Security and privacy in metaverse: A comprehensive survey

Y Huang, YJ Li, Z Cai - Big Data Mining and Analytics, 2023 - ieeexplore.ieee.org
Metaverse describes a new shape of cyberspace and has become a hot-trending word since
2021. There are many explanations about what Meterverse is and attempts to provide a …

Detecting and preventing cyber insider threats: A survey

L Liu, O De Vel, QL Han, J Zhang… - … Surveys & Tutorials, 2018 - ieeexplore.ieee.org
Information communications technology systems are facing an increasing number of cyber
security threats, the majority of which are originated by insiders. As insiders reside behind …

An insider data leakage detection using one-hot encoding, synthetic minority oversampling and machine learning techniques

T Al-Shehari, RA Alsowail - Entropy, 2021 - mdpi.com
Insider threats are malicious acts that can be carried out by an authorized employee within
an organization. Insider threats represent a major cybersecurity challenge for private and …

Insight into insiders and it: A survey of insider threat taxonomies, analysis, modeling, and countermeasures

I Homoliak, F Toffalini, J Guarnizo, Y Elovici… - ACM Computing …, 2019 - dl.acm.org
Insider threats are one of today's most challenging cybersecurity issues that are not well
addressed by commonly employed security solutions. In this work, we propose structural …

Enterprise data breach: causes, challenges, prevention, and future directions

L Cheng, F Liu, D Yao - Wiley Interdisciplinary Reviews: Data …, 2017 - Wiley Online Library
A data breach is the intentional or inadvertent exposure of confidential information to
unauthorized parties. In the digital era, data has become one of the most critical components …

AI^ 2: training a big data machine to defend

K Veeramachaneni, I Arnaldo… - 2016 IEEE 2nd …, 2016 - ieeexplore.ieee.org
We present AI2, an analyst-in-the-loop security system where Analyst Intuition (AI) is put
together with state-of-the-art machine learning to build a complete end-to-end Artificially …

Analyzing data granularity levels for insider threat detection using machine learning

DC Le, N Zincir-Heywood… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Malicious insider attacks represent one of the most damaging threats to networked systems
of companies and government agencies. There is a unique set of challenges that come with …

Anomaly detection for insider threats using unsupervised ensembles

DC Le, N Zincir-Heywood - IEEE Transactions on Network and …, 2021 - ieeexplore.ieee.org
Insider threat represents a major cybersecurity challenge to companies, organizations, and
government agencies. Insider threat detection involves many challenges, including …

A new take on detecting insider threats: exploring the use of hidden markov models

T Rashid, I Agrafiotis, JRC Nurse - Proceedings of the 8th ACM CCS …, 2016 - dl.acm.org
The threat that malicious insiders pose towards organisations is a significant problem. In this
paper, we investigate the task of detecting such insiders through a novel method of …

Incorporating expert feedback into active anomaly discovery

S Das, WK Wong, T Dietterich, A Fern… - 2016 IEEE 16th …, 2016 - ieeexplore.ieee.org
Unsupervised anomaly detection algorithms search for outliers and then predict that these
outliers are the anomalies. When deployed, however, these algorithms are often criticized …