Biblio
Experience shows that even with a well-intentioned user at the keyboard, a motivated attacker can compromise a computer system at a layer below or adjacent to the shallow forms of authentication that are now accepted as commonplace[3]. Therefore, rather than asking "Can we trust the person behind the keyboard", a still better question might be: "Can we trust the computer system underneath?". An emerging technology for gaining trust in a remote computing system is remote attestation. Remote attestation is the activity of making a claim about properties of a target by supplying evidence to an appraiser over a network[2]. Although many existing approaches to remote attestation wisely adopt a layered architecture-where the bottom layers measure layers above-the dependencies between components remain static and measurement orderings fixed. For modern computing environments with diverse topologies, we can no longer fix a target architecture any more than we can fix a protocol to measure that architecture.Copland [1] is a domain-specific language and formal framework that provides a vocabulary for specifying the goals of layered attestation protocols. It also provides a reference semantics that characterizes system measurement events and evidence handling; a foundation for comparing protocol alternatives. The aim of this work is to refine the Copland semantics to a more fine-grained notion of attestation manager execution-a high-privilege thread of control responsible for invoking attestation services and bundling evidence results. This refinement consists of two cooperating components called the Copland Compiler and the Attestation Virtual Machine (AVM). The Copland Compiler translates a Copland protocol description into a sequence of primitive attestation instructions to be executed in the AVM. When considered in combination with advances in virtualization, trusted hardware, and high-assurance system software components-like compilers, file-systems, and OS kernels-a formally verified remote attestation infrastructure creates exciting opportunities for building system-level security arguments.
Realistic state-based discrete-event simulation models are often quite complex. The complexity frequently manifests in models that (a) contain a large number of input variables whose values are difficult to determine precisely, and (b) take a relatively long time to solve. Traditionally, models that have a large number of input variables whose values are not well-known are understood through the use of sensitivity analysis (SA) and uncertainty quantification (UQ). However, it can be prohibitively time consuming to perform SA and UQ. In this work, we present a novel approach we developed for performing fast and thorough SA and UQ on a metamodel composed of a stacked ensemble of regressors that emulates the behavior of the base model. We demonstrate the approach using a previously published botnet model as a test case, showing that the metamodel approach is several orders of magnitude faster than the base model, more accurate than existing approaches, and amenable to SA and UQ.
Many popular online social networks, such as Twitter, Tum-blr, and Sina Weibo, adopt too simple privacy models to satisfy users’diverse needs for privacy protection. In platforms with no (i.e., completely open) or binary (i.e., “public” and “friends-only”) access con-trol, users cannot control the dissemination boundary of the contentthey share. For instance, on Twitter, tweets in “public” accounts areaccessible to everyone including search engines, while tweets in “pro-tected” accounts are visible toallthe followers. In this work, we presentArcanato enable fine-grained access control for social network content sharing. In particular, we target the Twitter platform and intro-duce the “private tweet” function, which allows users to disseminateparticular tweets to designated group(s) of followers. Arcana employsCiphertext-Policy Attribute-based Encryption (CP-ABE) to implement social circle detection and private tweet encryption so that access-controlled tweets are only readable by designated recipients. To bestealthy, Arcana further embeds the protected content as digital water-marks in image tweets. We have implemented the Arcana prototype asa Chrome browser plug-in, and demonstrated its flexibility and effec-tiveness. Different from existing approaches that require trusted third-parties or additional server/broker/mediator, Arcana is light-weight andcompletely transparent to Twitter – all the communications, includingkey distribution and private tweet dissemination, are exchanged as Twit-ter messages. Therefore, with small API modifications, Arcana could beeasily ported to other online social networking platforms to support fine-grained access control.
In this paper we investigate the feasibility of denial-of-service (DoS) attacks on shared caches in multicore platforms. With carefully engineered attacker tasks, we are able to cause more than 300X execution time increases on a victim task running on a dedicated core on a popular embedded multicore platform, regardless of whether we partition its shared cache or not. Based on careful experimentation on real and simulated multicore platforms, we identify an internal hardware structure of a non-blocking cache, namely the cache writeback buffer, as a potential target of shared cache DoS attacks. We propose an OS-level solution to prevent such DoS attacks by extending a state-of-the-art memory bandwidth regulation mechanism. We implement the proposed mechanism in Linux on a real multicore platform and show its effectiveness in protecting against cache DoS attacks.
