A Cyber-War Between Bots: Human-Like Attackers are More Challenging for Defenders than Deterministic Attackers

Adversary emulation is commonly used to test cyber defense performance against known threats to organizations. However, designing attack strategies is an expensive and unreliable manual process, based on subjective evaluation of the state of a network. In this paper, we propose the design of adversarial human-like cognitive models that are dynamic, adaptable, and have the ability to learn from experience. A cognitive model is built according to the theoretical principles of Instance-Based Learning Theory (IBLT) of experiential choice in dynamic tasks. In a simulation experiment, we compared the predictions of an IBL attacker with a carefully designed efficient but deterministic attacker attempting to access an operational server in a network. The results suggest that an IBL cognitive model that emulates human behavior can be a more challenging adversary for defenders than the carefully crafted optimal attack strategies. These insights can be used to inform future adversary emulation efforts and cyber defender training

A Cyber-War Between Bots: Human-Like Attackers are More Challenging for Defenders than Deterministic Attackers

Adversary emulation is commonly used to test cyber defense performance against known threats to organizations. However, designing attack strategies is an expensive and unreliable manual process, based on subjective evaluation of the state of a network. In this paper, we propose the design of adversarial human-like cognitive models that are dynamic, adaptable, and have the ability to learn from experience. A cognitive model is built according to the theoretical principles of Instance-Based Learning Theory (IBLT) of experiential choice in dynamic tasks. In a simulation experiment, we compared the predictions of an IBL attacker with a carefully designed efficient but deterministic attacker attempting to access an operational server in a network. The results suggest that an IBL cognitive model that emulates human behavior can be a more challenging adversary for defenders than the carefully crafted optimal attack strategies. These insights can be used to inform future adversary emulation efforts and cyber defender training

Aboveground Biomass and Soil Moisture as Affected by Short-Term Grazing Exclusion in Eastern Alpine Meadows of the Qinghai-Tibet Plateau, China

Heavy grazing substantially influences grassland vegetation and animal nutrition on the Qinghai-Tibet plateau (Guo et al. 2003). Degradation is characterized by a reduction in vegetation height, reduced ground cover decrease in species diversity (Wang et al. 2007). The objective of this study was to determine the effects of short-term exclusion from grazing on aboveground herbage, forage nutritive value, and soil moisture in an alpine meadow in the eastern zone of the plateau. Three farms, applying different intensity of grazing over the summer months, were compared

On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work is two-fold: (i) presenting performance bounds of MAE, and (ii) demonstrating new properties of MAE that make it more appropriate than mean squared error (MSE) as a loss function for DNN based vector-to-vector regression. First, we show that a generalized upper-bound for DNN-based vector-to-vector regression can be ensured by leveraging the known Lipschitz continuity property of MAE. Next, we derive a new generalized upper bound in the presence of additive noise. Finally, in contrast to conventional MSE commonly adopted to approximate Gaussian errors for regression, we show that MAE can be interpreted as an error modeled by Laplacian distribution. Speech enhancement experiments are conducted to corroborate our proposed theorems and validate the performance advantages of MAE over MSE for DNN based regression