Potential Vulnerabilities Research Articles

Abstract ED02-03: Innovative approaches at the diagnosis and treatment of invasive lobular carcinoma

Abstract Invasive Lobular Carcinoma (ILC) represents a unique subtype of breast cancer characterized by distinctive clinical and molecular features. This presentation aims to delve into innovative approaches in the diagnosis and treatment of ILC, focusing on novel techniques in imaging, unique molecular findings, signaling pathway alterations, targetable biomarkers, and microenvironment profiling that can pave the way for the development of novel therapies. Imaging Techniques in ILC: Accurate diagnosis is the foundation for effective treatment. ILC often presents diagnostic challenges due to the fact that lobular cells tends to infiltrate the affected organs in a diffuse manner instead of forming a discrete tumor mass making diagnostic imaging such as screening mammograms and CT scans less precise. This presentation will explore cutting-edge imaging techniques such as contrast-enhanced mammography, novel radioisotopes for positron emission tomography (PET) such as Fluoroestradiol F18 (FES), Fluciclovine and prostate-specific membrane antigen (PSMA) for improved detection and characterization of ILC lesions. Furthermore, we will discuss emerging modalities like radiomics and artificial intelligence, which hold promise in enhancing early diagnosis and risk assessment. Molecular Insights into ILC: ILC is distinguished by its unique molecular profile, notably the loss of E-cadherin expression. We will delve into recent advancements in understanding the molecular landscape of ILC, including genomic alterations, epigenetic changes, and the role of signaling pathways providing insights into potential therapeutic vulnerabilities. Targetable Biomarkers and Pathways: Identification of targetable biomarkers is essential for the development of precision therapies in ILC. This presentation will highlight promising targets, including ERBB2 (HER2), ROS1, TROP2, AKT1 along with other novel biomarkers. Microenvironment Profiling: The tumor microenvironment plays a pivotal role in cancer progression and therapy response. We will discuss recent advancements in characterizing the ILC microenvironment, including the immune cell infiltrate and stromal components. Understanding the dynamic interplay between the tumor and its microenvironment can inform the development of immunotherapies and combination strategies tailored to ILC. In conclusion, this presentation will provide a comprehensive overview of the latest innovations in ILC diagnosis and treatment. Our exploration of advanced imaging techniques, molecular findings, targetable pathways, biomarkers, and microenvironment profiling will shed light on the exciting potential for personalized therapeutic approaches in the management of ILC. By staying at the forefront of these developments, we can pave the way for improved outcomes and enhanced quality of life for patients with ILC. Citation Format: J. Mouabbi. Innovative approaches at the diagnosis and treatment of invasive lobular carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr ED02-03.

Salmon shark seasonal site fidelity demonstrates the influence of scale on identifying potential high-use areas and vulnerabilities

Considering habitat use throughout the whole range of a highly mobile marine species is necessary to understand life history, identify vulnerabilities, and inform effective management. We used satellite tagging data from 128 adult female salmon sharks Lamna ditropis to identify seasonal hotspots of activity in an extended California Current region (ECCR; encompassing the California Current Large Marine Ecosystem), an area far away from their well-described primary habitat in the Alaska Downwelling Region where they have been documented, but whose utility has been poorly understood. Tag track durations had a mean of 447.7 ± 381 d, and 88 sharks (68.8%) visited the ECCR, comprising 33.6% of 28019 total daily Argos detections. Tracking data revealed that the timing and duration of migrations to the ECCR varied, but salmon shark distribution within the ECCR displayed consistent latitudinal shifts in accordance with regional oceanographic seasons. High site fidelity across multi-year tracks to high-productivity features, such as sea banks, and previously published knowledge of salmon shark life history suggest that the ECCR provides important foraging habitat which may be linked to reproductive success. The data reveal high overlap of salmon shark distribution with cumulative fishing effort collected by Global Fishing Watch for 2012-2019, particularly around seasonal hotspots, suggesting that female salmon sharks might be at risk of fisheries encounters. Collectively, our findings emphasize the importance of the ECCR in salmon shark life history and demonstrate the influence of spatial and temporal scale on interpretation of large movement data sets and identification of critical habitat outside of well-studied regions.

