Recent advances in deep learning (DL) have brought tremendous gains in signal modulation classification. However, DL-based classifiers lack transparency and interpretability, which raises concern about model's reliability and hinders the wide deployment in real-word applications. While explainable methods have recently emerged, little has been done to explain the DL-based signal modulation classifiers. In this work, we propose a novel model-agnostic explainer, Model-Agnostic Signal modulation classification Explainer (MASE), which provides explanations for the predictions of black-box modulation classifiers. With the subsequence-based signal interpretable representation and in-distribution local signal sampling, MASE learns a local linear surrogate model to derive a class activation vector, which assigns importance values to the timesteps of signal instance. Besides, the constellation-based explanation visualization is adopted to spotlight the important signal features relevant to model prediction. We furthermore propose the first generic quantitative explanation evaluation framework for signal modulation classification to automatically measure the faithfulness, sensitivity, robustness, and efficiency of explanations. Extensive experiments are conducted on two real-world datasets with four black-box signal modulation classifiers. The quantitative results indicate MASE outperforms two state-of-the-art methods with 44.7% improvement in faithfulness, 30.6% improvement in robustness, and 44.1% decrease in sensitivity. Through qualitative visualizations, we further demonstrate the explanations of MASE are more human interpretable and provide better understanding into the reliability of black-box model decisions.

Robust reinforcement learning (RL) has been a challenging problem due to the gap between simulation and the real world. Existing efforts typically address the robust RL problem by solving a max-min problem. The main idea is to maximize the cumulative reward under the worst-possible perturbations. However, the worst-case optimization either leads to overly conservative solutions or unstable training process, which further affects the policy robustness and generalization performance. In this paper, we tackle this problem from both formulation definition and algorithm design. First, we formulate the robust RL as a max-expectation optimization problem, where the goal is to find an optimal policy under both the worst cases and the non-worst cases. Then, we propose a novel framework DRRL to solve the max-expectation optimization. Given our definition of the feasible tasks, a task generation and sequencing mechanism is introduced to dynamically output tasks at appropriate difficulty level for the current policy. With these progressive tasks, DRRL realizes dynamic multi-task learning to improve the policy robustness and the training stability. Finally, extensive experiments demonstrate that the proposed method exhibits significant performance on the unmanned CarRacing game and multiple high-dimensional MuJoCo environments.

Reward functions are critical hyperparameters with commercial values for individual or distributed reinforcement learning (RL), as slightly different reward functions result in significantly different performance. However, existing inverse reinforcement learning (IRL) methods can be utilized to approximate reward functions just based on collected expert trajectories through observing. Thus, in the real RL process, how to generate a polluted trajectory and perform an adversarial attack on IRL for protecting reward functions has become the key issue. Meanwhile, considering the actual RL cost, generated adversarial trajectories should be minimal and non-catastrophic for ensuring normal RL performance. In this work, we propose a novel approach to craft adversarial trajectories disguised as expert ones, for decreasing the IRL performance and realize the anti-IRL ability. Firstly, we design a reward clustering-based metric to integrate both advantages of fine- and coarse-grained IRL assessment, including expected value difference (EVD) and mean reward loss (MRL). Further, based on such metric, we explore an adversarial attack based on agglomerative nesting algorithm (AGNES) clustering and determine targeted states as starting states for reward perturbation. Then we employ the intrinsic fear model to predict the probability of imminent catastrophe, supporting to generate non-catastrophic adversarial trajectories. Extensive experiments of 7 state-of-the-art IRL algorithms are implemented on the Object World benchmark, demonstrating the capability of our proposed approach in (a) decreasing the IRL performance and (b) having minimal and non-catastrophic adversarial trajectories.

Socialnet becomes an important component in real life, drawing a lot of study issues of security and safety. Recently, for the features of graph structure in socialnet, adversarial attacks on node classification are exposed, and automatic attack methods such as fast gradient attack (FGA) and NETTACK are developed for per-node attacks, which can be utilized for multinode attacks in a sequential way. However, due to the overlook of perturbation influence between different per-node attacks, the above sequential method does not guarantee a global attack success rate for all target nodes, under a fixed budget of perturbation. In this paper, we propose a parallel adversarial attack framework on node classification. We redesign new loss function and objective function for nonconstraint and constraint perturbations, respectively. Through constructing intersection and supplement mechanisms of perturbations, we then integrate node filtering-based P-FGA and P-NETTACK in a unified framework, finally realizing parallel adversarial attacks. Experiments on politician socialnet dataset Polblogs with detailed analysis are conducted to show the effectiveness of our approach.

Adversarial example (AE), an input that is modified slightly to cause a machine learning system to produce erroneous outputs, has seen significant studies recently. Unfortunately, the fine data perturbation of AE ignores to keep potential data correlations between feature pairs. Thus, such AE will be easily filtered by configuring data correlations as basic filtering rules. In this paper, avoiding not to be filtered as well as causing false classification, an advanced robust AE generation attack is proposed. We first define four basic data correlations called strict linear constraint, approximate linear constraint, addition boundary constraint and zero multiplication constraint. Then, based on embedding multiple data correlations into one constraint matrix from the Pearson analysis, our approach can enable a Hadamard product of the constraint matrix and the sign of gradient matrix to craft perturbations, keeping consistent data correlations. Experimental results on intrusion detection system (IDS) indicate: 1) Nearly all AEs from original IFGSM are invalid by filtering according to basic data correlations; 2) In our method, AEs against a targeted DNN-based classifier can achieve an attack success rate of 99%, with transfer attack ability of 94% average success rate to attack other different mainstream classifiers.

With the rapid development of wireless communication technology and intelligent mobile devices, unmanned aerial vehicle (UAV) cluster is becoming increasingly popular in both civilian and military applications. Recently, a swarm intelligence-based UAV cluster study, aiming to enable efficient and autonomous collaboration, has drawn lots of interest. However, new security problems may be introduced with such swarm intelligence. In this work, we perform the first detailed security analysis to a kind of flocking-based UAV cluster with 5 policies, an upgrade version of the well-known Boids model. Targeting a realistic threat in a source-to-destination flying task, we design a data spoofing strategy and further perform complete vulnerability analysis. We reveal that such design and implementation are highly vulnerable. After breaking through the authentication of ad hoc on-demand distance vector (AODV) routing protocol by rushing attack, an attacker can masquerade as the first-arrival UAV within a specific scope of destination and generate data spoofing of arrival status to the following UAVs, so as to interfere with their normal flying paths of destination arrival and cause unexpected arrival delays amid urgent tasks. Experiments with detailed analysis from the 5-UAV cluster to the 10-UAV cluster are conducted to show specific feature composition-based attack effect and corresponding average delay. We also discuss promising defense suggestions leveraging the insights from our analysis.