Pull-Type Security Patch Management in Intrusion Tolerant Systems: Modeling and Analysis

In this chapter, we introduce a stochastic framework to evaluate the system availability of an intrusion tolerant system (ITS), where the system undergoes patch management with a periodic vulnerability checking strategy, i.e., pull-type patch management. In particular, a composite stochastic reward net (SRN) is developed to capture the overall system behaviors, including vulnerability discovery, intrusion tolerance, and reactive maintenance operations. Furthermore, two kinds of availability criteria, the interval availability and the steady-state availability of the system, are formulated by applying the phase-type (PH) approximation to solve the Markov regenerative process (MRGP) model derived from the composite SRN. Numerical experiments are conducted to investigate the effects of the vulnerability checking interval on the system availability

Observing the clouds : a survey and taxonomy of cloud monitoring

This research was supported by a Royal Society Industry Fellowship and an Amazon Web Services (AWS) grant. Date of Acceptance: 10/12/2014Monitoring is an important aspect of designing and maintaining large-scale systems. Cloud computing presents a unique set of challenges to monitoring including: on-demand infrastructure, unprecedented scalability, rapid elasticity and performance uncertainty. There are a wide range of monitoring tools originating from cluster and high-performance computing, grid computing and enterprise computing, as well as a series of newer bespoke tools, which have been designed exclusively for cloud monitoring. These tools express a number of common elements and designs, which address the demands of cloud monitoring to various degrees. This paper performs an exhaustive survey of contemporary monitoring tools from which we derive a taxonomy, which examines how effectively existing tools and designs meet the challenges of cloud monitoring. We conclude by examining the socio-technical aspects of monitoring, and investigate the engineering challenges and practices behind implementing monitoring strategies for cloud computing.Publisher PDFPeer reviewe

Design, Implementation and Experiments for Moving Target Defense Framework

The traditional defensive security strategy for distributed systems employs well-established defensive techniques such as; redundancy/replications, firewalls, and encryption to prevent attackers from taking control of the system. However, given sufficient time and resources, all these methods can be defeated, especially when dealing with sophisticated attacks from advanced adversaries that leverage zero-day exploits

Development of a Cyber Range with description language for network topology definition

Cyber Ranges are an essential tool for cybersecurity trainings and experiments because they enable to setup virtual, isolated and reproducible environments that can be safely used to execute different types of tests and scenarios. The preparation of scenarios is the most time-consuming phase, which includes the configuration of the virtual machines and the definition of the network topology, so it is important for a Cyber Range to include tools that simplify this operation. This work focuses on how to implement and setup a Cyber Range that includes the necessary features and tools to simplify the setup phase, in particular for large topologies. The literature review provides an analysis of the selected open-source and research solutions currently available for Cyber Ranges and their configuration for use in different scenarios. This work presents the development of a Cyber Range based on the open-source framework OpenStack and the entire design process of a new Description Language, starting from the analysis of the requirements for the defined use-cases, defining and designing the required features, the implementation of all the required components, and the testing of the correctness and effectiveness of the whole system. A comparison of the implemented solution against the selected solutions in the literature study is provided, summarising the unique features offered by this approach. The validation of the Description Language implementation with the defined use cases demonstrated that it can reduce the complexity and length of the required template, which can help to make the setup of scenarios faster

Unknown Threat Detection With Honeypot Ensemble Analsyis Using Big Datasecurity Architecture

The amount of data that is being generated continues to rapidly grow in size and complexity. Frameworks such as Apache Hadoop and Apache Spark are evolving at a rapid rate as organizations are building data driven applications to gain competitive advantages. Data analytics frameworks decomposes our problems to build applications that are more than just inference and can help make predictions as well as prescriptions to problems in real time instead of batch processes. Information Security is becoming more important to organizations as the Internet and cloud technologies become more integrated with their internal processes. The number of attacks and attack vectors has been increasing steadily over the years. Border defense measures (e.g. Intrusion Detection Systems) are no longer enough to identify and stop attackers. Data driven information security is not a new approach to solving information security; however there is an increased emphasis on combining heterogeneous sources to gain a broader view of the problem instead of isolated systems. Stitching together multiple alerts into a cohesive system can increase the number of True Positives. With the increased concern of unknown insider threats and zero-day attacks, identifying unknown attack vectors becomes more difficult. Previous research has shown that with as little as 10 commands it is possible to identify a masquerade attack against a user\u27s profile. This thesis is going to look at a data driven information security architecture that relies on both behavioral analysis of SSH profiles and bad actor data collected from an SSH honeypot to identify bad actor attack vectors. Honeypots should collect only data from bad actors; therefore have a high True Positive rate. Using Apache Spark and Apache Hadoop we can create a real time data driven architecture that can collect and analyze new bad actor behaviors from honeypot data and monitor legitimate user accounts to create predictive and prescriptive models. Previously unidentified attack vectors can be cataloged for review