A Pilot Study of Observation Poisoning on Selective Reincarnation in Multi-Agent Reinforcement Learning

This research explores the vulnerability of selective reincarnation, a concept in Multi-Agent Reinforcement Learning (MARL), in response to observation poisoning attacks. Observation poisoning is an adversarial strategy that subtly manipulates an agent’s observation space, potentially leading to a misdirection in its learning process. The primary aim of this paper is to systematically evaluate the robustness of selective reincarnation in MARL systems against the subtle yet potentially debilitating effects of observation poisoning attacks. Through assessing how manipulated observation data influences MARL agents, we seek to highlight potential vulnerabilities and inform the development of more resilient MARL systems. Our experimental testbed was the widely used HalfCheetah environment, utilizing the Independent Deep Deterministic Policy Gradient algorithm within a cooperative MARL setting. We introduced a series of triggers, namely Gaussian noise addition, observation reversal, random shuffling, and scaling, into the teacher dataset of the MARL system provided to the reincarnating agents of HalfCheetah. Here, the “teacher dataset” refers to the stored experiences from previous training sessions used to accelerate the learning of reincarnating agents in MARL. This approach enabled the observation of these triggers’ significant impact on reincarnation decisions. Specifically, the reversal technique showed the most pronounced negative effect for maximum returns, with an average decrease of 38.08% in Kendall’s tau values across all the agent combinations. With random shuffling, Kendall’s tau values decreased by 17.66%. On the other hand, noise addition and scaling aligned with the original ranking by only 21.42% and 32.66%, respectively. The results, quantified by Kendall’s tau metric, indicate the fragility of the selective reincarnation process under adversarial observation poisoning. Our findings also reveal that vulnerability to observation poisoning varies significantly among different agent combinations, with some exhibiting markedly higher susceptibility than others. This investigation elucidates our understanding of selective reincarnation’s robustness against observation poisoning attacks, which is crucial for developing more secure MARL systems and also for making informed decisions about agent reincarnation.

Formal Analysis and Detection for ROS2 Communication Security Vulnerability

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 (Robot Operating System 2) can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this study, we adopted a formal approach to analyze the security of the communication mechanism of ROS2. First, we used a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduced a CIA model based on the established vulnerability model and used linear temporal logic to define its security properties. Then, we designed and implemented a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally tested some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

Examining the Benefits and Drawbacks of Open Network Architectures in E-Commerce

The advent of digital commerce has revolutionized the way businesses operate and interact with customers. One of the key factors contributing to this transformation is the emergence of open network architectures, which facilitate seamless communication and data exchange between various entities within the e-commerce ecosystem. This research aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of digital commerce. And to investigate how open network architectures promote accessibility and interoperability among diverse systems, enabling seamless integration and collaboration within the e-commerce landscape. Explore the potential of open networks to foster innovation and scalability, empowering businesses to rapidly adapt to changing market demands and technological advancements. Evaluate the economic advantages of open network architectures, including reduced infrastructure costs, improved resource utilization, and streamlined operational processes. Examine the potential vulnerabilities and risks associated with open network architectures, such as data breaches, cyber threats, and privacy issues, and propose strategies to mitigate these challenges. Investigate the role of governance frameworks and industry standards in facilitating the effective implementation and management of open network architectures within the e-commerce domain. Identify and analyze the potential barriers to the widespread adoption for overcoming these challenges. This paper aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of e-commerce. Through real-world case studies, theoretical frameworks, and a multi-faceted approach encompassing technical, business, and strategic perspectives, we strive to equip e-commerce stakeholders with the insights necessary to navigate this complex landscape and make informed decisions that align with their organizational goals and market dynamics.

Potential vulnerabilities associated with emerging technologies: insights from a systematic literature review

Potential vulnerabilities associated with emerging technologies: insights from a systematic literature review

Relay network-based cross-chain data interaction protocol with integrity audit

Cross-chain interaction plays a crucial role in enhancing asset circulation and data sharing across diverse blockchain systems. However, achieving secure and stable cross-chain data transactions remains a significant challenge. Traditional frameworks often prioritize efficiency, leading to potential security vulnerabilities, such as single points of failure and excessive computational burdens. These issues compromise the reliability of cross-chain interactions, necessitating the development of more robust methodologies to ensure secure data communication across consortium blockchains. To address these concerns, this paper proposes a novel cross-chain data interaction protocol based on a relay network, incorporating auditing and result arbitration mechanisms using short signatures from bilinear pairings. The protocol employs a trusted execution environment (TEE) within the relay network to execute service nodes that record transaction states. This approach not only reduces the computational load on the chain but also facilitates effective coordination for cross-chain consensus. Moreover, we present a cross-chain asynchronous consensus mechanism, named d-PBFT, to improve the flexibility of the cross-chain network. Security analysis and experimental results show that the protocol enhances the efficiency and ensures the security of cross-chain interaction, while reducing on-chain storage consumption. Thus, the protocol has a high level of security and feasibility for cross-chain data interaction among consortium blockchains. © 2014 xxxxxxxx. Hosting by Elsevier B.V. All rights reserved.

