Methodologies to develop quantitative risk evaluation metrics

The goal of this work is to advance a new methodology to measure a severity cost for each host using the Common Vulnerability Scoring System (CVSS) based on base, temporal and environmental metrics by combining related sub-scores to produce a unique severity cost by modeling the problem's parameters in to a mathematical framework. We build our own CVSS Calculator using our equations to simplify the calculations of the vulnerabilities scores and to benchmark with other models. We design and develop a new approach to represent the cost assigned to each host by dividing the scores of the vulnerabilities to two main levels of privileges, user and root, and we classify these levels into operational levels to identify and calculate the severity cost of multi steps vulnerabilities. Finally we implement our framework on a simple network, using Nessus scanner as tool to discover known vulnerabilities and to implement the results to build and represent our cost centric attack graph

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

PLACES'10: The 3rd Workshop on Programmng Language Approaches to concurrency and Communication-Centric Software

Paphos, Cyprus. March 201

Attack Graph Generation and Analysis Techniques

As computer networks are emerging in everyday life, network security has become an important issue. Simultaneously, attacks are becoming more sophisticated, making the defense of computer networks increasingly difficult. Attack graph is a modeling tool used in the assessment of security of enterprise networks. Since its introduction a considerable amount of research effort has been spent in the development of theory and practices around the idea of attack graph. This paper presents a consolidated view of major attack graph generation and analysis techniques

Securing the software-defined networking control plane by using control and data dependency techniques

Software-defined networking (SDN) fundamentally changes how network and security practitioners design, implement, and manage their networks. SDN decouples the decision-making about traffic forwarding (i.e., the control plane) from the traffic being forwarded (i.e., the data plane). SDN also allows for network applications, or apps, to programmatically control network forwarding behavior and policy through a logically centralized control plane orchestrated by a set of SDN controllers. As a result of logical centralization, SDN controllers act as network operating systems in the coordination of shared data plane resources and comprehensive security policy implementation. SDN can support network security through the provision of security services and the assurances of policy enforcement. However, SDN’s programmability means that a network’s security considerations are different from those of traditional networks. For instance, an adversary who manipulates the programmable control plane can leverage significant control over the data plane’s behavior. In this dissertation, we demonstrate that the security posture of SDN can be enhanced using control and data dependency techniques that track information flow and enable understanding of application composability, control and data plane decoupling, and control plane insight. We support that statement through investigation of the various ways in which an attacker can use control flow and data flow dependencies to influence the SDN control plane under different threat models. We systematically explore and evaluate the SDN security posture through a combination of runtime, pre-runtime, and post-runtime contributions in both attack development and defense designs. We begin with the development a conceptual accountability framework for SDN. We analyze the extent to which various entities within SDN are accountable to each other, what they are accountable for, mechanisms for assurance about accountability, standards by which accountability is judged, and the consequences of breaching accountability. We discover significant research gaps in SDN’s accountability that impact SDN’s security posture. In particular, the results of applying the accountability framework showed that more control plane attribution is necessary at different layers of abstraction, and that insight motivated the remaining work in this dissertation. Next, we explore the influence of apps in the SDN control plane’s secure operation. We find that existing access control protections that limit what apps can do, such as role-based access controls, prove to be insufficient for preventing malicious apps from damaging control plane operations. The reason is SDN’s reliance on shared network state. We analyze SDN’s shared state model to discover that benign apps can be tricked into acting as “confused deputies”; malicious apps can poison the state used by benign apps, and that leads the benign apps to make decisions that negatively affect the network. That violates an implicit (but unenforced) integrity policy that governs the network’s security. Because of the strong interdependencies among apps that result from SDN’s shared state model, we show that apps can be easily co-opted as “gadgets,” and that allows an attacker who minimally controls one app to make changes to the network state beyond his or her originally granted permissions. We use a data provenance approach to track the lineage of the network state objects by assigning attribution to the set of processes and agents responsible for each control plane object. We design the ProvSDN tool to track API requests from apps as they access the shared network state’s objects, and to check requests against a predefined integrity policy to ensure that low-integrity apps cannot poison high-integrity apps. ProvSDN acts as both a reference monitor and an information flow control enforcement mechanism. Motivated by the strong inter-app dependencies, we investigate whether implicit data plane dependencies affect the control plane’s secure operation too. We find that data plane hosts typically have an outsized effect on the generation of the network state in reactive-based control plane designs. We also find that SDN’s event-based design, and the apps that subscribe to events, can induce dependencies that originate in the data plane and that eventually change forwarding behaviors. That combination gives attackers that are residing on data plane hosts significant opportunities to influence control plane decisions without having to compromise the SDN controller or apps. We design the EventScope tool to automatically identify where such vulnerabilities occur. EventScope clusters apps’ event usage to decide in which cases unhandled events should be handled, statically analyzes controller and app code to understand how events affect control plane execution, and identifies valid control flow paths in which a data plane attacker can reach vulnerable code to cause unintended data plane changes. We use EventScope to discover 14 new vulnerabilities, and we develop exploits that show how such vulnerabilities could allow an attacker to bypass an intended network (i.e., data plane) access control policy. This research direction is critical for SDN security evaluation because such vulnerabilities could be induced by host-based malware campaigns. Finally, although there are classes of vulnerabilities that can be removed prior to deployment, it is inevitable that other classes of attacks will occur that cannot be accounted for ahead of time. In those cases, a network or security practitioner would need to have the right amount of after-the-fact insight to diagnose the root causes of such attacks without being inundated with too much informa- tion. Challenges remain in 1) the modeling of apps and objects, which can lead to overestimation or underestimation of causal dependencies; and 2) the omission of a data plane model that causally links control and data plane activities. We design the PicoSDN tool to mitigate causal dependency modeling challenges, to account for a data plane model through the use of the data plane topology to link activities in the provenance graph, and to account for network semantics to appropriately query and summarize the control plane’s history. We show how prior work can hinder investigations and analysis in SDN-based attacks and demonstrate how PicoSDN can track SDN control plane attacks.Ope