In this paper, we present RT-Gang: a novel real-time gang scheduling framework that enforces a one-gang-at-a-time policy. We find that, in a multicore platform, co-scheduling multiple parallel real-time tasks would require highly pessimistic worst-case execution time (WCET) and schedulability analysis - even when there are enough cores - due to contention in shared hardware resources such as cache and DRAM controller. In RT-Gang, all threads of a parallel real-time task form a real-time gang and the scheduler globally enforces the one-gang-at-a-time scheduling policy to guarantee tight and accurate task WCET. To minimize under-utilization, we integrate a state-of-the-art memory bandwidth throttling framework to allow safe execution of best-effort tasks. Specifically, any idle cores, if exist, are used to schedule best-effort tasks but their maximum memory bandwidth usages are strictly throttled to tightly bound interference to real-time gang tasks. We implement RT-Gang in the Linux kernel and evaluate it on two representative embedded multicore platforms using both synthetic and real-world DNN workloads. The results show that RT-Gang dramatically improves system predictability and the overhead is negligible.
We present Copland, a language for specifying layered attestations. Layered attestations provide a remote appraiser with structured evidence of the integrity of a target system to support a trust decision. The language is designed to bridge the gap between formal analysis of attestation security guarantees and concrete implementations. We therefore provide two semantic interpretations of terms in our language. The first is a denotational semantics in terms of partially ordered sets of events. This directly connects Copland to prior work on layered attestation. The second is an operational semantics detailing how the data and control flow are executed. This gives explicit implementation guidance for attestation frameworks. We show a formal connection between the two semantics ensuring that any execution according to the operational semantics is consistent with the denotational event semantics. This ensures that formal guarantees resulting from analyzing the event semantics will hold for executions respecting the operational semantics. All results have been formally verified with the Coq proof assistant.
NVDLA is an open-source deep neural network (DNN) accelerator which has received a lot of attention by the community since its introduction by Nvidia. It is a full-featured hardware IP and can serve as a good reference for conducting research and development of SoCs with integrated accelerators. However, an expensive FPGA board is required to do experiments with this IP in a real SoC. Moreover, since NVDLA is clocked at a lower frequency on an FPGA, it would be hard to do accurate performance analysis with such a setup. To overcome these limitations, we integrate NVDLA into a real RISC-V SoC on the Amazon could FPGA using FireSim, a cycle-exact FPGA-accelerated simulator. We then evaluate the performance of NVDLA by running YOLOv3 object-detection algorithm. Our results show that NVDLA can sustain 7.5 fps when running YOLOv3. We further analyze the performance by showing that sharing the last-level cache with NVDLA can result in up to 1.56x speedup. We then identify that sharing the memory system with the accelerator can result in unpredictable execution time for the real-time tasks running on this platform. We believe this is an important issue that must be addressed in order for on-chip DNN accelerators to be incorporated in real-time embedded systems.
Speculative execution is an essential performance enhancing technique in modern processors, but it has been shown to be insecure. In this paper, we propose SpectreGuard, a novel defense mechanism against Spectre attacks. In our approach, sensitive memory blocks (e.g., secret keys) are marked using simple OS/library API, which are then selectively protected by hardware from Spectre attacks via low-cost micro-architecture extension. This technique allows microprocessors to maintain high performance, while restoring the control to software developers to make security and performance trade-offs.
Single sign-on (SSO) is becoming more and more popular in the Internet. An SSO ticket issued by the identity provider (IdP) allows an entity to sign onto a relying party (RP) on behalf of the account enclosed in the ticket. To ensure its authenticity, an SSO ticket is digitally signed by the IdP and verified by the RP. However, recent security incidents indicate that a signing system (e.g., certification authority) might be compromised to sign fraudulent messages, even when it is well protected in accredited commercial systems. Compared with certification authorities, the online signing components of IdPs are even more exposed to adversaries and thus more vulnerable to such threats in practice. This paper proposes ticket transparency to provide accountable SSO services with privacy-preserving public logs against potentially fraudulent tickets issued by a compromised IdP. With this scheme, an IdP-signed ticket is accepted by the RP only if it is recorded in the public logs. It enables a user to check all his tickets in the public logs and detect any fraudulent ticket issued without his participation or authorization. We integrate blind signatures, identity-based encryption and Bloom filters in the design, to balance transparency, privacy and efficiency in these security-enhanced SSO services. To the best of our knowledge, this is the first attempt to solve the security problems caused by potentially intruded or compromised IdPs in the SSO services.
