Mitigating Insider Threat Risks in Cyber-physical Manufacturing Systems

Cyber-Physical Manufacturing System (CPMS)—a next generation manufacturing system—seamlessly integrates digital and physical domains via the internet or computer networks. It will enable drastic improvements in production flexibility, capacity, and cost-efficiency. However, enlarged connectivity and accessibility from the integration can yield unintended security concerns. The major concern arises from cyber-physical attacks, which can cause damages to the physical domain while attacks originate in the digital domain. Especially, such attacks can be performed by insiders easily but in a more critical manner: Insider Threats. Insiders can be defined as anyone who is or has been affiliated with a system. Insiders have knowledge and access authentications of the system\u27s properties, therefore, can perform more serious attacks than outsiders. Furthermore, it is hard to detect or prevent insider threats in CPMS in a timely manner, since they can easily bypass or incapacitate general defensive mechanisms of the system by exploiting their physical access, security clearance, and knowledge of the system vulnerabilities. This thesis seeks to address the above issues by developing an insider threat tolerant CPMS, enhanced by a service-oriented blockchain augmentation and conducting experiments & analysis. The aim of the research is to identify insider threat vulnerabilities and improve the security of CPMS. Blockchain\u27s unique distributed system approach is adopted to mitigate the insider threat risks in CPMS. However, the blockchain limits the system performance due to the arbitrary block generation time and block occurrence frequency. The service-oriented blockchain augmentation is providing physical and digital entities with the blockchain communication protocol through a service layer. In this way, multiple entities are integrated by the service layer, which enables the services with less arbitrary delays while retaining their strong security from the blockchain. Also, multiple independent service applications in the service layer can ensure the flexibility and productivity of the CPMS. To study the effectiveness of the blockchain augmentation against insider threats, two example models of the proposed system have been developed: Layer Image Auditing System (LIAS) and Secure Programmable Logic Controller (SPLC). Also, four case studies are designed and presented based on the two models and evaluated by an Insider Attack Scenario Assessment Framework. The framework investigates the system\u27s security vulnerabilities and practically evaluates the insider attack scenarios. The research contributes to the understanding of insider threats and blockchain implementations in CPMS by addressing key issues that have been identified in the literature. The issues are addressed by EBIS (Establish, Build, Identify, Simulation) validation process with numerical experiments and the results, which are in turn used towards mitigating insider threat risks in CPMS

Infinitely many 4d N=1 SCFTs with a=c

We study a rich set of four-dimensional $\mathcal{N}=1$ superconformal field theories (SCFTs) with both central charges identical: $a = c$. We construct them via the diagonal $\mathcal{N}=1$ gauging of the flavor symmetry $G$ of a collection of $\mathcal{N}=2$ Argyres--Douglas theories of type $\mathcal{D}_p(G)$, with or without additional adjoint chiral multiplets. In this way, we construct infinitely-many theories that flow to interacting SCFTs with $a = c$ in the infrared. Finally, we briefly highlight the features of the SCFTs without $a = c$ that arise from generalizing this construction.Comment: 43 pages+references, 11 figures, 5 table

KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services

With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident. The anonymous nature of online services often leads to the presence of biased and harmful language, posing challenges to maintaining the health of online communities. This phenomenon is especially relevant in South Korea, where large-scale hate speech detection algorithms have not yet been broadly explored. In this paper, we introduce "KoMultiText", a new comprehensive, large-scale dataset collected from a well-known South Korean SNS platform. Our proposed dataset provides annotations including (1) Preferences, (2) Profanities, and (3) Nine types of Bias for the text samples, enabling multi-task learning for simultaneous classification of user-generated texts. Leveraging state-of-the-art BERT-based language models, our approach surpasses human-level accuracy across diverse classification tasks, as measured by various metrics. Beyond academic contributions, our work can provide practical solutions for real-world hate speech and bias mitigation, contributing directly to the improvement of online community health. Our work provides a robust foundation for future research aiming to improve the quality of online discourse and foster societal well-being. All source codes and datasets are publicly accessible at https://github.com/Dasol-Choi/KoMultiText.Comment: Accepted to the NeurIPS 2023 Workshop on Socially Responsible Language Modelling Research (SoLaR

Time Is MattEr: Temporal Self-supervision for Video Transformers

Understanding temporal dynamics of video is an essential aspect of learning better video representations. Recently, transformer-based architectural designs have been extensively explored for video tasks due to their capability to capture long-term dependency of input sequences. However, we found that these Video Transformers are still biased to learn spatial dynamics rather than temporal ones, and debiasing the spurious correlation is critical for their performance. Based on the observations, we design simple yet effective self-supervised tasks for video models to learn temporal dynamics better. Specifically, for debiasing the spatial bias, our method learns the temporal order of video frames as extra self-supervision and enforces the randomly shuffled frames to have low-confidence outputs. Also, our method learns the temporal flow direction of video tokens among consecutive frames for enhancing the correlation toward temporal dynamics. Under various video action recognition tasks, we demonstrate the effectiveness of our method and its compatibility with state-of-the-art Video Transformers.Comment: Accepted to ICML 2022. Code is available at https://github.com/alinlab/temporal-selfsupervisio

Induction FOLFIRINOX followed by stereotactic body radiation therapy in locally advanced pancreatic cancer

IntroductionFOLFIRINOX (the combination of 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin) is the preferred systemic regimen for locally advanced pancreatic cancer (LAPC). Furthermore, stereotactic body radiation therapy (SBRT) is a promising treatment option for achieving local control in these patients. However, clinical outcomes in patients with LAPC treated using FOLFIRINOX followed by SBRT have not been clarified. Therefore, we aimed to evaluate clinical outcomes of induction FOLFIRINOX treatment followed by SBRT in patients with LAPC.MethodsTo this end, we retrospectively reviewed the medical records of patients with LAPC treated with induction FOLFIRINOX followed by SBRT in a single tertiary hospital. We evaluated overall survival (OS), progression-free survival (PFS), resection rate, SBRT-related adverse events, and prognostic factors affecting survival.ResultsFifty patients were treated with induction FOLFIRINOX for a median of 8 cycles (range: 3–28), which was followed by SBRT. The median OS and PFS were 26.4 (95% confidence interval [CI]: 22.4–30.3) and 16.7 months (95% CI: 13.0–20.3), respectively. Nine patients underwent conversion surgery (eight achieved R0) and showed better OS than those who did not (not reached vs. 24.1 months, p = 0.022). During a follow-up period of 23.6 months, three cases of grade 3 gastrointestinal bleeding at the pseudoaneurysm site were noted, which were managed successfully. Analysis of the factors affecting clinical outcomes revealed that a high radiation dose (≥ 35 Gy) resulted in a higher rate of conversion surgery (25% [8/32] vs. 5.6% [1/18], respectively) and was an independent favorable prognostic factor for OS in the adjusted analysis (hazard ratio: 2.024, 95% CI: 1.042–3.930, p = 0.037).ConclusionOur findings suggest that induction FOLFIRINOX followed by SBRT in patients with LAPC results in better survival with manageable toxicities. A high total SBRT dose was associated with a high rate of conversion surgery and could afford better survival