Adversarial Deep Learning for Robust Detection of Binary Encoded Malware

Adversarial malware examples refer to the malwares that can evade the malware detector. Researching adversarial malware examples can help us find the vulnerability of malware detector and improve the defense ability of cyberspace. Considering the huge market share of android system, adversarial malware examples of android are studied in this paper. And an algorithm is proposed to find universal adversarial perturbations of malware. Such perturbation can be inserted the different malwares to generate adversarial examples. Then the effectiveness of algorithm is verified in the experiment. And three classic android malware detectors are used as targets. Experimental results show that universal adversarial perturbations for different machine learning models can be discovered via the proposed algorithm.KeywordsAdversarial malware examplesUniversal adversarial perturbationsAndroid platform

Generating adversarial examples via enhancing latent spatial features of benign traffic and preserving malicious functions

Many companies offer automatic speech recognition or Speech-to-Text APIs for use in diverse applications. However, audio classification algorithms trained with deep neural networks (DNNs) can sometimes misclassify adversarial examples, posing a significant threat to critical applications. In this paper, we present a novel way to create adversarial audio examples using a genetic algorithm. Our algorithm creates adversarial examples by iteratively adding perturbations to the original audio signal. Unlike most white-box adversarial example generations, our approach does not require knowledge about the target DNN's model and parameters (black-box) and heavy computational power of GPU resources. To show the feasibility of the proposed idea, we implement a tool called, Foolgle, using a genetic algorithm that performs untargeted attacks to create adversarial audio examples and evaluate those with the state-of-the-art Google Cloud Speech-to-Text API. Our preliminary experiment results show that Foolgle deceives the API with a success probability of 86%.

The automatic speech recognition (ASR) system based on deep neural network is easy to be attacked by an adversarial example due to the vulnerability of neural network, which is a hot topic in recent years. The adversarial example does harm to the ASR system, especially if the common-dependent ASR goes wrong, it will lead to serious consequences. To improve the robustness and security of the ASR system, the defense method against adversarial examples must be proposed. Based on this idea, we propose an algorithm of devastation and detection on adversarial examples which can attack the current advanced ASR system. We choose advanced text-dependent and command-dependent ASR system as our target system. Generating adversarial examples by the OPT on text-dependent ASR and the GA-based algorithm on command-dependent ASR. The main idea of our method is input transformation of the adversarial examples. Different random intensities and kinds of noise are added to the adversarial examples to devastate the perturbation previously added to the normal examples. From the experimental results, the method performs well. For the devastation of examples, the original speech similarity before and after adding noise can reach 99.68%, the similarity of the adversarial examples can reach 0%, and the detection rate of the adversarial examples can reach 94%.

State-of-the-art Deep Neural Networks (DNNs) are promoting medical image processing. However, DNNs are susceptible to adversarial attacks, which could significantly deteriorate model performances and pose a threat to clinical diagnoses. One common method for prevention is through adversarial training, which is highly dependent on harnessing adversarial examples during the training stage. However, adversarial medical examples generated by many existing works are too perceptible to be adversarial examples. To improve the imperceptibility, we proposed a Regional Saliency Map Attack that generates an adversarial example by only perturbing a small number of pixels. Extensive experiments have shown that, on average, our method caused the same degradation in model performance by quantitatively less perceptible perturbations. Visualisations have also verified that the improvement in imperceptibility in an image is both global and regional.

Neural networks provide excellent service on recognition tasks such as image recognition and speech recognition as well as for pattern analysis and other tasks in fields related to artificial intelligence. However, neural networks are vulnerable to adversarial examples. An adversarial example is a sample that is designed to be misclassified by a target model, although it poses no problem for recognition by humans, that is created by applying a minimal perturbation to a legitimate sample. Because the perturbation applied to the legitimate sample to create an adversarial example is optimized, the classification score for the target class has the characteristic of being similar to that for the legitimate class. This regularity occurs because minimal perturbations are applied only until the classification score for the target class is slightly higher than that for the legitimate class. Given the existence of this regularity in the classification scores, it is easy to detect an optimized adversarial example by looking for this pattern. However, the existing methods for generating optimized adversarial examples do not consider their weakness of allowing detectability by recognizing the pattern in the classification scores. To address this weakness, we propose an optimized adversarial example generation method in which the weakness due to the classification score pattern is removed. In the proposed method, a minimal perturbation is applied to a legitimate sample such that the classification score for the legitimate class is less than that for some of the other classes, and an optimized adversarial example is created with the pattern vulnerability removed. The results show that using 500 iterations, the proposed method can generate an optimized adversarial example that has a 100% attack success rate, with distortions of 2.81 and 2.23 for MNIST and Fashion-MNIST, respectively.

