Biblio

Network policies govern the use of an institution's networks, and are usually written in a high-level human-readable natural language. Normally these policies are enforced by low-level, technically detailed network configurations. The translation from network policies into network configurations is a tedious, manual and error-prone process. To address this issue, we propose a new intermediate language called POlicy LANguage for Campus Operations (POLANCO), which is a human-readable network policy definition language intended to approximate natural language. Because POLANCO is a high-level language, the translation from natural language policies to POLANCO is straightforward. Despite being a high-level human readable language, POLANCO can be used to express network policies in a technically precise way so that policies written in POLANCO can be automatically translated into a set of software defined networking (SDN) rules and actions that enforce the policies. Moreover, POLANCO is capable of incorporating information about the current network state, reacting to changes in the network and adjusting SDN rules to ensure network policies continue to be enforced correctly. We present policy examples found on various public university websites and show how they can be written as simplified human-readable statements using POLANCO and how they can be automatically translated into SDN rules that correctly enforce these policies.

Cloud Computing is the most suitable environment for the collaboration of multiple organizations via its multi-tenancy architecture. However, due to the distributed management of policies within these collaborations, they may contain several anomalies, such as conflicts and redundancies, which may lead to both safety and availability problems. On the other hand, current cloud computing solutions do not offer verification tools to manage access control policies. In this paper, we propose a cloud policy verification service (CPVS), that facilitates to users the management of there own security policies within Openstack cloud environment. Specifically, the proposed cloud service offers a policy verification approach to dynamically choose the adequate policy using Aspect-Oriented Finite State Machines (AO-FSM), where pointcuts and advices are used to adopt Domain-Specific Language (DSL) state machine artifacts. The pointcuts define states' patterns representing anomalies (e.g., conflicts) that may occur in a security policy, while the advices define the actions applied at the selected pointcuts to remove the anomalies. In order to demonstrate the efficiency of our approach, we provide time and space complexities. The approach was implemented as middleware service within Openstack cloud environment. The implementation results show that the middleware can detect and resolve different policy anomalies in an efficient manner.

Host-based anomaly intrusion detection system design is very challenging due to the notoriously high false alarm rate. This paper introduces a new host-based anomaly intrusion detection methodology using discontiguous system call patterns, in an attempt to increase detection rates whilst reducing false alarm rates. The key concept is to apply a semantic structure to kernel level system calls in order to reflect intrinsic activities hidden in high-level programming languages, which can help understand program anomaly behaviour. Excellent results were demonstrated using a variety of decision engines, evaluating the KDD98 and UNM data sets, and a new, modern data set. The ADFA Linux data set was created as part of this research using a modern operating system and contemporary hacking methods, and is now publicly available. Furthermore, the new semantic method possesses an inherent resilience to mimicry attacks, and demonstrated a high level of portability between different operating system versions.

Host-based anomaly intrusion detection system design is very challenging due to the notoriously high false alarm rate. This paper introduces a new host-based anomaly intrusion detection methodology using discontiguous system call patterns, in an attempt to increase detection rates whilst reducing false alarm rates. The key concept is to apply a semantic structure to kernel level system calls in order to reflect intrinsic activities hidden in high-level programming languages, which can help understand program anomaly behaviour. Excellent results were demonstrated using a variety of decision engines, evaluating the KDD98 and UNM data sets, and a new, modern data set. The ADFA Linux data set was created as part of this research using a modern operating system and contemporary hacking methods, and is now publicly available. Furthermore, the new semantic method possesses an inherent resilience to mimicry attacks, and demonstrated a high level of portability between different operating system versions.