Insider attacks are one of the most challenging cybersecurity issues for companies, businesses and critical infrastructures. Despite the implemented perimeter defences, the risk of this kind of attack is still very high. In fact, the detection of insider attacks is a very complicated security task and presents a serious challenge to the research community. In this paper, we aim to address this issue by using deep learning algorithms Autoencoder and Variational Autoencoder deep. We will especially investigate the usefulness of applying these algorithms to automatically defend against potential internal threats, without human intervention. The effectiveness of these two models is evaluated on the public dataset CERT dataset (CERT r4.2). This version of the CERT Insider Threat Test dataset includes both benign and malicious activities generated from 1000 simulated users. The comparison results with other models show that the Variational Autoencoder neural network provides the best overall performance with a greater detection accuracy and a reasonable false positive rate

If an employee who recently gave two weeks' notice starts downloading large numbers of files from the company network and copying them to a thumb drive, it is entirely possible that he or she has no malicious intent. The employee could be saving innocuous files related to their employment record or examples of marketing pieces they created. However, in a small number of cases, the employee could be attempting to take confidential product designs, sensitive legal information, private employee data or trade secrets with them to a rival company. It can be difficult for a company to even spot these "insider risks," much less distinguish between routine behavior and the outlier that could destroy a company's competitive advantage or reputation. That's why Microsoft is offering a new Insider Risk Management solution within Microsoft 365 that uses machine learning to intelligently detect potentially risky behavior within a company.

Protecting user credentials from compromise is a nearly impossible task. Billions of credentials uncovered in data breaches are circulating online, and every month millions more are exposed, either through intrusions or unprotected servers. In addition, phishing attacks continue to dupe users into coughing up their credentials voluntarily. You'll always need layers of security controls to secure credentials. But when credential controls are bypassed--either by an external threat actor or an insider--user and entity behavioral analytics (UEBA) can help.

Behavior Analytics Market size is set to exceed USD 3.5 billion by 2024; according to a new research report by Global Market Insights, Inc. Technology advancement has bolstered the demand for behavior analytics market solutions among organizations to detect threats even before they occur and to mitigate their impact. Integration of advanced analytics and machine learning algorithms for analyzing user behavior allows automatic analysis enables organizations to link user and entity activity to support security analyst in threat detection and remediation. Besides these, advanced behavior analytics market systems also offer certain benefits over conventional security enterprise systems, such as end to end protection, automated response and access control. The healthcare sector has emerged as one of the major end-users of the behavior analytics market and is anticipated to register substantial growth during the forecast timeline. The growing demand for these solutions among healthcare organizations is attributed to the growing threat of insider attacks and data breaches among healthcare institutes, which poses a financial risk. Besides, healthcare institutes are more exposed to the risk of insider attacks owing to the general lack of cybersecurity infrastructure.