Cognitive Machine Individualism in a Symbiotic Cybersecurity Policy Framework for the Preservation of Internet of Things Integrity: A Quantitative Study

This quantitative study examined the complex nature of modern cyber threats to propose the establishment of cyber as an interdisciplinary field of public policy initiated through the creation of a symbiotic cybersecurity policy framework. For the public good (and maintaining ideological balance), there must be recognition that public policies are at a transition point where the digital public square is a tangible reality that is more than a collection of technological widgets. The academic contribution of this research project is the fusion of humanistic principles with Internet of Things (IoT) technologies that alters our perception of the machine from an instrument of human engineering into a thinking peer to elevate cyber from technical esoterism into an interdisciplinary field of public policy. The contribution to the US national cybersecurity policy body of knowledge is a unified policy framework (manifested in the symbiotic cybersecurity policy triad) that could transform cybersecurity policies from network-based to entity-based. A correlation archival data design was used with the frequency of malicious software attacks as the dependent variable and diversity of intrusion techniques as the independent variable for RQ1. For RQ2, the frequency of detection events was the dependent variable and diversity of intrusion techniques was the independent variable. Self-determination Theory is the theoretical framework as the cognitive machine can recognize, self-endorse, and maintain its own identity based on a sense of self-motivation that is progressively shaped by the machine’s ability to learn. The transformation of cyber policies from technical esoterism into an interdisciplinary field of public policy starts with the recognition that the cognitive machine is an independent consumer of, advisor into, and influenced by public policy theories, philosophical constructs, and societal initiatives

Economists and Ecologists: Different Frames of Reference for Global Climate Change

Economists and ecologists, in general, have offered differing opinions about the seriousness of climate change and the need for rapid reductions in greenhouse gas emissions. Economists have tended to urge caution, focusing on the potential for large-scale cutbacks to upset the economy. Ecologists have tended to focus on the potential for catastrophic losses from climate change, and have urged extensive shifts in policy. This paper uses the tools of cost benefit analysis and the decision sciences to examine why members of the two disciplines often reach different conclusions. First, economists and ecologists start from different perspectives about what is the point of reference against which policies should be judged. Second, economists and ecologists tend to apply different discount rates to future impacts of climate change. Third, economists and ecologists are likely to interpret differently the substantive findings and expressed uncertainties of formal cost-benefit analysis. Using a simplified version of the DICE model of climate change, this paper explores how these different viewpoints can be expressed in practice

Predictive Cyber-security Analytics Framework: A non-homogenous Markov model for Security Quantification

Numerous security metrics have been proposed in the past for protecting computer networks. However we still lack effective techniques to accurately measure the predictive security risk of an enterprise taking into account the dynamic attributes associated with vulnerabilities that can change over time. In this paper we present a stochastic security framework for obtaining quantitative measures of security using attack graphs. Our model is novel as existing research in attack graph analysis do not consider the temporal aspects associated with the vulnerabilities, such as the availability of exploits and patches which can affect the overall network security based on how the vulnerabilities are interconnected and leveraged to compromise the system. Gaining a better understanding of the relationship between vulnerabilities and their lifecycle events can provide security practitioners a better understanding of their state of security. In order to have a more realistic representation of how the security state of the network would vary over time, a nonhomogeneous model is developed which incorporates a time dependent covariate, namely the vulnerability age. The daily transition-probability matrices are estimated using Frei's Vulnerability Lifecycle model. We also leverage the trusted CVSS metric domain to analyze how the total exploitability and impact measures evolve over a time period for a given network.Comment: 16 pages, 6 Figures in International Conference of Security, Privacy and Trust Management 201

Risk Assessment Framework for Evaluation of Cybersecurity Threats and Vulnerabilities in Medical Devices

