Hierarchical Attack Representation Model Research Articles

Moving-Target Defense in Depth: Pervasive Self- and Situation-Aware VM Mobilization across Federated Clouds in Presence of Active Attacks

Federated clouds are interconnected cooperative cloud infrastructures offering vast hosting capabilities, smooth workload migration and enhanced reliability. However, recent devastating attacks on such clouds have shown that such features come with serious security challenges. The oblivious heterogeneous construction, management, and policies employed in federated clouds open the door for attackers to induce conflicts to facilitate pervasive coordinated attacks. In this paper, we present a novel proactive defense that aims to increase attacker uncertainty and complicate target tracking, a critical step for successful coordinated attacks. The presented systemic approach acts as a VM management platform with an intrinsic multidimensional hierarchical attack representation model (HARM) guiding a dynamic, self and situation-aware VM live-migration for moving-target defense (MtD). The proposed system managed to achieve the proposed goals in a resource-, energy-, and cost-efficient manner.

HARMer: Cyber-Attacks Automation and Evaluation

With the increasing growth of cyber-attack incidences, it is important to develop innovative and effective techniques to assess and defend networked systems against cyber attacks. One of the well-known techniques for this is performing penetration testing which is carried by a group of security professionals (i.e, red team). Penetration testing is also known to be effective to find existing and new vulnerabilities, however, the quality of security assessment can be depending on the quality of the red team members and their time and devotion to the penetration testing. In this paper, we propose a novel automation framework for cyber-attacks generation named `HARMer' to address the challenges with respect to manual attack execution by the red team. Our novel proposed framework, design, and implementation is based on a scalable graphical security model called Hierarchical Attack Representation Model (HARM). (1) We propose the requirements and the key phases for the automation framework. (2) We propose security metrics-based attack planning strategies along with their algorithms. (3) We conduct experiments in a real enterprise network and Amazon Web Services. The results show how the different phases of the framework interact to model the attackers' operations. This framework will allow security administrators to automatically assess the impact of various threats and attacks in an automated manner.

Security modelling and assessment of modern networks using time independent Graphical Security Models

Graphical Security Models (GSMs), such as an Attack Graph, are used to assess the security of networks, but they are often limited to assess the security of the given network state (i.e., a snapshot at the current time). To address this issue, we develop a GSM named Time-independent Hierarchical Attack Representation Model (TI-HARM), which analyses the security of multiple network states combined taking into account the time duration of each network state and the visibility of the network components (e.g., hosts and edges). Also, we develop a new security rating system for dynamic networks to evaluate the changing security posture. Lastly, we present an approach that utilises the functionalities of the TI-HARM to compute global optimal defence solutions for dynamic networks. Our experimental results show that the TI-HARM can model and analyse the security of multiple states of dynamic networks, which the existing GSMs mostly assumed that it is static. Also, we found that the TI-HARM can be used to effectively compute the global optimal security solutions compared to existing models that only focus on local optimal solutions. Therefore, our proposed approach could be used to aid security administrators to understand the security posture of dynamic networks better and be able to enhance the security taking into account multiple changes in dynamic networks.

A Systematic Approach to Threat Modeling and Security Analysis for Software Defined Networking

Software Defined Networking (SDN) extends capabilities of existing networks by providing various functionalities, such as flexible networking controls. However, there are many security threat vectors in SDN, including existing and emerging ones arising from new functionalities, that may hinder the use of SDN. To tackle this problem, many countermeasures have been developed to mitigate various threats faced in SDN. However, their effectiveness must be analyzed and compared to fully understand how security posture of SDN changes when the countermeasure is adopted. Also, it becomes difficult to optimize the security of SDN without using a systematic approach to evaluate the security posture of SDN. In this paper, we propose a novel framework to systematically model and analyze the security posture of SDN. We develop a novel graphical security model formalism named Threat Vector Hierarchical Attack Representation Model (TV-HARM), which provides a systematic approach to evaluate threats, attacks and countermeasures for SDN. The TV-HARM captures different threats and their combinations, enabling security risk assessment of SDN. In addition, we define three new security metrics to represent security of SDN. Our experimental results showed that the proposed security assessment framework can capture and evaluate various security threats to SDN, demonstrating the applicability and feasibility of the proposed framework.

