Scalable attack modelling in support of security information and event management

Includes bibliographical referencesWhile assessing security on single devices can be performed using vulnerability assessment tools, modelling of more intricate attacks, which incorporate multiple steps on different machines, requires more advanced techniques. Attack graphs are a promising technique, however they face a number of challenges. An attack graph is an abstract description of what attacks are possible against a specific network. Nodes in an attack graph represent the state of a network at a point in time while arcs between nodes indicate the transformation of a network from one state to another, via the exploit of a vulnerability. Using attack graphs allows system and network configuration information to be correlated and analysed to indicate imminent threats. This approach is limited by several serious issues including the state-space explosion, due to the exponential nature of the problem, and the difficulty in visualising an exhaustive graph of all potential attacks. Furthermore, the lack of availability of information regarding exploits, in a standardised format, makes it difficult to model atomic attacks in terms of exploit requirements and effects. This thesis has as its objective to address these issues and to present a proof of concept solution. It describes a proof of concept implementation of an automated attack graph based tool, to assist in evaluation of network security, assessing whether a sequence of actions could lead to an attacker gaining access to critical network resources. Key objectives are the investigation of attacks that can be modelled, discovery of attack paths, development of techniques to strengthen networks based on attack paths, and testing scalability for larger networks. The proof of concept framework, Network Vulnerability Analyser (NVA), sources vulnerability information from National Vulnerability Database (NVD), a comprehensive, publicly available vulnerability database, transforming it into atomic exploit actions. NVA combines these with a topological network model, using an automated planner to identify potential attacks on network devices. Automated planning is an area of Artificial Intelligence (AI) which focuses on the computational deliberation process of action sequences, by measuring their expected outcomes and this technique is applied to support discovery of a best possible solution to an attack graph that is created. Through the use of heuristics developed for this study, unpromising regions of an attack graph are avoided. Effectively, this prevents the state-space explosion problem associated with modelling large scale networks, only enumerating critical paths rather than an exhaustive graph. SGPlan5 was selected as the most suitable automated planner for this study and was integrated into the system, employing network and exploit models to construct critical attack paths. A critical attack path indicates the most likely attack vector to be used in compromising a targeted device. Critical attack paths are identifed by SGPlan5 by using a heuristic to search through the state-space the attack which yields the highest aggregated severity score. CVSS severity scores were selected as a means of guiding state-space exploration since they are currently the only publicly available metric which can measure the impact of an exploited vulnerability. Two analysis techniques have been implemented to further support the user in making an informed decision as to how to prevent identified attacks. Evaluation of NVA was broken down into a demonstration of its effectiveness in two case studies, and analysis of its scalability potential. Results demonstrate that NVA can successfully enumerate the expected critical attack paths and also this information to establish a solution to identified attacks. Additionally, performance and scalability testing illustrate NVA's success in application to realistically sized larger networks

Decision support for infrastructure network vulnerability assessment in natural disaster crisis situations

This thesis deals with infrastructure network vulnerability analysis in the natural disaster context. It starts from the observation that infrastructure such as water supply or power grid has significant influence on natural disasters’ indirect consequences. The aim is to model the vulnerability to take efficient actions. The scientific approach is divided into two complementary parts. The first one deals with the vulnerability assessment, while the second one focuses on the decision aiding process to be implemented for the assessed vulnerability reducing. The proper vulnerability analysis is based on the analysis objects modelling. In order to achieve this, we will adopt a graph theory representation. A literature review will allow us to identify the graph structure which best suits the context of the thesis. In a multi network analysis environment, interdependences, i.e. relationships between components of the same network or different networks - are a determining factor for any vulnerability model. We have thus proposed an approach to model interdependence compatible with the graph theory. There are two types of relationships: the one first is functional (dependence), while the second one is dysfunctional (influence). The vulnerability is assessed by a simulation-based approach. It is composed of one part relating to the system ability to resist the feared event; and the other part relative to its ability to be back on its nominal state after the disaster. When the vulnerability is determined, the next step will be to take the necessary decisions to manage it. This part on the decision aiding is itself divided into two sub parts: first of all the process to be used for the crisis management is established. Then a methodology for decision aiding is proposed and results on a Decision Support System development. In the age of the internet and social networks, it is possible to deploy the application on the internet

