Detecting and Preventing Cyber Insider Threats: A Survey

By Liu Liu, O. De Vel, Qing-Long Han, Jun Zhang, Yang Xiang. IEEE Communications Surveys and Tutorials, 2018

341 citations recorded by the source corpus.

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 the enterprise-level security defence mechanisms and often have privileged access to the network, detecting and preventing insider threats is a complex and challenging problem. In fact, many schemes and systems have been proposed to address insider threats from different perspectives, such as intent, type of threat, or available audit data source. This survey attempts to line up these works together with only three most common types of insider namely traitor, masquerader, and unintentional perpetrator, while reviewing the countermeasures from a data analytics perspective. Uniquely, this survey takes into account the early stage threats which may lead to a malicious insider rising up. When direct and indirect threats are put on the same page, all the relevant works can be categorised as host, network, or contextual data-based according to audit data source and each work is reviewed for its capability against insider threats, how the information is extracted from the engaged data sources, and what the decision-making algorithm is. The works are also compared and contrasted. Finally, some issues are raised based on the observations from the reviewed works and new research gaps and challenges identified.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a1daaaa50792a9b6c1164707c15874ce5122ac54

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Qing-Long Han
2. Xianming Zhang
3. Xiaohua Ge
4. Lei Ding
5. Fuwen Yang
6. Yang Xiang
7. Jun Zhang
8. Sheng Wen

In Information Systems

1. Sheng Wen
2. Qing-Long Han
3. Guanjun Lin
4. Jun Zhang
5. Yang Xiang

In Signal Processing

1. Sheng Wen
2. Jun Zhang
3. Yang Xiang

Citations in the corpus

• A Survey of IoT Applications in Blockchain Systems (2020, background): “In the traditional data-sharing system, user information and privacy are often revealed, mis-handled, stole, and accessed without authorization [Chen and Zhao 2012; Liu et al. 2018; Sun et al. 2018].”
• Distributed Event-Triggered Estimation Over Sensor Networks: A Survey (2020, background): “bringing more opportunities to security threats, which renders distributed secure estimation fundamentally significant in sensor networks [128]–[131].”
• Software Vulnerability Detection Using Deep Neural Networks: A Survey (2020, background): “Data-driven vulnerability discovery using pattern recognition and machine learning (ML) provides alternative solutions for automated and potentially more efficient vulnerability discovery [21], [33], [63], [101].”