 2017 to date, 60% of the studies found are concentrated and that 90% come from conferences and publications in journals. Significant advances were detected in protection systems against data leakage with the incorporation of new techniques and technologies, such as machine learning, blockchain, and digital rights management policies. In 40% of the relevant studies, significant interest was shown in avoiding internal threats. The most used techniques in the analyzed DLP tools were encryption and machine learning.

Precipitation Nowcasting using Data Augmentation

“…For training and validating the forecasting models, we first split the examples resulting from the sliding window technique into train, validation and test datasets. We took some measures to prevent data leakage issues [Montano et al, 2022]. First, we did the splitting using the proportions 70%, 20% and 10% for train, validation and test datasets, respectively, without shuffling the observations, to maintain the temporal order between datasets.…”

A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods

“…Concurrently, data loss prevention (DLP) solutions gained prominence. DLP systems focused on monitoring and preventing the unauthorized transfer of sensitive data, a critical aspect of combating insider threats related to data exfiltration [7]. Furthermore, the integration of machine learning and artificial intelligence (AI) into insider threat detection marked a significant turning point.…”