Attack graph approach to dynamic network vulnerability analysis and countermeasures

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyIt is widely accepted that modern computer networks (often presented as a heterogeneous collection of functioning organisations, applications, software, and hardware) contain vulnerabilities. This research proposes a new methodology to compute a dynamic severity cost for each state. Here a state refers to the behaviour of a system during an attack; an example of a state is where an attacker could influence the information on an application to alter the credentials. This is performed by utilising a modified variant of the Common Vulnerability Scoring System (CVSS), referred to as a Dynamic Vulnerability Scoring System (DVSS). This calculates scores of intrinsic, time-based, and ecological metrics by combining related sub-scores and modelling the problem’s parameters into a mathematical framework to develop a unique severity cost. The individual static nature of CVSS affects the scoring value, so the author has adapted a novel model to produce a DVSS metric that is more precise and efficient. In this approach, different parameters are used to compute the final scores determined from a number of parameters including network architecture, device setting, and the impact of vulnerability interactions. An attack graph (AG) is a security model representing the chains of vulnerability exploits in a network. A number of researchers have acknowledged the attack graph visual complexity and a lack of in-depth understanding. Current attack graph tools are constrained to only limited attributes or even rely on hand-generated input. The automatic formation of vulnerability information has been troublesome and vulnerability descriptions are frequently created by hand, or based on limited data. The network architectures and configurations along with the interactions between the individual vulnerabilities are considered in the method of computing the Cost using the DVSS and a dynamic cost-centric framework. A new methodology was built up to present an attack graph with a dynamic cost metric based on DVSS and also a novel methodology to estimate and represent the cost-centric approach for each host’ states was followed out. A framework is carried out on a test network, using the Nessus scanner to detect known vulnerabilities, implement these results and to build and represent the dynamic cost centric attack graph using ranking algorithms (in a standardised fashion to Mehta et al. 2006 and Kijsanayothin, 2010). However, instead of using vulnerabilities for each host, a CostRank Markov Model has developed utilising a novel cost-centric approach, thereby reducing the complexity in the attack graph and reducing the problem of visibility. An analogous parallel algorithm is developed to implement CostRank. The reason for developing a parallel CostRank Algorithm is to expedite the states ranking calculations for the increasing number of hosts and/or vulnerabilities. In the same way, the author intends to secure large scale networks that require fast and reliable computing to calculate the ranking of enormous graphs with thousands of vertices (states) and millions of arcs (representing an action to move from one state to another). In this proposed approach, the focus on a parallel CostRank computational architecture to appraise the enhancement in CostRank calculations and scalability of of the algorithm. In particular, a partitioning of input data, graph files and ranking vectors with a load balancing technique can enhance the performance and scalability of CostRank computations in parallel. A practical model of analogous CostRank parallel calculation is undertaken, resulting in a substantial decrease in calculations communication levels and in iteration time. The results are presented in an analytical approach in terms of scalability, efficiency, memory usage, speed up and input/output rates. Finally, a countermeasures model is developed to protect against network attacks by using a Dynamic Countermeasures Attack Tree (DCAT). The following scheme is used to build DCAT tree (i) using scalable parallel CostRank Algorithm to determine the critical asset, that system administrators need to protect; (ii) Track the Nessus scanner to determine the vulnerabilities associated with the asset using the dynamic cost centric framework and DVSS; (iii) Check out all published mitigations for all vulnerabilities. (iv) Assess how well the security solution mitigates those risks; (v) Assess DCAT algorithm in terms of effective security cost, probability and cost/benefit analysis to reduce the total impact of a specific vulnerability