Identification & Evaluation of Distribution Risk in Food Supply Chain in Zomato

The rapid growth of the food delivery industry has transformed the way consumers access food, with platforms like Zomato playing a pivotal role in facilitating this change. However, this growth has also brought forth various challenges, particularly in ensuring the efficiency and safety of the food supply chain. Distribution risks, encompassing factors such as delays, quality issues, and disruptions, pose significant threats to the smooth flow of food from suppliers to consumers. This research project aims to identify and evaluate distribution risks in the food supply chain of Zomato, shedding light on potential vulnerabilities and proposing strategies for mitigation.

Disaster recovery strategies for cyber-physical systems considering the Interdependence

Improving the resilience of Cyber-Physical Systems (CPS) against extreme events is vital. Current research, overly focused on power system restoration, often misses the complex interplay between the physical and cyber aspects of these systems, leading to potential vulnerabilities. This paper first proposes a framework for CPS considering interdependence. The framework accounts for cyber-physical collaborative recovery and the connectivity between cyber nodes and control center in the cyber system. The model is built considering factors such as repair crew, repair time, and network reconstruction, aimed at rapidly restoring the structural and functional resilience of CPS in the shortest possible time. Next, it integrates complex network theory to propose different repair strategies based on structural and functional indicators. Finally, the repair efficiency of the proposed strategies is comprehensively evaluated using structural and functional metrics. Simulation results show that the repair efficiency of CPS is superior to traditional power grids, and moderate reduction in the repair time of cyber nodes and a decrease in network size both contribute to enhancing repair efficiency.

Risk management strategies for smart hotel supply chain

<p>In the dynamic and interconnected world of hospitality, smart hotel supply chains play a pivotal role in maintaining operational efficiency and service excellence. However, these supply chains are susceptible to many risks, including transportation disruptions, supplier insolvencies, and cybersecurity threats, which can severely impact business continuity and degrade service quality. This conceptual paper proposes a holistic framework for identifying, assessing, and mitigating supply chain risks within the context of smart hotels. By integrating advanced technologies and strategic management practices, this study aims to reinforce smart hotel supply chains against potential vulnerabilities, ensuring sustainable business operations and superior guest satisfaction. The paper explores various risk management measures and leverages supply chain management, hospitality, and cybersecurity insights to develop a robust risk management strategy tailored for smart hotels.<strong></strong></p>

Comprehensive Security for IoT Devices with Kubernetes and Raspberry Pi Cluster

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster’s hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system’s ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures.

Evolution of system connectivity to support food production in the Indus Basin in Pakistan

Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production.

Enhancing Wireless Sensor Network Security Through Mutual Information Analysis for Intrusion Detection and Resilience

Intrusion detection is a critical aspect of network security, involving the identification and response to unauthorized access or malicious activities within a system. It plays a crucial role in safeguarding networks and ensuring data integrity and confidentiality. The Mutual Information (MI) analysis is a vital component in the realm of intrusion detection and security within Wireless Sensor Networks (WSNs). By assessing the information shared between pairs of sensor nodes, MI analysis reveals underlying patterns and relationships in network data. This process involves calculating the mutual information for each node pair based on statistical properties of observed values. Strong correlations or dependencies between nodes are identified, aiding in the detection of critical nodes or clusters vulnerable to compromise. Additionally, MI analysis informs feature extraction by guiding the selection of informative features that capture network structure. It also serves as a proactive tool for identifying anomalies or deviations from expected patterns, which may signal intrusion attempts or malicious activities. Through monitoring changes in mutual information over time, MI analysis facilitates prompt responses to emerging threats, enhancing the resilience and security of WSNs. The work exhibits high accuracy, recall rates, and detection rates across various attack scenarios, underscoring its efficacy in identifying and mitigating security threats. The algorithm's efficiency, effectiveness, and reliability make it a promising solution for enhancing WSN security. Through sophisticated methodologies and adaptive defensive strategies, the proposed algorithm strengthens the robustness and dependability of WSNs, minimizing the risk of potential security vulnerabilities and ensuring comprehensive threat detection in real-world deployment scenarios.

