5,488 research outputs found
A Graphical Adversarial Risk Analysis Model for Oil and Gas Drilling Cybersecurity
Oil and gas drilling is based, increasingly, on operational technology, whose
cybersecurity is complicated by several challenges. We propose a graphical
model for cybersecurity risk assessment based on Adversarial Risk Analysis to
face those challenges. We also provide an example of the model in the context
of an offshore drilling rig. The proposed model provides a more formal and
comprehensive analysis of risks, still using the standard business language
based on decisions, risks, and value.Comment: In Proceedings GraMSec 2014, arXiv:1404.163
Defense against Insider Threat: a Framework for Gathering Goal-based Requirements
Insider threat is becoming comparable to outsider threat in frequency of security events. This is a worrying situation, since insider attacks have a high probability of success because insiders have authorized access and legitimate privileges. Despite their importance, insider threats are still not properly addressed by organizations. We contribute to reverse this situation by introducing a framework composed of a method for identification and assessment of insider threat risks and of two supporting deliverables for awareness of insider threat. The deliverables are: (i) attack strategies structured in four decomposition trees, and (ii) a matrix which correlates defense strategies, attack strategies and control principles. The method output consists of goal-based requirements for the defense against insiders
Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications
Social Virtual Reality based Learning Environments (VRLEs) such as vSocial
render instructional content in a three-dimensional immersive computer
experience for training youth with learning impediments. There are limited
prior works that explored attack vulnerability in VR technology, and hence
there is a need for systematic frameworks to quantify risks corresponding to
security, privacy, and safety (SPS) threats. The SPS threats can adversely
impact the educational user experience and hinder delivery of VRLE content. In
this paper, we propose a novel risk assessment framework that utilizes attack
trees to calculate a risk score for varied VRLE threats with rate and duration
of threats as inputs. We compare the impact of a well-constructed attack tree
with an adhoc attack tree to study the trade-offs between overheads in managing
attack trees, and the cost of risk mitigation when vulnerabilities are
identified. We use a vSocial VRLE testbed in a case study to showcase the
effectiveness of our framework and demonstrate how a suitable attack tree
formalism can result in a more safer, privacy-preserving and secure VRLE
system.Comment: Tp appear in the CCNC 2019 Conferenc
Improving SIEM for critical SCADA water infrastructures using machine learning
Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset
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