Analysis of Bulk Power System Resilience Using Vulnerability Graph

Critical infrastructure such as a Bulk Power System (BPS) should have some quantifiable measure of resiliency and definite rule-sets to achieve a certain resilience value. Industrial Control System (ICS) and Supervisory Control and Data Acquisition (SCADA) networks are integral parts of BPS. BPS or ICS are themselves not vulnerable because of their proprietary technology, but when the control network and the corporate network need to have communications for performance measurements and reporting, the ICS or BPS become vulnerable to cyber-attacks. Thus, a systematic way of quantifying resiliency and identifying crucial nodes in the network is critical for addressing the cyber resiliency measurement process. This can help security analysts and power system operators in the decision-making process. This thesis focuses on the resilience analysis of BPS and proposes a ranking algorithm to identify critical nodes in the network. Although there are some ranking algorithms already in place, but they lack comprehensive inclusion of the factors that are critical in the cyber domain. This thesis has analyzed a range of factors which are critical from the point of view of cyber-attacks and come up with a MADM (Multi-Attribute Decision Making) based ranking method. The node ranking process will not only help improve the resilience but also facilitate hardening the network from vulnerabilities and threats. The proposed method is called MVNRank which stands for Multiple Vulnerability Node Rank. MVNRank algorithm takes into account the asset value of the hosts, the exploitability and impact scores of vulnerabilities as quantified by CVSS (Common Vulnerability Scoring System). It also considers the total number of vulnerabilities and severity level of each vulnerability, degree centrality of the nodes in vulnerability graph and the attacker’s distance from the target node. We are using a multi-layered directed acyclic graph (DAG) model and ranking the critical nodes in the corporate and control network which falls in the paths to the target ICS. We don\u27t rank the ICS nodes but use them to calculate the potential power loss capability of the control center nodes using the assumed ICS connectivity to BPS. Unlike most of the works, we have considered multiple vulnerabilities for each node in the network while generating the rank by using a weighted average method. The resilience computation is highly time consuming as it considers all the possible attack paths from the source to the target node which increases in a multiplicative manner based on the number of nodes and vulnerabilities. Thus, one of the goals of this thesis is to reduce the simulation time to compute resilience which is achieved as illustrated in the simulation results

Assessing the criticality of interdependent power and gas systems using complex networks and load flow techniques

Gas and electricity transmission systems are increasingly interconnected, and an attack on certain assets can cause serious energy supply disruptions, as stated in recommendation (EU) 2019/553 on cybersecurity in the energy sector, recently approved by the European Commission. This study aims to assess the vulnerability of coupled natural gas and electricity infrastructures and proposes a method based on graph theory that incorporates the effects of interdependencies between networks. This study is built in a joint framework, where two different attack strategies are applied to the integrated systems: (1) disruptions to facilities with most links and (2) disruptions to the most important facilities in terms of flow. The vulnerability is measured after each network attack by quantifying the unmet load (UL) through a power flow analysis and calculating the topological damage of the systems with the geodesic vulnerability (v) index. The proposed simulation framework is applied to a case study that consists of the IEEE 118-bus test system and a 25-node high-pressure natural gas network, where both are coupled through seven gas-fired power plants (GFPPs) and three electric compressors (ECs). The methodology is useful for estimating vulnerability in both systems in a coupled manner, studying the propagation of interdependencies in the two networks and showing the applicability of the v index as a substitute for the UL index

Network hierarchy evolution and system vulnerability in power grids

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The seldom addressed network hierarchy property and its relationship with vulnerability analysis for power transmission grids from a complex-systems point of view are given in this paper. We analyze and compare the evolution of network hierarchy for the dynamic vulnerability evaluation of four different power transmission grids of real cases. Several meaningful results suggest that the vulnerability of power grids can be assessed by means of a network hierarchy evolution analysis. First, the network hierarchy evolution may be used as a novel measurement to quantify the robustness of power grids. Second, an antipyramidal structure appears in the most robust network when quantifying cascading failures by the proposed hierarchy metric. Furthermore, the analysis results are also validated and proved by empirical reliability data. We show that our proposed hierarchy evolution analysis methodology could be used to assess the vulnerability of power grids or even other networks from a complex-systems point of view.Peer ReviewedPostprint (author's final draft