Automating Cyber Analytics

Model based security metrics are a growing area of cyber security research concerned with measuring the risk exposure of an information system. These metrics are typically studied in isolation, with the formulation of the test itself being the primary finding in publications. As a result, there is a flood of metric specifications available in the literature but a corresponding dearth of analyses verifying results for a given metric calculation under different conditions or comparing the efficacy of one measurement technique over another. The motivation of this thesis is to create a systematic methodology for model based security metric development, analysis, integration, and validation. In doing so we hope to fill a critical gap in the way we view and improve a system’s security. In order to understand the security posture of a system before it is rolled out and as it evolves, we present in this dissertation an end to end solution for the automated measurement of security metrics needed to identify risk early and accurately. To our knowledge this is a novel capability in design time security analysis which provides the foundation for ongoing research into predictive cyber security analytics. Modern development environments contain a wealth of information in infrastructure-as-code repositories, continuous build systems, and container descriptions that could inform security models, but risk evaluation based on these sources is ad-hoc at best, and often simply left until deployment. Our goal in this work is to lay the groundwork for security measurement to be a practical part of the system design, development, and integration lifecycle. In this thesis we provide a framework for the systematic validation of the existing security metrics body of knowledge. In doing so we endeavour not only to survey the current state of the art, but to create a common platform for future research in the area to be conducted. We then demonstrate the utility of our framework through the evaluation of leading security metrics against a reference set of system models we have created. We investigate how to calibrate security metrics for different use cases and establish a new methodology for security metric benchmarking. We further explore the research avenues unlocked by automation through our concept of an API driven S-MaaS (Security Metrics-as-a-Service) offering. We review our design considerations in packaging security metrics for programmatic access, and discuss how various client access-patterns are anticipated in our implementation strategy. Using existing metric processing pipelines as reference, we show how the simple, modular interfaces in S-MaaS support dynamic composition and orchestration. Next we review aspects of our framework which can benefit from optimization and further automation through machine learning. First we create a dataset of network models labeled with the corresponding security metrics. By training classifiers to predict security values based only on network inputs, we can avoid the computationally expensive attack graph generation steps. We use our findings from this simple experiment to motivate our current lines of research into supervised and unsupervised techniques such as network embeddings, interaction rule synthesis, and reinforcement learning environments. Finally, we examine the results of our case studies. We summarize our security analysis of a large scale network migration, and list the friction points along the way which are remediated by this work. We relate how our research for a large-scale performance benchmarking project has influenced our vision for the future of security metrics collection and analysis through dev-ops automation. We then describe how we applied our framework to measure the incremental security impact of running a distributed stream processing system inside a hardware trusted execution environment

Configuration Management of Distributed Systems over Unreliable and Hostile Networks

Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems. This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration. Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture. The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn. Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts

Détecter et survivre aux intrusions : exploration de nouvelles approches de détection, de restauration, et de réponse aux intrusions

Computing platforms, such as embedded systems or laptops, are built with layers of preventive security mechanisms to reduce the likelihood of attackers successfully compromising them. Nevertheless, given time and despite decades of improvements in preventive security, intrusions still happen. Therefore, systems should expect intrusions to occur, thus they should be built to detect and to survive them. Commodity Operating Systems (OSs) are deployed with intrusion detection solutions, but their ability to survive them is limited. State-of-the-art approaches from industry or academia either involve manual procedures, loss of availability, coarse-grained responses, or non-negligible performance overhead. Moreover, low-level components, such as the BIOS, are increasingly targeted by sophisticated attackers to implant stealthy and resilient malware. State-of-the-art solutions, however, mainly focus on boot time integrity, leaving the runtime part of the BIOS—known as the System Management Mode (SMM)—a prime target. This dissertation shows that we can build platforms that detect intrusions at the BIOS level and survive intrusions at the OS level. First, by demonstrating that intrusion survivability is a viable approach for commodity OSs. We develop a new approach that address various limitations from the literature, and we evaluate its security and performance. Second, by developing a hardware-based approach that detects attacks at the BIOS level where we demonstrate its feasibility with multiple detection methods.Les systèmes informatiques, tels que les ordinateurs portables ou les systèmes embarqués, sont construits avec des couches de mécanismes de sécurité préventifs afin de réduire la probabilité qu'un attaquant les compromettent. Néanmoins, malgré des décennies d'avancées dans ce domaine, des intrusions surviennent toujours. Par conséquent, nous devons supposer que des intrusions auront lieu et nous devons construire nos systèmes afin qu'ils puissent les détecter et y survivre. Les systèmes d'exploitation généralistes sont déployés avec des mécanismes de détection d'intrusion, mais leur capacité à survivre à une intrusion est limitée. Les solutions de l'état de l'art nécessitent des procédures manuelles, comportent des pertes de disponibilité, ou font subir un fort coût en performance. De plus, les composants de bas niveau tels que le BIOS sont de plus en plus la cible d'attaquants cherchant à implanter des logiciels malveillants, furtifs, et résilients. Bien que des solutions de l'état de l'art garantissent l'intégrité de ces composants au démarrage, peu s'intéressent à la sécurité des services fournis par le BIOS qui sont exécutés au sein du System Management Mode (SMM). Ce manuscrit montre que nous pouvons construire des systèmes capables de détecter des intrusions au niveau du BIOS et y survivre au niveau du système d'exploitation. Tout d'abord, nous démontrons qu'une approche de survivabilité aux intrusions est viable et praticable pour des systèmes d'exploitation généralistes. Ensuite, nous démontrons qu'il est possible de détecter des intrusions au niveau du BIOS avec une solution basée sur du matériel

TAXONOMY OF SECURITY AND PRIVACY ISSUES IN SERVERLESS COMPUTING

The advent of cloud computing has led to a new era of computer usage. Networking and physical security are some of the IT infrastructure concerns that IT administrators around the world had to worry about for their individual environments. Cloud computing took away that burden and redefined the meaning of IT administrators. Serverless computing as it relates to secure software development is creating the same kind of change. Developers can quickly spin up a secure development environment in a matter of minutes without having to worry about any of the underlying infrastructure setups. In the paper, we will look at the merits and demerits of serverless computing, what is drawing the demand for serverless computing among developers, the security and privacy issues of serverless technology, and detail the parameters to consider when setting up and using a secure development environment based on serverless computin