Biblio

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features integrated neuro-symbolic inference, where symbolic context is used by deep learning, and deep learning models provide atomic concepts for symbolic reasoning. The incorporation of high-level symbolic reasoning improves data efficiency during training and makes inference more robust, interpretable, and resource-efficient. In this paper, we identify the key challenges in developing context-aware collaborative neuro-symbolic inference in IoBTs and review some recent progress in addressing these gaps.

Personalized IoT adapt their behavior based on contextual information, such as user behavior and location. Unfortunately, the fact that personalized IoT adapt to user context opens a side-channel that leaks private information about the user. To that end, we start by studying the extent to which a malicious eavesdropper can monitor the actions taken by an IoT system and extract user's private information. In particular, we show two concrete instantiations (in the context of mobile phones and smart homes) of a new category of spyware which we refer to as Context-Aware Adaptation Based Spyware (SpyCon). Experimental evaluations show that the developed SpyCon can predict users' daily behavior with an accuracy of 90.3%. Being a new spyware with no known prior signature or behavior, traditional spyware detection that is based on code signature or system behavior are not adequate to detect SpyCon. We discuss possible detection and mitigation mechanisms that can hinder the effect of SpyCon.

Autonomic resource management for distributed edge computing systems provides an effective means of enabling dynamic placement and adaptation in the face of network changes, load dynamics, and failures. However, adaptation in-and-of-itself offers a side channel by which malicious entities can extract valuable information. An attacker can take advantage of autonomic resource management techniques to fool a system into misallocating resources and crippling applications. Using a few scenarios, we outline how attacks can be launched using partial knowledge of the resource management substrate - with as little as a single compromised node. We argue that any system that provides adaptation must consider resource management as an attack surface. As such, we propose ADAPT2, a framework that incorporates concepts taken from Moving-Target Defense and state estimation techniques to ensure correctness and obfuscate resource management, thereby protecting valuable system and application information from leaking.

Internet-of-Things (IoTs) are becoming more and more popular in our life. IoT devices are generally designed for sensing or actuation purposes. However, the current sensing system on IoT devices lacks the understanding of sensing needs, which diminishes the sensing flexibility, isolation, and security when multiple sensing applications need to use sensor resources. In this work, we propose VirtSense, an ARM TrustZone based virtual sensing system, to provide each sensing application a virtual sensor instance, which further enables a safe, flexible and isolated sensing environment on the IoT devices. Our preliminary results show that VirtSense: 1) can provide virtual sensor instance for each sensing application so that the sensing needs of each application will be satisfied without affecting others; 2) is able to enforce access control policy even under an untrusted environment.