Biblio

Multi-tenant cloud networks have various security and monitoring service functions (SFs) that constitute a service function chain (SFC) between two endpoints. SF rule ordering overlaps and policy conflicts can cause increased latency, service disruption and security breaches in cloud networks. Software Defined Network (SDN) based Network Function Virtualization (NFV) has emerged as a solution that allows dynamic SFC composition and traffic steering in a cloud network. We propose an SDN enabled Universal Policy Checking (SUPC) framework, to provide 1) Flow Composition and Ordering by translating various SF rules into the OpenFlow format. This ensures elimination of redundant rules and policy compliance in SFC. 2) Flow conflict analysis to identify conflicts in header space and actions between various SF rules. Our results show a significant reduction in SF rules on composition. Additionally, our conflict checking mechanism was able to identify several rule conflicts that pose security, efficiency, and service availability issues in the cloud network.

Large scale cloud networks consist of distributed networking and computing elements that process critical information and thus security is a key requirement for any environment. Unfortunately, assessing the security state of such networks is a challenging task and the tools used in the past by security experts such as packet filtering, firewall, Intrusion Detection Systems (IDS) etc., provide a reactive security mechanism. In this paper, we introduce a Moving Target Defense (MTD) based proactive security framework for monitoring attacks which lets us identify and reason about multi-stage attacks that target software vulnerabilities present in a cloud network. We formulate the multi-stage attack scenario as a two-player zero-sum Markov Game (between the attacker and the network administrator) on attack graphs. The rewards and transition probabilities are obtained by leveraging the expert knowledge present in the Common Vulnerability Scoring System (CVSS). Our framework identifies an attacker's optimal policy and places countermeasures to ensure that this attack policy is always detected, thus forcing the attacker to use a sub-optimal policy with higher cost.

Software Defined Networking (SDN) has emerged as a revolutionary paradigm to manage cloud infrastructure. SDN lacks scalable trust setup and verification mechanism between Data Plane-Control Plane elements, Control Plane elements, and Control Plane-Application Plane. Trust management schemes like Public Key Infrastructure (PKI) used currently in SDN are slow for trust establishment in a larger cloud environment. We propose a distributed trust mechanism - TRUFL to establish and verify trust in SDN. The distributed framework utilizes parallelism in trust management, in effect faster transfer rates and reduced latency compared to centralized trust management. The TRUFL framework scales well with the number of OpenFlow rules when compared to existing research works.

Security issues in a Software Defined Network (SDN) environment like system vulnerabilities and intrusion attempts can pose a security risk for multi-tenant network managed by SDN. In this research work, Moving target defense (MTD)technique based on shuffle strategy - port hopping has been employed to increase the difficulty for the attacker trying to exploit the cloud network. Our research workMASON, considers the problem of multi-stage attacks in a network managed using SDN. SDN controller can be used to dynamically reconfigure the network and render attacker»s knowledge in multi-stage attacks redundant. We have used a threat score based on vulnerability information and intrusion attempts to identify Virtual Machines (VMs) in systems with high-security risk and implement MTD countermeasures port hopping to assess threat score reduction in a cloud network.

SDN provides a way to manage complex networks by introducing programmability and abstraction of the control plane. All networks suffer from attacks to critical infrastructure and services such as DDoS attacks. We make use of the programmability provided by the SDN environment to provide a game theoretic attack analysis and countermeasure selection model in this research work. The model is based on reward and punishment in a dynamic game with multiple players. The network bandwidth of attackers is downgraded for a certain period of time, and restored to normal when the player resumes cooperation. The presented solution is based on Nash Folk Theorem, which is used to implement a punishment mechanism for attackers who are part of DDoS traffic, and reward for players who cooperate, in effect enforcing desired outcome for the network administrator.