This chapter explores the relationship between the concept of emergence, the goal of theoretical completeness, and the Principle of Sufficient Reason. Samuel Alexander and C. D. Broad argued for limits to the power of scientific explanation. Chemical explanation played a central role in their thinking. After Schrödinger’s work in the 1920s their examples seem to fall flat. However, there are more general lessons from the emergentists that need to be explored. There are cases where we know that explanation of some phenomenon is impossible. What are the implications of known limits to the explanatory power of science, and the apparent ineliminability of brute facts for emergence? One lesson drawn here is that we must embrace a methodological rather than a metaphysical conception of the Principle of Sufficient Reason.
The explosive proliferation of Internet of Things (IoT) devices is generating an incomprehensible amount of data. Machine learning plays an imperative role in aggregating this data and extracting valuable information for improving operational and decision-making processes. In particular, emerging machine intelligence platforms that host pre-trained machine learning models are opening up new opportunities for IoT industries. While those platforms facilitate customers to analyze IoT data and deliver faster and accurate insights, end users and machine learning service providers (MLSPs) have raised concerns regarding security and privacy of IoT data as well as the pre-trained machine learning models for certain applications such as healthcare, smart energy, etc. In this paper, we propose a cloud-assisted, privacy-preserving machine learning classification scheme over encrypted data for IoT devices. Our scheme is based on a three-party model coupled with a two-stage decryption Paillier-based cryptosystem, which allows a cloud server to interact with MLSPs on behalf of the resource-constrained IoT devices in a privacy-preserving manner, and shift load of computation-intensive classification operations from them. The detailed security analysis and the extensive simulations with different key lengths and number of features and classes demonstrate that our scheme can effectively reduce the overhead for IoT devices in machine learning classification applications.
Fundamentality is the central conceptual component of discussions concerning the emergence. Most obviously, contemporary uses of the term "emergence" vary according to their users' views of fundamentality. This chapter provides a general characterization of fundamentality, explaining the challenges faced by the anti‐emergentist versions of fundamentalism. It discusses the limitations of one prominent account of ontological fundamentality, physicalism. Although physicalism does not present a viable alternative to emergentism, this does not mean that emergentists can declare victory. Completeness is essential to arguments against the possibility of strongly emergent properties. Three interlocking concepts: causation, completeness, and reality, are not straightforwardly scientific in nature, but are, instead, metaphysical, or at least conceptual. Scientific models are intended to provide guidance with respect to explanations and predictions of emergent properties or to offer possible interventions that would allow control over those properties.
Poor time predictability of multicore processors has been a long-standing challenge in the realtime systems community. In this paper, we make a case that a fundamental problem that prevents efficient and predictable real-time computing on multicore is the lack of a proper memory abstraction to express memory criticality, which cuts across various layers of the system: the application, OS, and hardware. We, therefore, propose a new holistic resource management approach driven by a new memory abstraction, which we call Deterministic Memory. The key characteristic of deterministic memory is that the platform–the OS and hardware–guarantees small and tightly bounded worst-case memory access timing. In contrast, we call the conventional memory abstraction as best-effort memory in which only highly pessimistic worst-case bounds can be achieved. We propose to utilize both abstractions to achieve high time predictability but without significantly sacrificing performance. We present deterministic memory-aware OS and architecture designs, including OS-level page allocator, hardware-level cache, and DRAM controller designs. We implement the proposed OS and architecture extensions on Linux and gem5 simulator. Our evaluation results, using a set of synthetic and real-world benchmarks, demonstrate the feasibility and effectiveness of our approach.
We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture—9 layers, 27 million connections and 250K parameters—and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3’s computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar’s CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.