Exploring the intersection of network security and database communication: a PostgreSQL Socket Connection case study

In this study, the network security of PostgreSQL database using Socket connection is deeply analyzed. By exploring Socket connections established by PostgreSQL over TCP, we find potential security threats and vulnerabilities during data transmission, which may expose database systems to network attacks such as unauthorized access and data leakage. In order to assess these security risks, this study simulated a variety of network attack scenarios, especially the implantation and detection of Webshell, to reveal the vulnerability of PostgreSQL to such network threats. Especially in defending against complex and changeable cyber threats such as Webshell attacks, this research also uses machine learning and artificial intelligence techniques to improve the automation level of security threat detection and response. These technologies can help identify complex attack patterns and improve resilience to emerging threats, thereby enhancing the overall security of PostgreSQL databases.

Medical Data in Wireless Body Area Networks: Device Authentication Techniques and Threat Mitigation Strategies Based on a Token-Based Communication Approach

Wireless Body Area Networks (WBANs), low power, and short-range wireless communication in a near-body area provide advantages, particularly in the medical and healthcare sector: (i) they enable continuous monitoring of patients and (ii) the recording and correlation of physical and biological information. Along with the utilization and integration of these (sensitive) private and personal data, there are substantial requirements concerning security and privacy, as well as protection during processing and transmission. Contrary to the star topology frequently used in various standards, the overall concept of a novel low-data rate token-based WBAN framework is proposed. This work further comprises the evaluation of strategies for handling medical data with WBANs and emphasizes the importance and necessity of encryption and security strategies in the context of sensitive information. Furthermore, this work considers the recent advancements in Artificial Intelligence (AI), which are opening up opportunities for enhancing cyber resilience, but on the other hand, also new attack vectors. Moreover, the implications of targeted regulatory measures, such as the European AI Act, are considered. In contrast to, for instance, the proposed star network topologies of the IEEE 802.15.6 WBAN standard or the Technical Committee (TC) SmartBAN of the European Telecommunication Standards Institute (ETSI), the concept of a ring topology is proposed which concatenates information in the form of a ‘data train’ and thus results in faster and more efficient communication. Beyond that, the conductivity of human skin is included in the approach presented to incorporate a supplementary channel. This direct contact requirement not only fortifies the security of the system but also facilitates a reliable means of secure communication, pivotal in maintaining the integrity of sensitive health data. The work identifies different threat models associated with the WBAN system and evaluates potential data vulnerabilities and risks to maximize security. It highlights the crucial balance between security and efficiency in WBANs, using the token-based approach as a case study. Further, it sets a foundation for future healthcare technology advancements, aiming to ensure the secure and efficient integration of patient data.

Detour-RS: Reroute Attack Vulnerability Assessment with Awareness of the Layout and Resource

Recent decades have witnessed a remarkable pace of innovation and performance improvements in integrated circuits (ICs), which have become indispensable in an array of critical applications ranging from military infrastructure to personal healthcare. Meanwhile, recent developments have brought physical security to the forefront of concern, particularly considering the valuable assets handled and stored within ICs. Among the various invasive attack vectors, micro-probing attacks have risen as a particularly menacing threat. These attacks leverage advanced focused ion beam (FIB) systems to enable post-silicon secret eavesdropping and circuit modifications with minimal traceability. As an evolved variant of micro-probing attacks, reroute attacks possess the ability to actively disable built-in shielding measures, granting access to the security-sensitive signals concealed beneath. To address and counter these emerging challenges, we introduce a layout-level framework known as Detour-RS. This framework is designed to automatically assess potential vulnerabilities, offering a systematic approach to identifying and mitigating exploitable weaknesses. Specifically, we employed a combination of linear and nonlinear programming-based approaches to identify the layout-aware attack costs in reroute attempts given specific target assets. The experimental results indicate that shielded designs outperform non-shielded structures against reroute attacks. Furthermore, among the two-layer shield configurations, the orthogonal layout exhibits better performance compared to the parallel arrangement. Furthermore, we explore both independent and dependent scenarios, where the latter accounts for potential interference among circuit edit locations. Notably, our results demonstrate a substantial near 50% increase in attack cost when employing the more realistic dependent estimation approach. In addition, we also propose time and gas consumption metrics to evaluate the resource consumption of the attackers, which provides a perspective for evaluating reroute attack efforts. We have collected the results for different categories of target assets and also the average resource consumption for each via, required during FIB reroute attack.

