It is a challenging problem to resist unknown advanced persistent threats (APTs) on stealing data resources in an information system of critical infrastructures, because APT attackers have very specific objectives and compromise the system stealthily and slowly. We observe that it is a necessary condition for APT attackers to achieve their campaigns via controlling unknown Trojans to access and exfiltrate critical files. We present a theoretical model called Digital Scapegoat (abbreviated as DS-IDep) that constructs an Incentive Deception defense schema to hijack the attacker’s access to critical files and redirect it to avatar files without awareness. We propose a FlipIDep Game model (GF) and a Markov Game model (GM) to characterize completely the payoffs, equilibria, and best strategies from the perspective of the attacker and the defender respectively. We also design an exponential risk propagation model to evaluate the ability of DS-IDep to eliminate stealing impact when the risk is propagated between states. Theoretically, we can achieve the objective of stealing impact elimination (LK0.7) and the probability of an attack operation bypassing the defense surface is less than 0.1 (r* × μ <0.1) under Stackelberg strategies. We develop a kernel-level incentive deception defense surface according to the theoretical parameters of the DS-IDep. The experimental results show that DS-IDep can resist APT stealing attacks from unknown Trojans. We also evaluate the DS-IDep in five well-known software applications. It demonstrates that DS-IDep can address unknown attacks from compromised software with less than 10% performance overhead.

With the rapid development of the Internet of Things (IoT), a massive number of smart devices are deployed in industry and critical infrastructures. Nowadays, IoT smart devices have drawn increasing attention for collaborative learning tasks, e.g., persistent monitoring and online recognition. In this article, we present an adaptive compact deep neural network (DNN) approach (termed as AC-DNN) to tackle the challenging problem of unreliable transmission for collaborative learning tasks among heterogeneous smart devices. We introduce a cross-platform model weight encoding, decoding, and dispatching architecture to accommodate to differential smart devices and improve the reliability of intermediate model transmission via encapsulating binary model weights into self-contained transactions. To decrease encoding and decoding overhead, we design a quantile-based histogram sketch to compress the intermediate model. We conduct extensive evaluations to test our AC-DNN framework and deploy the AC-DNN on federated learning testbed FedAvg. We evaluate our approach functionality using different DNN architectures, such as convolutional neural network and ResNet and compare their effectiveness within the different network structures. The experiments reveal that our approach can improve the reliability of collaborative learning tasks among smart devices. Meanwhile, we can achieve nearly 70% weight compression compared to the original model size with minimal loss of accuracy. Our approach facilitates the deployment of a DNN-like network among discrete mobile smart devices for deep and persistent learning tasks.