Biblio

SCADA systems are one of the critical infrastructures and face many security threats. Attackers can control SCADA systems through network attacks, destroying the normal operation of the power system. It is important to conduct a risk assessment of security threats on SCADA systems. However, existing models for risk assessment using attack trees mainly focus on describing possible intrusions rather than the interaction between threats and defenses. In this paper, we comprehensively consider intrusion likelihood and defense capability and propose a quantitative risk assessment model of security threats based on attack countermeasure tree (ACT). Each leaf node in ACT contains two attributes: exploitable vulnerabilities and defense countermeasures. An attack scenario can be constructed by means of traversing the leaf nodes. We set up six indicators to evaluate the impact of security threats in attack scenarios according to NISTIR 7628 standard. Experimental results show the attack probability of security threats and high-risk attack scenarios in SCADA systems. We can improve defense countermeasures to protect against security threats corresponding to high-risk scenarios. In addition, the model can continually update risk assessments based on the implementation of the system’s defensive countermeasures.

Industrial control systems (ICSs) are facing serious security challenges due to their inherent flaws, and emergence of vulnerabilities from the integration with commercial components and networks. To that end, assessing the security plays a vital role for current industrial enterprises which are responsible for critical infrastructure. This paper accomplishes a complex task of quantitative assessment based on attack graphs in order to look forward critical paths. For the purpose of application to a large-scale heterogeneous ICSs, we propose a flexible attack graph generation algorithm is proposed with the help of the graph data model. Hereafter, our quantitative assessment takes a consideration of graph indicators on specific nodes and edges to get the security metrics. In order to improve results of obtaining the critical attack path, we introduced a formulating selection rule, considering the asset value of industrial control devices. The experimental results show validation and verification of the proposed method.

Software development and deployment are generally fast-pacing practices, yet to date there is still a significant amount of legacy software running in various critical industries with years or even decades of lifespans. As the source code of some legacy software became unavailable, it is difficult for maintainers to actively patch the vulnerabilities, leaving the outdated binaries appealing targets of advanced security attacks. One of the most powerful attacks today is code reuse, a technique that can circumvent most existing system-level security facilities. While there have been various countermeasures against code reuse, applying them to sourceless software appears to be exceptionally challenging. Fine-grained code randomization is considered to be an effective strategy to impede modern code-reuse attacks. To apply it to legacy software, a technique called binary rewriting is employed to directly reconstruct binaries without symbol or relocation information. However, we found that current rewriting-based randomization techniques, regardless of their designs and implementations, share a common security defect such that the randomized binaries may remain vulnerable in certain cases. Indeed, our finding does not invalidate fine-grained code randomization as a meaningful defense against code reuse attacks, for it significantly raises the bar for exploits to be successful. Nevertheless, it is critical for the maintainers of legacy software systems to be aware of this problem and obtain a quantitative assessment of the risks in adopting a potentially incomprehensive defense. In this paper, we conducted a systematic investigation into the effectiveness of randomization techniques designed for hardening outdated binaries. We studied various state-of-the-art, fine-grained randomization tools, confirming that all of them can leave a certain part of the retrofitted binary code still reusable. To quantify the risks, we proposed a set of concrete criteria to classify gadgets immune to rewriting-based randomization and investigated their availability and capability.

We introduce Active Dependency Mapping (ADM), a method for establishing dependency relations among a set of interdependent services. The approach is to artificially degrade network performance to infer which assets on the network support a particular process. Artificial degradation of the network environment could be transparent to users; run continuously it could identify dependencies that are rare or occur only at certain timescales. A useful byproduct of this dependency analysis is a quantitative assessment of the resilience and robustness of the system. This technique is intriguing for hardening both enterprise networks and cyber physical systems. We present a proof-of-concept experiment executed on a real-world set of interrelated software services. We assess the efficacy of the approach, discuss current limitations, and suggest options for future development of ADM.