Driven by the substantial profits, the evolution of Portable Executable (PE) malware has posed persistent threats. PE malware classification has been an important research field, and numerous classification methods have been proposed. With the development of machine learning, learning-based static classification methods achieve excellent performance. However, most existing methods cannot meet the requirements of industrial applications due to the limited resource consumption and concept drift. In this article, we propose a fast, high-accuracy, and robust FCG-based PE malware classification method. We first extract precise function call relationships through code and data cross-referencing analysis. Then we normalize function names to construct a concise and accurate function call graph. Furthermore, we perform topological analysis of the function call graph using social network analysis techniques, thereby enhancing the program function call features. Finally, we use a series of machine learning algorithms for classification. We implement a prototype system named MalSensor and compare it with nine state-of-the-art static PE malware classification methods. The experimental results show that MalSensor is capable of classifying a malicious file in 0.7 seconds on average with up to 98.35% accuracy, which represents a significant advantage over existing methods.

Android malware detection has always been a hot research field. Prior work has validated that graph-based Android malware detection methods are effective, and several works have been proposed to regard the call graph of an app as a social network for more efficient classification. However, a social network contains many properties and there is a lack of perception as to which social network properties are more useful in differentiating malware from benign apps. Therefore, in this article, we present the first empirical study to analyze Android malware by different social network properties. We conduct extensive statistical analysis on 100 000 Android apps and apply three feature ranking methods to research the ability of 57 social network properties on malware detection. Moreover, in an effort to validate the effectiveness of these social network properties on malware detection, we implement a tool calledSNADroidby using these properties as features for models training and use it to complete classification. Our study reveals that theaverage triangles numberis the most impactful social network property in distinguishing malware from benign apps. Combined with the experimental results and in-depth analysis, we present the 15 most effective features for graph-based malware detection using social properties as a guideline.

Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) can lead to erroneous results, which greatly reduces the reliability and security of the DL tasks. Considering the wide application of modulation recognition in the communication field and the rapid development of DL, by adding a well-designed adversarial perturbation to the input signal, this article explores the performance of attack methods on modulation recognition, measures the effectiveness of adversarial attacks on signals, and provides the empirical evaluation of the reliabilities of CNNs. The results indicate that the accuracy of the target model reduce significantly by adversarial attacks, when the perturbation factor is 0.001, the accuracy of the model could drop by about 50% on average. Among them, iterative methods show greater attack performances than that of one-step method. In addition, the consistency of the waveform before and after the perturbation is examined, to consider whether the added adversarial examples are small enough (i.e., hard to distinguish by human eyes). This article also aims at inspiring researchers to further promote the CNNs reliabilities against adversarial attacks.

The discovery of adversarial examples poses a serious risk to the deep neural networks (DNN). By adding a subtle perturbation that is imperceptible to the human eye, a well-behaved DNN model can be easily fooled and completely change the prediction categories of the input samples. However, research on adversarial attacks in the field of modulation recognition mainly focuses on increasing the prediction error of the classifier, while ignores the importance of decreasing the perceptual invisibility of attack. Aiming at the task of DNNbased modulation recognition, this study designs the Fitting Difference as a metric to measure the perturbed waveforms and proposes a new method: the Nesterov Adam Iterative Method to generate adversarial examples. We show that the proposed algorithm not only exerts excellent white-box attacks but also can initiate attacks on a black-box model. Moreover, our method decreases the waveform perceptual invisibility of attacks to a certain degree, thereby reducing the risk of an attack being detected.

With the emergence of the information age, mobile data has become more random, heterogeneous and massive. Thanks to its many advantages, deep learning is increasingly applied in communication fields such as modulation recognition. However, recent studies show that the deep neural networks (DNN) is vulnerable to adversarial examples, where subtle perturbations deliberately designed by an attacker can fool a classifier model into making mistakes. From the perspective of an attacker, this study adds elaborate adversarial examples to the modulation signal, and explores the threats and impacts of adversarial attacks on the DNN-based modulation recognition in different environments. The results show that, regardless of a white-box or a black-box model, the adversarial attack can reduce the accuracy of the target model. Among them, the performance of the iterative attack is superior to the one-step attack in most scenarios. In order to ensure the invisibility of the attack (the waveform being consistent before and after the perturbations), an appropriate perturbation level is found without losing the attack effect. Finally, it is attested that the signal confidence level is inversely proportional to the attack success rate, and several groups of signals with high robustness are obtained.

Relevant physiological studies have revealed that the response of the classical receptive field (CRF) to visual stimuli could be suppressed by non‐CRF (nCRF) inhibition of the kernel in the primary visual cortex (V1). Based on this mechanism, many bio‐inspired contour detection models have been proposed, which are mainly achieved through CRF responses and nCRF surround inhibition calculation. In fact, the dynamic characteristics of neurons play an important role in contour detection in biological vision. Inspired by these visual mechanisms, the authors propose a contour detection model that emulates these dynamic characteristics. By introducing a multi‐bandwidth Gabor filter, according to the target image, they can effectively adjust the weight ratios of the filter to protect the contours and filter the background textures in the calculation of CRF responses. Additionally, they logarithmically modulate the nCRF inhibition kernel to make texture suppression more flexible and effective, thus improving the accuracy of detection algorithm as a whole. Compared with existing bio‐inspired contour detection models, the proposed model is more effective at contour detection, which will aid engineering applications that utilise pattern recognition in machine vision.