Biblio

Today, most conversational agents are limited to simple tasks supported by standalone commands, such as getting directions or scheduling an appointment. To support more complex tasks, agents must be able to generalize from and combine the commands they already understand. This paper presents a new approach to designing conversational agents inspired by linguistic theory, where agents can execute complex requests interactively by combining commands through nested conversations. We demonstrate this approach in Iris, an agent that can perform open-ended data science tasks such as lexical analysis and predictive modeling. To power Iris, we have created a domain-specific language that transforms Python functions into combinable automata and regulates their combinations through a type system. Running a user study to examine the strengths and limitations of our approach, we find that data scientists completed a modeling task 2.6 times faster with Iris than with Jupyter Notebook.

In this paper, we analyze the security of cyber-physical systems using the ADversary VIew Security Evaluation (ADVISE) meta modeling approach, taking into consideration the effects of physical attacks. To build our model of the system, we construct an ontology that describes the system components and the relationships among them. The ontology also defines attack steps that represent cyber and physical actions that affect the system entities. We apply the ADVISE meta modeling approach, which admits as input our defined ontology, to a railway system use case to obtain insights regarding the system's security. The ADVISE Meta tool takes in a system model of a railway station and generates an attack execution graph that shows the actions that adversaries may take to reach their goal. We consider several adversary profiles, ranging from outsiders to insider staff members, and compare their attack paths in terms of targeted assets, time to achieve the goal, and probability of detection. The generated results show that even adversaries with access to noncritical assets can affect system service by intelligently crafting their attacks to trigger a physical sequence of effects. We also identify the physical devices and user actions that require more in-depth monitoring to reinforce the system's security.

The risk posed by insider threats has usually been approached by analyzing the behavior of users solely in the cyber domain. In this paper, we show the viability of using physical movement logs, collected via a building access control system, together with an understanding of the layout of the building housing the system's assets, to detect malicious insider behavior that manifests itself in the physical domain. In particular, we propose a systematic framework that uses contextual knowledge about the system and its users, learned from historical data gathered from a building access control system, to select suitable models for representing movement behavior. We then explore the online usage of the learned models, together with knowledge about the layout of the building being monitored, to detect malicious insider behavior. Finally, we show the effectiveness of the developed framework using real-life data traces of user movement in railway transit stations.

For industrial control systems, ensuring the software integrity of their devices is a key security requirement. A pure software-based attestation solution is highly desirable for protecting legacy field devices that lack hardware root of trust (e.g., Trusted Platform Module). However, for the large population of field devices with ARM processors, existing software-based attestation schemes either incur long attestation time or are insecure. In this paper, we design a novel memory stride technique that significantly reduces the attestation time while remaining secure against known attacks and their advanced variants on ARM platform. We analyze the scheme's security and performance based on the formal framework proposed by Armknecht et al. [7] (with a necessary change to ensure its applicability in practical settings). We also implement memory stride on two models of real-world power grid devices that are widely deployed today, and demonstrate its superior performance.

In this paper, we analyze the security of cyber-physical systems using the ADversary VIew Security Evaluation (ADVISE) meta modeling approach, taking into consideration the efects of physical attacks. To build our model of the system, we construct an ontology that describes the system components and the relationships among them. The ontology also deines attack steps that represent cyber and physical actions that afect the system entities. We apply the ADVISE meta modeling approach, which admits as input our deined ontology, to a railway system use case to obtain insights regarding the system’s security. The ADVISE Meta tool takes in a system model of a railway station and generates an attack execution graph that shows the actions that adversaries may take to reach their goal. We consider several adversary proiles, ranging from outsiders to insider staf members, and compare their attack paths in terms of targeted assets, time to achieve the goal, and probability of detection. The generated results show that even adversaries with access to noncritical assets can afect system service by intelligently crafting their attacks to trigger a physical sequence of efects. We also identify the physical devices and user actions that require more in-depth monitoring to reinforce the system’s security.

Electrical substations are crucial for power grids. A number of international standards, such as IEC 60870 and 61850, have emerged to enable remote and automated control over substations. However, owing to insufficient security consideration in their design and implementation, the resulting systems could be vulnerable to cyber attacks. As a result, the modernization of a large number of substations dramatically increases the scale of potential damage successful attacks can cause on power grids. To counter such a risk, one promising direction is to design and deploy an additional layer of defense at the substations. However, it remains a challenge to evaluate various substation cybersecurity solutions in a realistic environment. In this paper, we present the design and implementation of SoftGrid, a software-based smart grid testbed for evaluating the effectiveness, performance, and interoperability of various security solutions implemented to protect the remote control interface of substations. We demonstrate the capability and usefulness of SoftGrid through a concrete case study. We plan to open-source SoftGrid to facilitate security research in related areas.