Toward Learning Model-Agnostic Explanations for Deep Learning-Based Signal Modulation Classifiers

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

Adversarial attack and defense in reinforcement learning-from AI security view

Abstract Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks

Energy efficient sleep schedule with service coverage guarantee in wireless sensor networks

Service oriented architecture has been proposed to support collaborations among distributed wireless sensor network (WSN) applications in an open dynamic environment. However, WSNs are resource constraint, and have limited computation abilities, limited communication bandwidth and especially limited energy. Fortunately, sensor nodes in WSNs are usually deployed redundantly, which brings the opportunity to adopt a sleep schedule for balanced energy consumption to extend the network lifetime. Due to miniaturization and energy efficiency, one sensor node can integrate several sense units and support a variety of services. Traditional sleep schedule considers only the constraints from the sensor nodes, can be categorized to a one-layer (i.e., node layer) issue. The service oriented WSNs should resolve the energy optimization issue considering the two-layer constraints, i.e., the sensor nodes layer and service layer. Then, the one-layer energy optimization scheme in previous work is not applicable for service oriented WSNs. Hence, in this paper we propose a sleep schedule with a service coverage guarantee in WSNs. Firstly, by considering the redundancy degree on both the service level and the node level, we can get an accurate redundancy degree of one sensor node. Then, we can adopt fuzzy logic to integrate the redundancy degree, reliability and energy to get a sleep factor. Based on the sleep factor, we furthermore propose the sleep mechanism. The case study and simulation evaluations illustrate the capability of our proposed approach

Hierarchical workflow management in wireless sensor network

To build the service-oriented applications in a wireless sensor network (WSN), the workflow can be utilized to compose a set of atomic services and execute the corresponding pre-designed processes. In general, WSN applications rely closely on the sensor data which are usually inaccurate or even incomplete in the resource-constrained WSN. Then, the erroneous sensor data will affect the execution of atomic services and furthermore the workflows, which form an important part in the bottom-to-up dynamics of WSN applications. In order to alleviate this issue, it is necessary to manage the workflow hierarchically. However, the hierarchical workflow management remains an open and challenging problem. In this paper, by adopting the Bloom filter as an effective connection between the sensor node layer and the upper application layer, a hierarchical workflow management approach is proposed to ensure the QoS of workflow-based WSN application . The case study and experimental evaluations demonstrate the capability of the proposed approach

Recover fault services via complex service-to-node mappings in wireless sensor networks

With the motivation of seamlessly extending wireless sensor networks to the external environment, service-oriented architecture comes up as a promising solution. However, as sensor nodes are failure prone, this consequently renders the whole wireless sensor network to seriously faulty. When a particular node is faulty, the service on it should be migrated into those substitute sensor nodes that are in a normal status. Currently, two kinds of approaches exist to identify the substitute sensor nodes: the most common approach is to prepare redundancy nodes, though the involved tasks such as maintaining redundancy nodes, i.e., relocating the new node, lead to an extra burden on the wireless sensor networks. More recently, other approaches without using redundancy nodes are emerging, and they merely select the substitute nodes in a sensor node\u27s perspective i.e., migrating the service of faulty node to it\u27s nearest sensor node, though usually neglecting the requirements of the application level. Even a few work consider the need of the application level, they perform at packets granularity and don\u27t fit well at service granularity. In this paper, we aim to remove these limitations in the wireless sensor network with the service-oriented architecture. Instead of deploying redundancy nodes, the proposed mechanism replaces the faulty sensor node with consideration of the similarity on the application level, as well as on the sensor level. On the application level, we apply the Bloom Filter for its high efficiency and low space costs. While on the sensor level, we design an objective solution via the coefficient of a variation as an evaluation for choosing the substitute on the sensor level. © 2014 Springer Science+Business Media New York

Context-aware service ranking in wireless sensor networks

Wireless sensor networks (WSNs) are widely used in practice for comprehensively monitoring and gathering physical information via a multitude of sensors. As the development of WSNs, the integration of them with the external Internet is a urgent need. By wrapping the sensor functionality as a WSN service, the Web service is considered as the most promising technology to incorporate WSNs into the Internet. The quest for selecting the service with the best performance promotes service ranking technology. However, due to the dynamic WSN environment, traditional quality of service (QoS) based ranking approaches for general Web services are no longer suitable for the WSN service. In this article, in order to fit the characteristics of the WSN environment, we propose a context-aware WSN service ranking approach by aggregating the user rating and WSN service context. First, the User QoS Assessment(UQA) and Context QoS Assessment(CQA) are proposed, respectively. Then, through the performance influence on the WSN service by the variations in their context, a Fuzzy mechanism is further developed to aggregate the UQA and the CQA. Finally, the experiments are presented to confirm the validity of the proposed approach. © 2013 Springer Science+Business Media New York.Wireless sensor networks (WSNs) are widely used in practice for comprehensively monitoring and gathering physical information via a multitude of sensors. As the development of WSNs, the integration of them with the external Internet is a urgent need. By wrapping the sensor functionality as a WSN service, the Web service is considered as the most promising technology to incorporate WSNs into the Internet. The quest for selecting the service with the best performance promotes service ranking technology. However, due to the dynamic WSN environment, traditional quality of service (QoS) based ranking approaches for general Web services are no longer suitable for the WSN service. In this article, in order to fit the characteristics of the WSN environment, we propose a context-aware WSN service ranking approach by aggregating the user rating and WSN service context. First, the User QoS Assessment(UQA) and Context QoS Assessment(CQA) are proposed, respectively. Then, through the performance influence on the WSN service by the variations in their context, a Fuzzy mechanism is further developed to aggregate the UQA and the CQA. Finally, the experiments are presented to confirm the validity of the proposed approach. © 2013 Springer Science+Business Media New York