Biblio

We propose Black-Box IoT (BBox-IoT), a new ultra-lightweight black-box system for authenticating and storing IoT data. BBox-IoT is tailored for deployment on IoT devices (including low-Size Weight and Power sensors) which are extremely constrained in terms of computation, storage, and power. By utilizing core Blockchain principles, we ensure that the collected data is immutable and tamper-proof while preserving data provenance and non-repudiation. To realize BBox-IoT, we designed and implemented a novel chain-based hash signature scheme which only requires hashing operations and removes all synchronicity dependencies between signer and verifier. Our approach enables low-SWaP devices to authenticate removing reliance on clock synchronization. Our evaluation results show that BBox-IoT is practical in Industrial Internet of Things (IIoT) environments: even devices equipped with 16MHz microcontrollers and 2KB memory can broadcast their collected data without requiring heavy cryptographic operations or synchronicity assumptions. Finally, when compared to industry standard ECDSA, our approach is two and three orders of magnitude faster for signing and verification operations respectively. Thus, we are able to increase the total number of signing operations by more than 5000% for the same amount of power.

Many self-adaptive systems benefit from human involvement, where human operators can complement the capabilities of systems (e.g., by supervising decisions, or performing adaptations and tasks involving physical changes that cannot be automated). However, insufficient preparation (e.g., lack of task context comprehension) may hinder the effectiveness of human involvement, especially when operators are unexpectedly interrupted to perform a new task. Preparatory notification of a task provided in advance can sometimes help human operators focus their attention on the forthcoming task and understand its context before task execution, hence improving effectiveness. Nevertheless, deciding when to use preparatory notification as a tactic is not obvious and entails considering different factors that include uncertainties induced by human operator behavior (who might ignore the notice message), human attributes (e.g., operator training level), and other information that refers to the state of the system and its environment. In this paper, informed by work in cognitive science on human attention and context management, we introduce a formal framework to reason about the usage of preparatory notifications in self-adaptive systems involving human operators. Our framework characterizes the effects of managing attention via task notification in terms of task context comprehension. We also build on our framework to develop an automated probabilistic reasoning technique able to determine when and in what form a preparatory notification tactic should be used to optimize system goals. We illustrate our approach in a representative scenario of human-robot collaborative goods delivery.

Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.

The question of whether “handling unanticipated changes is the ultimate challenge for self-adaptation” is impossible to evaluate without looking closely at what “unanticipated” means. In this position paper I try to bring a little clarity to this issue by arguing that the common distinction between “known unknowns” and “unknown unknowns” is too crude: for most systems there are changes that are not directly handled by “first-order” adaptation, but can, with appropriate engineering, be addressed naturally through “second-order” adaptation. I explain what I mean by this and consider ways in which such systems might be engineered.

Motivated by the transformative impact of deep neural networks (DNNs) in various domains, researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering. In this work, we propose an attack that interweaves binary-diversification techniques and optimization frameworks to mislead such DNNs while preserving the functionality of binaries. Unlike prior attacks, ours manipulates instructions that are a functional part of the binary, which makes it particularly challenging to defend against. We evaluated our attack against three DNNs in white- and black-box settings, and found that it often achieved success rates near 100%. Moreover, we found that our attack can fool some commercial anti-viruses, in certain cases with a success rate of 85%. We explored several defenses, both new and old, and identified some that can foil over 80% of our evasion attempts. However, these defenses may still be susceptible to evasion by attacks, and so we advocate for augmenting malware-detection systems with methods that do not rely on machine learning.