Chaos Engineering (CE), which Netflix introduced in 2008, is used by researchers to assess and find weaknesses in system resiliency. Such weaknesses can arise, when subsystems are individually robust, but that robustness disappears when multiple subsystems are paired together in a System of Systems (SoS). CE researchers develops methods and metrics for finding such fragilities. In this paper, we expand previous examinations of CE experimentation for SoS and introduce Security Chaos Engineering (SCE) for SoS. These SCE experiments include terminating message service, flooding multi queues/message, and injecting corrupted Service. SCE assumes compromise by adding a malicious actor to the tests that can induce adversarial failures into a SoS. For our SoS testbed, we instantiated a virtual Unmanned Aerial Vehicle (VUAV). We use the open-source Chaos Toolkit to run consistent CE and SCE experiments on the VUAV. Chaos Toolkit with SCE exposes the VUAV attack surfaces to evaluate performance and system security. This research allows us to establish an understanding of baseline system performance and gaps in procedures, techniques, and tools from the state of the art as applied to DoD-relevant systems like SoS. We use the load placed on the Central Processing Unit (CPU) and Random-Access Memory (RAM) by the VUAV as metrics for baseline performance. The results showed that these two metrics did not provide enough fidelity in where CE/SCE creates failures. Feeding these results into the CE methodology allows for additional metrics to better pinpoint failures with CE/SCE testing.
KEYWORDS: Receivers, Data modeling, Received signal strength, Machine learning, Global Positioning System, Filtering (signal processing), Sensors, Fourier transforms, Distance measurement, Detection and tracking algorithms
Determining position within an indoor environment can be difficult when GPS signals become too weak. For this reason, alternatives are desired for indoor positioning systems (IPSes). The Bluetooth Low Energy (BLE) protocol is one alternative solution for IPSes. BLE is a low power wireless technology used for connecting devices with each other. There are two different methods for using BLE for localization: deterministic, and machine learning (ML) models. Each method uses a measured received signal strength indicator (RSSI) to determine distances from fixed, known locations. Deterministic models rely on empirical equations relating signal strength to distance, while ML uses collected signal strengths, or fingerprints, to learn positions. This paper assesses the robustness of an IPS system we built that uses BLE and ML by executing a distance fraud attack. A distance fraud attack causes intentional miscalculations of positions. The attack executed on the system assumes the attacker has network access and has compromised some small fraction of the receiving nodes. The results show a significant difference between the calculated positions of the system operating under benign conditions and operating under attack. We explore one possible defense against this attack by training an ML system for attack identification.
KEYWORDS: Signal to noise ratio, Binary data, Data modeling, Signal processing, Analog electronics, Error analysis, Light emitting diodes, Remote sensing, Internet, Analytical research
We use machine learning to characterize the state of digital devices based on their analog emissions. As digital devices operate, they emit internal information into a number of analog side channels. Remote sensing of these unintended signals leads to low signal-to-noise-ratio (SNR) and significant clutter. We developed classifiers to determine which program is executing on a digital device based on analog radio-frequency (RF) emissions collected via a 500-MHz Riscure RF probe. A standard algorithm was developed to serve as a baseline program and intrusions were simulated by introducing minor modifications to this program. We collected a thousand RF traces from each of these modified programs running on ten different devices for thousands of instruction cycles. The ten devices tested are representative of the Internet of Things (IoT) devices including Arduino Unos and PIC24 processors. Our primary approach to mitigating the impact of low SNR is to extend the program execution and signal collection time. Collecting a training set with more traces than samples is not practical. Even after down-sampling the raw data to thirty samples per instruction, the number of samples exceeds the number of traces by orders of magnitude. Such a training set nearly guarantees overlearning. To mitigate this, we present our Whitened Mean Classifier as a method to whiten this sparse training set and avoid overlearning. Classification accuracy exceeded 90% for the modified programs on a subset of the ten devices.
In this work, we describe a rapid-innovation challenge to combat and deal with the problem of internal, insider physical threats (e.g., active shooters) and associated first-responder situation awareness on military installations. Our team’s research and development effort described within focused on several key tech development areas: (1) indoor acoustical gunshot detection, (2) indoor spatial tracking of first responders, (3) bystander safety and protection, (4) two-way mass alerting capability, and (5) spatial information displays for command and control. The technological solutions were specifically designed to be innovative, low-cost, and (relatively) easy-to-implement, and to provide support across the spectrum of possible users including potential victims/bystanders, first responders, dispatch, and incident command.
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