Abstract LB332: Spatial transcriptomic and proteomic analysis of the treatment resistant slow cycling micro environment in GBM

Abstract Within the Glioblastoma (GBM) microenvironment, there are micro-niches originating from independent lineages of cancer stem cells with distinct metabolic requirements that are characterized by Fast cycling cells (FCCs) and Slow cycling cells (SCC). The FCCs employ aerobic glycolysis for energy, while the treatment-resistant SCCs primarily rely on lipid metabolism. Our study allows for the molecular and spatial decoding of the heterogeneity within the GBM microenvironment, with a specific focus on decoding the dichotomy of the immune infiltrates and studying the interactions between the SCCs and the immune compartment in human GBM patients. Our research reveals a new paradigm of interaction within the tumor microenvironment (TME) of glioblastoma (GBM) patients, in which SCCs are instrumental in altering the tumor milieu to favor immune suppression. These SCCs actively recruit and harness sub-population of immune cells of myeloid origin that promote tumor growth and simultaneously provide metabolic aid to these treatment-resistant cells. Our research maps out the intricate metabolic dialogues within GBM and evaluates the potential therapeutic effect of disrupting this interaction to open up new treatment avenues. The findings based on our spatial transcriptomic and proteomic study on brain biopsies from human patients provide a deeper understanding of the critical links among the tumor's local environment, cellular metabolism, anti-tumor immunity, and potential treatment vulnerabilities. In summary, our investigation offers novel insights into how metabolic exchanges contribute to the evolution of GBM leading to the development of drug and immune resistant GBM. Our data is suggestive that therapies aimed at these metabolic interactions, especially those between SCCs and metabolically supporting immunosuppressive myeloid originating immune cells, hold potential as effective therapeutic strategy for GBM. Citation Format: Avirup Chakraborty, Diana Feier, Aryeh Silver, Changlin Yang, Miruna Anica, Avinash Pittu, Olusegun Sobanjo, Ethan Hodge, Mia Engelbart, Christina Von Roemeling, Maryam Rahman, Matthew Sarkisian, Jianping Huang, Jeffrey Harrison, Duane Mitchell, Loic Deleyrolle. Spatial transcriptomic and proteomic analysis of the treatment resistant slow cycling micro environment in GBM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB332.

Wearable Device Bluetooth/BLE Physical Layer Dataset

Wearable devices, such as headsets and activity trackers, rely heavily on the Bluetooth and/or the Bluetooth Low Energy wireless communication standard to exchange data with smartphones or other peripherals. Since these devices collect personal health and activity data, ensuring the privacy and security of the transmitted data is crucial. Therefore, we present a dataset that captures complete Bluetooth communications—including advertising, connection, data exchange, and disconnection—in an RF isolated environment using software-defined radio. We were able to successfully decode the captured Bluetooth packets using existing tools. This dataset provides researchers with the ability to fully analyze Bluetooth traffic and gain insight into communication patterns and potential security vulnerabilities.

Hardware-in-Loop (HIL) Testbed Design of Thermal Power Plant for Threat Modeling and Attack Vector Analysis

Industrial control systems (ICSs) are extensively utilized worldwide to control and regulate various processes in energy utilities. It consists of various field devices, control and monitoring devices and communication devices. This paper focuses on the testing and analysis of various attack vectors that could potentially occur in a hardware-in-loop (HIL) Industrial Control System (ICS) testbed designed for a 500 MW thermal power. In the testbed, four typical process scenarios have been identified that can be manipulated through cyber-attacks, leading to severe issues such as plant shutdown or even explosions. The four significant plant scenarios recognized include minimal coal mill levels and increased temperatures in the classifier, heightened primary airflow to the coal mill, the tripping of an ID Fan, and adjustment of the Super-heater temperature to its lowest setting. Also, we utilize the STRIDE threat modeling methodology to accurately represents the elements of Cyber-Physical Systems (CPS), their inter-dependencies, and the potential attack entry points and system vulnerabilities.

The role of vulnerability assessments in USA agricultural animal operations

Major disaster events such as hurricanes, floods, fires, earthquakes and tornadoes pose significant challenges to agricultural production every year. Commercial livestock and poultry operations are highly dependent on favourable environment, weather conditions and infrastructure to thrive. Adverse conditions during extreme weather events and other emergencies can result in significant loss of animals and commodities due to disruptions in utilities and critical services, facility damage, contamination of feed and water supplies, environmental extremes and biosecurity lapses. The effects on production can be long lasting and cumulative. Being able to identify, assess and mitigate potential vulnerabilities that lower risk helps organisations to prepare for and recover from these events. This paper provides an overview of the vulnerability assessment process in the United States that focuses on general and better practices that should be considered when applying these principles to commercial livestock and poultry operations. This approach has broad advantages for all countries where livestock and agriculture are exposed to these risks.