Although machine learning (ML)-based models are increasingly being used by cloud-based data-driven services, two key problems exist when used at the edge. First, the size and complexity of these models hampers their deployment at the edge, where heterogeneity of resource types and constraints on resources is the norm. Second, ML models are known to be vulnerable to adversarial perturbations. To address the edge deployment issue, model compression techniques, especially model quantization, have shown significant promise. However, the adversarial robustness of such quantized models remains mostly an open problem. To address this challenge, this paper investigates whether quantized models with different precision levels can be vulnerable to the same universal adversarial perturbation (UAP). Based on these insights, the paper then presents a cloud-native service that generates and distributes adversarially robust compressed models deployable at the edge using a novel, defensive post-training quantization approach. Experimental evaluations reveal that although quantized models are vulnerable to UAPs, post-training quantization on the synthesized, adversarially-trained models are effective against such UAPs. Furthermore, deployments on heterogeneous edge devices with flexible quantization settings are efficient thereby paving the way in realizing adversarially robust data-driven cloud/edge services.

High-assurance software is often used in safety- and mission-critical systems where loss of functionality can lead to loss of life or property. Naturally, such systems need to be certified before use and several technologies have been developed to support such efforts. The techniques build structured assurance arguments to justify the safety and performance of the system. Most frequently, software is certified as part of a larger system where that larger system changes rather infrequently. However, this contradicts the current practice of rapid software evolution, where the need for new functionality is addressed by a software upgrade. As a consequence, assurance arguments often lag behind, leading to delays in implementing new capabilities. Hence, there is a clear need for the rapid re-analysis and re-evaluation of the assurance arguments. This paper argues that assurance arguments are a special kind of software documentation that need to be tightly integrated with the implementation, and their construction and managed evolution are critical to the safety and performance of software-integrated systems.

This paper presents our preliminary results developing an incremental query and transformation engine for our modeling framework. Our prior framework combined WebGME, a cloud-based collaborative modeling tool, with FORMULA, a language and tool for specifying and analyzing domain-specific modeling languages. While this arrangement has been successful for defining non-trivial languages in domains like CPS, one ongoing challenge is the scalability of executing model queries and transformations on large models. The inherent incremental nature of the modeling process exacerbates this scalability issue: model queries and transformations are repeatedly performed on incrementally updated models. To address this issue, we are developing an incremental version of FORMULA that can perform efficient model queries and transformations in the face of continual model updates. This paper describes our experiences designing this incremental version, including the challenges we faced and design decisions. We also report encouraging benchmark results.

The rowhammer bug belongs to software-induced hardware faults, and has been exploited to form a wide range of powerful rowhammer attacks. Yet, how to effectively detect such attacks remains a challenging problem. In this paper, we propose a novel approach named RADAR (Rowhammer Attack Detection via A Radio) that leverages certain electromagnetic (EM) signals to detect rowhammer attacks. In particular, we have found that there are recognizable hammering-correlated sideband patterns in the spectrum of the DRAM clock signal. As such patterns are inevitable physical side effects of hammering the DRAM, they can "expose" any potential rowhammer attacks including the extremely elusive ones hidden inside encrypted and isolated environments like Intel SGX enclaves. However, the patterns of interest may become unapparent due to the common use of spread-spectrum clocking (SSC) in computer systems. We propose a de-spreading method that can reassemble the hammering-correlated sideband patterns scattered by SSC. Using a common classification technique, we can achieve both effective and robust detection-based defense against rowhammer attacks, as evaluated on a RADAR prototype under various scenarios. In addition, our RADAR does not impose any performance overhead on the protected system. There has been little prior work that uses physical side-channel information to perform rowhammer defenses, and to the best of our knowledge, this is the first investigation on leveraging EM side-channel information for this purpose.

Cyber-physical systems increasingly rely on distributed computing platforms where sensing, computing, actuation, and communication resources are shared by a multitude of applications. Such 'cyber-physical cloud computing platforms' present novel challenges because the system is built from mobile embedded devices, is inherently distributed, and typically suffers from highly fluctuating connectivity among the modules. Architecting software for these systems raises many challenges not present in traditional cloud computing. Effective management of constrained resources and application isolation without adversely affecting performance are necessary. Autonomous fault management and real-time performance requirements must be met in a verifiable manner. It is also both critical and challenging to support multiple end-users whose diverse software applications have changing demands for computational and communication resources, while operating on different levels and in separate domains of security. The solution presented in this paper is based on a layered architecture consisting of a novel operating system, a middleware layer, and component-structured applications. The component model facilitates the construction of software applications from modular and reusable components that are deployed in the distributed system and interact only through well-defined mechanisms. The complexity of creating applications and performing system integration is mitigated through the use of a domain-specific model-driven development process that relies on a domain-specific modeling language and its accompanying graphical modeling tools, software generators for synthesizing infrastructure code, and the extensive use of model-based analysis for verification and validation.

Mobile clouds such as network-connected vehicles and satellite clusters are an emerging class of systems that are extensions to traditional real-time embedded systems: they provide long-term mission platforms made up of dynamic clusters of heterogeneous hardware nodes communicating over ad hoc wireless networks. Besides the inherent complexities entailed by a distributed architecture, developing software and testing these systems is difficult due to a number of other reasons, including the mobile nature of such systems, which can require a model of the physical dynamics of the system for accurate simulation and testing. This paper describes a rapid development and testing framework for a distributed satellite system. Our solutions include a modeling language for configuring and specifying an application's interaction with the middleware layer, a physics simulator integrated with hardware in the loop to provide the system's physical dynamics and the integration of a network traffic tool to dynamically vary the network bandwidth based on the physical dynamics.

We report here on our on-going work that addresses the automated analysis and test case generation for software systems modeled using multiple State chart formalisms.

In large programs such as NASA Exploration, multiple systems that interact via safety-critical protocols are already designed with different Statechart variants. To verify these safety-critical systems, a unified framework is needed based on a formal semantics that captures the variants of Statecharts. We describe Polyglot, a unified framework for the analysis of models described using multiple State-chart formalisms. In this framework, Statechart models are translated into Java and analyzed using pluggable semantics for different variants operating in a polymorphic execution environment. The framework has been built on the basis of a parametric formal semantics that captures the common core of Statecharts with extensions for different variants, and addresses previous limitations. Polyglot has been integrated with the Java Pathfinder verification tool-set, providing analysis and test-case generation capabilities. We describe the application of this unified framework to the analysis of NASA/JPL's MER Arbiter whose interacting components were modeled using multiple Statechart formalisms.

Assigning behavioral semantics to domain-specific languages (DSLs) opens the door for the application of formal methods, yet is largely an unresolved problem. Previously proposed solutions include semantic anchoring, in which a transformation from the DSL to an external framework that can supply both behavioral semantics and apply formal methods is constructed. The drawback of this approach is that it loses the structural constraints of the original DSL along with the details of the transformation, which can lead to erroneous results when formal methods are applied. We demonstrate this problem of ¿forgetful¿ semantic anchoring using existing approaches through a translation from dataflow systems to interface automata. We then describe our modeling tool FORMULA and apply it to the same example, showing how forgetful semantic anchoring can be avoided.