Medical devices are vulnerable to cybersecurity exploitation and, while they can provide improvements to clinical care, they can put healthcare organizations and their patients at risk of adverse impacts. Evidence has shown that the proliferation of devices on medical networks present cybersecurity challenges for healthcare organizations due to their lack of built-in cybersecurity controls and the inability for organizations to implement security controls on them. The negative impacts of cybersecurity exploitation in healthcare can include the loss of patient confidentiality, risk to patient safety, negative financial consequences for the organization, and loss of business reputation. Assessing the risk of vulnerabilities and threats to medical devices can inform healthcare organizations toward prioritization of resources to reduce risk most effectively. In this research, we build upon a database-driven approach to risk assessment that is based on the elements of threat, vulnerability, asset, and control (TVA-C). We contribute a novel framework for the cybersecurity risk assessment of medical devices. Using a series of papers, we answer questions related to the risk assessment of networked medical devices. We first conducted a case study empirical analysis that determined the scope of security vulnerabilities in a typical computerized medical environment. We then created a cybersecurity risk framework to identify threats and vulnerabilities to medical devices and produce a quantified risk assessment. These results supported actionable decision making at managerial and operational levels of a typical healthcare organization. Finally, we applied the framework using a data set of medical devices received from a partnering healthcare organization. We compare the assessment results of our framework to a commercial risk assessment vulnerability management system used to analyze the same assets. The study also compares our framework results to the NIST Common Vulnerability Scoring System (CVSS) scores related to identified vulnerabilities reported through the Common Vulnerability and Exposure (CVE) program. As a result of these studies, we recognize several contributions to the area of healthcare cybersecurity. To begin with, we provide the first comprehensive vulnerability assessment of a robotic surgical environment, using a da Vinci surgical robot along with its supporting computing assets. This assessment supports the assertion that networked computer environments are at risk of being compromised in healthcare facilities. Next, our framework, known as MedDevRisk, provides a novel method for risk quantification. In addition, our assessment approach uniquely considers the assets that are of value to a medical organization, going beyond the medical device itself. Finally, our incorporation of risk scenarios into the framework represents a novel approach to medical device risk assessment, which was synthesized from other well-known standards. To our knowledge, our research is the first to apply a quantified assessment framework to the problem area of healthcare cybersecurity and medical networked devices. We would conclude that a reduction in the uncertainty about the riskiness of the cybersecurity status of medical devices can be achieved using this framework

Detecting cyber supply chain attacks on cyber physical systems using Bayesian belief network

Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain

Quantifying Impact of Cyber Actions on Missions or Business Processes: A Multilayer Propagative Approach

Ensuring the security of cyberspace is one of the most significant challenges of the modern world because of its complexity. As the cyber environment is getting more integrated with the real world, the direct impact of cybersecurity problems on actual business frequently occur. Therefore, operational and strategic decision makers in particular need to understand the cyber environment and its potential impact on business. Cyber risk has become a top agenda item for businesses all over the world and is listed as one of the most serious global risks with significant financial implications for businesses. Risk analysis is one of the primary tools used in this endeavor. Impact assessment, as an integral part of risk analysis, tries to estimate the possible damage of a cyber threat on business. It provides the main insight into risk prioritization as it incorporates business requirements into risk analysis for a better balance of security and usability. Moreover, impact assessment constitutes the main body of information flow between technical people and business leaders. Therefore, it requires the effective synergy of technological and business aspects of cybersecurity for protection against cyber threats. The purpose of this research is to develop a methodology to quantify the impact of cybersecurity events, incidents, and threats. The developed method addresses the issue of impact quantification from an interdependent system of systems point of view. The objectives of this research are (1) developing a quantitative model to determine the impact propagation within a layer of an enterprise (i.e., asset, service or business process layer); (2) developing a quantitative model to determine the impact propagation among different layers within an enterprise; (3) developing an approach to estimate the economic cost of a cyber incident or event. Although there are various studies in cybersecurity risk quantification, only a few studies focus on impact assessment at the business process layer by considering ripple effects at both the horizontal and vertical layers. This research develops an approach that quantifies the economic impact of cyber incidents, events and threats to business processes by considering the horizontal and vertical interdependencies and impact propagation within and among layers