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We model a vehicle equipped with an autonomous cyber-defense system in addition to its inherent physical resilience features. When attacked, this ensemble of cyber-physical features (i.e., 'bonware') strives to resist and recover from the performance degradation caused by the malware's attack. We model the underlying differential equations governing such attacks for piecewise linear characterizations of malware and bonware, develop a discrete time stochastic model, and show that averages of instantiations of the stochastic model approximate solutions to the continuous differential equation. We develop a theory and methodology for approximating the parameters associated with these equations.

We identify quantitative characteristics of responses to cyber compromises that can be learned from repeatable, systematic experiments. We model a vehicle equipped with an autonomous cyber-defense system and which also has some inherent physical resilience features. When attacked by malware, this ensemble of cyber-physical features (i.e., 'bonware') strives to resist and recover from the performance degradation caused by the malware's attack. We propose parsimonious continuous models, and develop stochastic models to aid in quantifying systems' resilience to cyber attacks.

The vulnerability of cyber-physical systems to cyber attack is well known, and the requirement to build cyber resilience into these systems has been firmly established. The key challenge this paper addresses is that maturing this discipline requires the development of techniques, tools, and processes for objectively, rigorously, and quantitatively measuring the attributes of cyber resilience. Researchers and program managers need to be able to determine if the implementation of a resilience solution actually increases the resilience of the system. In previous work, a table top exercise was conducted using a notional heavy vehicle on a fictitious military mission while under a cyber attack. While this exercise provided some useful data, more and higher fidelity data is required to refine the measurement methodology. This paper details the efforts made to construct a cost-effective experimentation infrastructure to provide such data. It also presents a case study using some of the data generated by the infrastructure.

Machine learning-based network intrusion detection systems have demonstrated state-of-the-art accuracy in flagging malicious traffic. However, machine learning has been shown to be vulnerable to adversarial examples, particularly in domains such as image recognition. In many threat models, the adversary exploits the unconstrained nature of images–the adversary is free to select some arbitrary amount of pixels to perturb. However, it is not clear how these attacks translate to domains such as network intrusion detection as they contain domain constraints, which limit which and how features can be modified by the adversary. In this paper, we explore whether the constrained nature of networks offers additional robustness against adversarial examples versus the unconstrained nature of images. We do this by creating two algorithms: (1) the Adapative-JSMA, an augmented version of the popular JSMA which obeys domain constraints, and (2) the Histogram Sketch Generation which generates adversarial sketches: targeted universal perturbation vectors that encode feature saliency within the envelope of domain constraints. To assess how these algorithms perform, we evaluate them in a constrained network intrusion detection setting and an unconstrained image recognition setting. The results show that our approaches generate misclassification rates in network intrusion detection applications that were comparable to those of image recognition applications (greater than 95%). Our investigation shows that the constrained attack surface exposed by network intrusion detection systems is still sufficiently large to craft successful adversarial examples – and thus, network constraints do not appear to add robustness against adversarial examples. Indeed, even if a defender constrains an adversary to as little as five random features, generating adversarial examples is still possible. [ABSTRACT FROM AUTHOR]

There has been an increasing interest in ultraviolet (UV) communications as a promising technology for non-line-of-sight (NLOS) networking by exploiting atmospheric scattering at UV wavelengths that enables a unique NLOS UV communication channel. While there has been significant theoretical and simulation-based investigation of the UV channel characteristics, there is limited work in terms of experimental research and validation of the analytical models. In this paper, we present a flexible experimental system for precise UV channel and communications measurements. Specifically, a transceiver system is developed that consists of a gimbal, UV light-emitting-diode array, and photomultiplier tube detector, node synchronization, and LabVIEW-based data acquisition subsystems. Novel techniques to precisely characterize the UV LED array radiation pattern, absolute transmit power, and field of view of the detector are also presented. The utility of the developed system is then demonstrated by performing a variety of experiments including UV channel model validation and steering optimization for UV communication links where the results were in very good agreement with theory and simulation.

The ultraviolet communication (UV) channel has been shown to have unique features that could be exploited for covert ground-to-ground communications in complex non-line-of-sight (NLOS) scenarios. A key challenge is the determination of optimal configuration of pointing directions of the UV nodes in unknown NLOS environments to maximize the link performance. In this paper, we proposed a novel steering optimization approach based on Finite Difference Stochastic Approximation (FDSA) to simultaneously optimize the transmitter (Tx) and receiver (Rx) pointing directions without any knowledge about the locations and relative orientations of the two nodes. We perform parametric analysis using Monte Carlo channel simulations to investigate and select appropriate key algorithmic parameters and analyze the performance of the proposed algorithm. We also carry out experimentation using our custom designed UV Tx and Rx gimbal systems and demonstrate the utility and efficiency of the proposed steering optimization approach and show that the received photon count can be increased significantly.

Deep ultraviolet wavelengths have been proposed for low-probability-of-detection (LPD) communications, particularly for non-line-of-sight (NLOS) links, because of the increased atmospheric absorption at these wavelengths. Motivated by this favorable feature, we develop a modeling framework to quantitatively study the LPD characteristics of ultraviolet communications (UVC). We then demonstrate the application of our modeling framework by considering various friendly and adversarial system configurations and quantifying the proposed LPD metric (the range at which an adversary can detect communications that uses the minimum power needed to meet given communications performance requirements), as well as investigating the sensitivity of the analysis to various scenario parameters. The results demonstrate the potential for this modeling and analysis approach to provide key insights into the design and operation of LPD NLOS UVC systems.