A systematic evaluation of cybersecurity metrics for dynamic networks

It is difficult to assess the security of modern networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) are widely used to systematically analyse the security posture of network systems using security metrics. However, there are problems using them to assess the security of dynamic networks. First, most models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for the analysis of dynamic networks and hence their effectiveness to the dynamic changes in the network still remains unclear.In this paper, we systematically categorise network changes into two categories (i.e., changes in hosts and changes in edges). We conduct a comprehensive analysis to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model, which can capture and analyse the changes in the security of network systems. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks.Our simulation results show that different security metrics (except the shortest attack path) have varying security posture changes with respect to changes in the network (when we introduce time to them). However, none of the security metrics consistently changes for all the network changes that we observe in our scenarios. Hence, the results provide some insights into what security metrics can change (accordingly) when a particular network change is observed. It also provides a foundation for further research in this area.

A Survey of Security Models Using Effective Moving Target Defenses

This article describes how nowadays, attackers are targeting valuable assets and infrastructures in networked systems causing an impact on enterprises and individuals. By implementing moving target defenses helps to prevent cyber-attacks by changing the attack surface. Some security models like Attack Graph (A.G) and Attack Tree (A.T) provide a formal method to access and compare the effectiveness of them. So, in this article, the authors incorporate moving target defenses in a security model, using a Hierarchical Attack Representation Model (HARM), to compare and access the effectiveness of the security. In addition, the authors are also taking important measures (IMs) for implementing MTD techniques to enhance the scalability of the network. Finally, they compare the scalability of an attack graph and HARM models by implementing MTD techniques to find the effectiveness of security in network.

Composite Metrics for Network Security Analysis

Security metrics present the security level of a system or a network in both qualitative and quantitative ways. In general, security metrics are used to assess the security level of a system and to achieve security goals. There are a lot of security metrics for security analysis, but there is no systematic classification of security metrics that are based on network reachability information. To address this, we propose a systematic classification of existing security metrics based on network reachability information. Mainly, we classify the security metrics into host-based and network-based metrics. The host-based metrics are classified into metrics ``without probability" and "with probability", while the network-based metrics are classified into "path-based" and "non-path based". Finally, we present and describe an approach to develop composite security metrics and it's calculations using a Hierarchical Attack Representation Model (HARM) via an example network. Our novel classification of security metrics provides a new methodology to assess the security of a system.

Towards scalable security analysis using multi-layered security models

Security models, such as an attack graph (AG), are widely adopted to assess the security of networked systems, such as utilizing various security metrics and providing a cost-effective network hardening solution. There are various methods of generating these models, but the scalability problem exists for single-layered graph-based security models when analyzing all possible attack paths. To address this problem, we propose to use a multi-layer hierarchical attack representation model (HARM) that models various components in the networked system in different layers to reduce the computational complexity. First, we formulate key questions that need to be answered to assess the scalability of security models. Second, we formally define the multi-layer HARM. Last, we conduct experiments to show the scalability of security models. Our experimental results show that using the HARM can improve the scalability of assessing the security of the networked system significantly in comparison to the single-layered security models in various network scenarios.

Assessing the Effectiveness of Moving Target Defenses Using Security Models

Cyber crime is a developing concern, where criminals are targeting valuable assets and critical infrastructures within networked systems, causing a severe socio-economic impact on enterprises and individuals. Adopting moving target defense (MTD) helps thwart cyber attacks by continuously changing the attack surface. There are numerous MTD techniques proposed in various domains (e.g., virtualized network, wireless sensor network), but there is still a lack of methods to assess and compare the effectiveness of them. Security models, such as an attack graph (AG), provide a formal method of analyzing the security, but incorporating MTD techniques in those security models has not been studied. In this paper, we incorporate MTD techniques into a security model, namely a hierarchical attack representation model (HARM), to assess the effectiveness of them. In addition, we use importance measures (IMs) for deploying MTD techniques to enhance the scalability. Finally, we compare the scalability of AG and HARM when deploying MTD techniques, as well as changes in performance and security in our experiments.