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

Blockchain-Based Transaction Validation Protocol for a Secure Distributed IoT Network

Funding Agency: 10.13039/501100010418-Institute for Information and Communications Technology Promotion (IITP), Ministry of Science and ICT (MSIT); 10.13039/501100003621-Korea Government;Peer reviewedPublisher PD

Measuring network security using Bayesian Network-based attack graphs

Given the increasing dependence of our societies on networked information systems, the overall security of such systems should be measured and improved. Recent research has explored the application of attack graphs and probabilistic security metrics to address this challenge. However, such work usually shares several limitations. First, individual vulnerabilities' scores are usually assumed to be independent. This assumption will not hold in many realistic cases where exploiting a vulnerability may change the score of other vulnerabilities. Second, the evolving nature of vulnerabilities and networks has generally been ignored. The scores of individual vulnerabilities are constantly changing due to released patches and exploits, which should be taken into account in measuring network security. To address these limitations, this thesis first proposes a Bayesian Network-based attack graph model for combining scores of individual vulnerabilities into a global measurement of network security. The application of Bayesian Networks allows us to handle dependency between scores and provides a sound theoretical foundation to network security metrics. We then extend the model using Dynamic Bayesian Networks in order to reason about the patterns and trends in changing scores of vulnerabilities. Finally, we implement and evaluate the proposed models through simulation studies

Evolution of security engineering artifacts: a state of the art survey

Security is an important quality aspect of modern open software systems. However, it is challenging to keep such systems secure because of evolution. Security evolution can only be managed adequately if it is considered for all artifacts throughout the software development lifecycle. This article provides state of the art on the evolution of security engineering artifacts. The article covers the state of the art on evolution of security requirements, security architectures, secure code, security tests, security models, and security risks as well as security monitoring. For each of these artifacts the authors give an overview of evolution and security aspects and discuss the state of the art on its security evolution in detail. Based on this comprehensive survey, they summarize key issues and discuss directions of future research

Attack graph compression

Attack graph has emerged as a useful tool for defending against multi-step network attacks involving correlated vulnerabilities. However, most current representations of attack graphs are not scalable [35]. Even the attack graph of a reasonably large network is usually incomprehensible to the human eyes. For realistic networks with tens of thousands of hosts and hundreds of vulnerabilities, even computing the attack graph may become infeasible. On the other hand, an attack graph of a real-world network usually has much redundancy due to the presence of hosts with similar configurations, such as those in an office or computer lab. To out best knowledge, existing work can at best hide such scalability issues through visualization techniques but cannot remove the redundant information, which does not comprise real solutions. This thesis presents a scalable representation of attack graphs for removing such redundancy. The representation is based on a well known compression technique, namely, reference encoding. More precisely, we use one host as the reference to other hosts with similar vulnerabilities and connectivity; details of the latter can then be omitted in the resultant attack graph. We introduce our compression model step by step. We start with a simple case where hosts have identical connectivity and vulnerabilities. We show that a one-host model can be used in some cases but it has limitations in representing remote exploits across different machines. We then introduce a two-node model to address the limitation and show that the one-host model is actually a special case of the two-node model. Next, we study the more realistic case where hosts may have different connectivity and vulnerabilities. We show that in some cases small differences are better hidden in textual rules while in other cases the differences are better handled by leaving the involved hosts outside the compression model. To evaluate the proposed compression model, we will describe a case study on a small network. We will also show experimental results based on random network topologies generated by existing tools. Both results confirm that our model can significantly reduce the complexity of attack graphs

Recycling and reuse of treated wastewater in urban India

Recycling and reuse of treated wastewater are an important part of the sanitation cycle and critical in an environment such as urban India with decreasing freshwater availability and increasing costs for delivering acceptable quality water, often from far distance. This report has been developed as a possible guidance document for the Indian government and gives substantial focus to the financial and economic benefits of wastewater recycling from the perspective of public spending. The report presents possible strategies for city and state planners and policymakers in view of the sanitation situation and the role of wastewater recycling in the larger cities in India (class I and II cities and towns with populations above 50,000), and focuses on recycling at the end of sewerage systems after treatment at sewage treatment plants

Trust engineering framework for software services

La presente tesis presenta un marco de trabajo que abarca distintas fases del ciclo de vida de los servicios software y que permite a ingenieros de requisitos, diseñadores y desarrolladores la integración en dichos servicios de modelos de confianza y reputación. En la fase de planificación, proponemos una metodología para evaluar la confianza en proveedores de Cloud antes de decidir si el sistema, o parte de él, se traslada al mismo. En la fase de análisis, ofrecemos una notación para la captura y representación de requisitos de confianza y reputación. Asimismo en esta misma fase, desarrollamos una metodología que permite detectar amenazas internas en un sistema a través de análisis de relaciones de confianza. Para la fase de diseño, proponemos un perfil UML que permite la especificación de modelos de confianza y reputación, lo cual facilita la siguiente fase de implementación, para la que desarrollamos un marco de trabajo que los desarrolladores pueden usar para implementar una amplia variedad de modelos de confianza y reputación. Finalmente, para la fase de verificación en tiempo de ejecución, presentamos un marco de trabajo desarrollado sobre una plataforma de sistemas auto-adaptativos que implementa el paradigma de modelos en tiempo de ejecución. Con dicho marco de trabajo, hacemos posible que los desarrolladores puedan implementar modelos de confianza y reputación, y que puedan usar la información proporcionada por dichos modelos para especificar políticas de reconfiguración en tiempo de ejecución. Esto permite que el sistema se adapte de forma que se mantengan niveles tolerables de confianza y reputación en los componentes de los que consiste. Todo los trabajos anteriores se apoyan sobre un marco conceptual que captura y relaciona entre sí las nociones más relevantes en los dominios de la confianza y la reputación