Novel Alert Visualization: The Development of a Visual Analytics Prototype for Mitigation of Malicious Insider Cyber Threats

Cyber insider threat is one of the most difficult risks to mitigate in organizations. However, innovative validated visualizations for cyber analysts to better decipher and react to detected anomalies has not been reported in literature or in industry. Attacks caused by malicious insiders can cause millions of dollars in losses to an organization. Though there have been advances in Intrusion Detection Systems (IDSs) over the last three decades, traditional IDSs do not specialize in anomaly identification caused by insiders. There is also a profuse amount of data being presented to cyber analysts when deciphering big data and reacting to data breach incidents using complex information systems. Information visualization is pertinent to the identification and mitigation of malicious cyber insider threats. The main goal of this study was to develop and validate, using Subject Matter Experts (SME), an executive insider threat dashboard visualization prototype. Using the developed prototype, an experimental study was conducted, which aimed to assess the perceived effectiveness in enhancing the analysts’ interface when complex data correlations are presented to mitigate malicious insiders cyber threats. Dashboard-based visualization techniques could be used to give full visibility of network progress and problems in real-time, especially within complex and stressful environments. For instance, in an Emergency Room (ER), there are four main vital signs used for urgent patient triage. Cybersecurity vital signs can give cyber analysts clear focal points during high severity issues. Pilots must expeditiously reference the Heads Up Display (HUD), which presents only key indicators to make critical decisions during unwarranted deviations or an immediate threat. Current dashboard-based visualization techniques have yet to be fully validated within the field of cybersecurity. This study developed a visualization prototype based on SME input utilizing the Delphi method. SMEs validated the perceived effectiveness of several different types of the developed visualization dashboard. Quantitative analysis of SME’s perceived effectiveness via self-reported value and satisfaction data as well as qualitative analysis of feedback provided during the experiments using the prototype developed were performed. This study identified critical cyber visualization variables and identified visualization techniques. The identifications were then used to develop QUICK.v™ a prototype to be used when mitigating potentially malicious cyber insider threats. The perceived effectiveness of QUICK.v™ was then validated. Insights from this study can aid organizations in enhancing cybersecurity dashboard visualizations by depicting only critical cybersecurity vital signs

A System for Detecting Malicious Insider Data Theft in IaaS Cloud Environments

The Cloud Security Alliance lists data theft and insider attacks as critical threats to cloud security. Our work puts forth an approach using a train, monitor, detect pattern which leverages a stateful rule based k-nearest neighbors anomaly detection technique and system state data to detect inside attacker data theft on Infrastructure as a Service (IaaS) nodes. We posit, instantiate, and demonstrate our approach using the Eucalyptus cloud computing infrastructure where we observe a 100 percent detection rate for abnormal login events and data copies to outside systems

EEVi – framework for evaluating the effectiveness of visualization in cyber-security

Cyber-security visualization is an up-and-coming area which aims to reduce security analysts’ workload by presenting information as visual analytics rather than a string of text and characters. But the adoption of the resultant visualizations has not increased. The literature indicates a research gap of a lack of guidelines and standardized evaluation techniques for effective visualization in cyber-security, as a reason for it. Therefore, this research addresses the research gap by developing a framework called EEVi for effective cyber-security visualizations for the performed task. The term ‘effective visualization’ can be defined as the features of visualization that are crucial to perform a certain task successfully. EEVi has been developed by analyzing qualitative data that leads to the formation of cognitive relationships (called links) between data that act as guidelines for effective cyber-security visualization in terms of the performed task. The methodology to develop this framework can be applied to other fields to understand cognitive relationships between data. Additionally, the analysis presents a glimpse into the usage of EEVi in cyber-security visualization

Internal hacking detection using machine learning

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Being able to prevent and early detect insider threats through an automated forewarning system has been a massive challenge for large companies. In recent years, to fill this gap several anomaly user behavior algorithms based on machine learning have been proposed, experimentally evaluated and analyzed in numerous surveys. The present work was conducted in the cybersecurity department (DCY) of Altice Portugal (MEO) and aims to address this problem identifying the families of unsupervised anomaly detection techniques that are more effective for insider threats detection based on a large dataset corresponding to a collection of users’ access log records. To this end, multi-domain attributes related to possible insider threats are interactively extracted and processed, creating a summary of user account’s daily activity. A clusteringbased algorithm that groups and characterizes similar accounts was applied. Without any example anomalies required in the training set, anomaly detection techniques were computed over those profiles, identifying unusual changes in user account behavior on a current day. Finally, to make it easier for analysts and managers to understand the anomaly, anomaly metrics and a visualization dashboard were created. To evaluate the efficiency of this project ten insider threat scenarios were injected and was found that the system can successfully detect anomalous behavior that may be an insider threat event