arXiv subjects

Benjamin Green

Publications and source records attributed to Benjamin Green.

4 recordsLinked to original sources

arXiv2023

Dead Man's PLC: Towards Viable Cyber Extortion for Operational Technology

For decades, operational technology (OT) has enjoyed the luxury of being suitably inaccessible so as to experience directly targeted cyber attacks from only the most advanced and well-resourced adversaries. However, security via obscurity cannot last forever, and indeed a shift is happening whereby less advanced adversaries are showing an appetite for targeting OT. With this shift in adversary demographics, there will likely also be a shift in attack goals, from clandestine process degradation and espionage to overt cyber extortion (Cy-X). The consensus from OT cyber security practitioners suggests that, even if encryption-based Cy-X techniques were launched against OT assets, typical recovery practices designed for engineering processes would provide adequate resilience. In response, this paper introduces Dead Man's PLC (DM-PLC), a pragmatic step towards viable OT Cy-X that acknowledges and weaponises the resilience processes typically encountered. Using only existing functionality, DM-PLC considers an entire environment as the entity under ransom, whereby all assets constantly poll one another to ensure the attack remains untampered, treating any deviations as a detonation trigger akin to a Dead Man's switch. A proof of concept of DM-PLC is implemented and evaluated on an academically peer reviewed and industry validated OT testbed to demonstrate its malicious efficacy.

cs.CR↗

arXiv2022

Walking Under the Ladder Logic: PLC-VBS, a PLC Control Logic Vulnerability Discovery Tool

Cyber security risk assessments provide a pivotal starting point towards the understanding of existing risk exposure, through which suitable mitigation strategies can be formed. Where risk is viewed as a product of threat, vulnerability, and impact, understanding each element is of equal importance. This can be a challenge in Industrial Control System (ICS) environments, where adopted technologies are typically not only bespoke, but interact directly with the physical world. To date, existing vulnerability identification has focused on traditional vulnerability categories. While this provides risk assessors with a baseline understanding, and the ability to hypothesize on potential resulting impacts, it is high level, operating at a level of abstraction that would be viewed as incomplete within a traditional information system context. The work presented in this paper takes the understanding of ICS device vulnerabilities one step further. It offers a tool, PLC-VBS, that helps identify Programmable Logic Controller (PLC) vulnerabilities, specifically within logic used to monitor, control, and automate operational processes. PLC-VBS gives risk assessors a more coherent picture about the potential impact should the identified vulnerabilities be exploited; this applies specifically to operational process elements.

cs.CR↗

arXiv2021

A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation

The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These networks usually require large numbers of annotated data to train vast parameters. However, it takes significant effort to annotate a large histopathology dataset. We introduce a light-weight and interpretable model for nuclei detection and weakly-supervised segmentation. It only requires annotations on isolated nucleus, rather than on all nuclei in the dataset. Besides, it is a generative compositional model that first locates parts of nucleus, then learns the spatial correlation of the parts to further locate the nucleus. This process brings interpretability in its prediction. Empirical results on an in-house dataset show that in detection, the proposed method achieved comparable or better performance than its deep network counterparts, especially when the annotated data is limited. It also outperforms popular weakly-supervised segmentation methods. The proposed method could be an alternative solution for the data-hungry problem of deep learning methods.

cs.CV↗

arXiv2019

Design Considerations for Building Credible Security Testbeds: A Systematic Study of Industrial Control System Use Cases

This paper presents a mapping framework for design factors and implementation process for building credible Industrial Control Systems (ICS) security testbeds. The resilience of ICSs has become a critical concern to operators and governments following widely publicised cyber security events. The inability to apply conventional Information Technology security practice to ICSs further compounds challenges in adequately securing critical systems. To overcome these challenges, and do so without impacting live environments, testbeds for the exploration, development and evaluation of security controls are widely used. However, how a testbed is designed and its attributes, can directly impact not only its viability but also its credibility as a whole. Through a combined systematic and thematic analysis and mapping of ICS security testbed design attributes, this paper suggests that the expertise of human experimenters, design objectives, the implementation approach, architectural coverage, core characteristics, and evaluation methods; are considerations that can help establish or enhance confidence, trustworthiness and acceptance; thus, credibility of ICS security testbeds.

cs.CR↗