Remote sensing image (RSI) scene classification is the foundation and important technology of ground object detection, land use management and geographic analysis. During recent years, convolutional neural networks (CNNs) have achieved significant success and are widely applied in RSI scene classification. However, crafted images that serve as adversarial examples can potentially fool CNNs with high confidence and are hard for human eyes to interpret. For the increasing security and robust requirements of RSI scene classification, the adversarial example problem poses a serious problem for the classification results derived from systems using CNN models, which has not been fully recognized by previous research. In this study, to explore the properties of adversarial examples of RSI scene classification, we create different scenarios by testing two major attack algorithms (i.e., the fast gradient sign method (FGSM) and basic iterative method (BIM)) trained on different RSI benchmark datasets to fool CNNs (i.e., InceptionV1, ResNet and a simple CNN). In the experiment, our results show that CNNs of RSI scene classification are also vulnerable to adversarial examples, and some of them have a fooling rate of over 80%. These adversarial examples are affected by the architecture of CNNs and the type of RSI dataset. InceptionV1 has a fooling rate of less than 5%, which is lower than the others. Adversarial examples generated on the UCM dataset are easier than other datasets. Importantly, we also find that the classes of adversarial examples have an attack selectivity property. Misclassifications of adversarial examples of RSIs are related to the similarity of the original classes in the CNN feature space. Attack selectivity reveals potential classes of adversarial examples and provides insights into the design of defensive algorithms in future research.

Feature-filter: Detecting adversarial examples by filtering out recessive features

Adversarial examples are crucial tools for assessing the robustness of deep neural networks (DNNs) and revealing potential security vulnerabilities. Adversarial example generation methods based on Generative Adversarial Networks (GANs) have made significant progress in generating image adversarial examples, but still suffer from insufficient sparsity and transferability. To address these issues, this study proposes a novel semi-white-box untargeted adversarial example generation method named Wavelet-AdvGAN, with an explicit threat model defined as follows. Specifically, the attack is strictly untargeted without predefined target categories, aiming solely to mislead DNNs into classifying adversarial examples into any category other than the original label. It adopts a semi-white-box setting where attackers are denied access to the target model’s private information. Regarding the generator’s information dependence, the training phase only utilizes public resources (i.e., the target model’s public architecture and CIFAR-10 public training data), while the test phase generates adversarial examples through one-step feedforward of clean images without interacting with the target model. The method incorporates a Frequency Sub-band Difference (FSD) module and a Wavelet Transform Local Feature (WTLF) extraction module, evaluating the differences between original and adversarial examples from the frequency domain perspective. This approach constrains the magnitude of perturbations, reinforces feature regions, and further enhances the attack effectiveness, thereby improving the sparsity and transferability of adversarial examples. Experimental results demonstrate that the Wavelet-AdvGAN method achieves an average increase of 1.26% in attack success rates under two defense strategies—data augmentation and adversarial training. Additionally, the adversarial transferability improves by an average of 2.7%. Moreover, the proposed method exhibits a lower l0 norm, indicating better perturbation sparsity. Consequently, it effectively evaluates the robustness of deep neural networks.

Most deep neural networks are vulnerable to adversarial attacks, making it crucial to develop effective defenses against them. Adversarial examples are created by applying slight perturbations either to the entire image or specific regions, depending on attack methods. By contrast, benign examples typically lead models to focus attention on contiguous regions of the image during classification. Therefore, adversarial examples can be detected effectively by leveraging attention values. First, we conduct simple experiment to investigate how the model’s attention responds to adversarial perturbations. Second, we derive features by calculating the changes in attention values across ViT layers for adversarial and benign examples. These attention-derived features are then fed into the detection model, ResNet-50, to learn the differences in the activation locations between both examples. Third, we demonstrate the effectiveness of our approach by thorough experiments on various attack methods, datasets, and target models. The experiments prove the effectiveness of our approach, achieving superior performance compared to baseline methods on the ImageNet dataset across ViT-B and ViT-L. Our findings underscore the potential of utilizing attention-derived features as powerful indicators for enhancing the resilience of ViT models against adversarial perturbations.

Deep neural networks (DNNs) have useful applications in machine learning tasks involving recognition and pattern analysis. Despite the favorable applications of DNNs, these systems can be exploited by adversarial examples. An adversarial example, which is created by adding a small amount of noise to an original sample, can cause misclassification by the DNN. Under specific circumstances, it may be necessary to create a selective untargeted adversarial example that will not be classified as certain avoided classes. Such is the case, for example, if a modified tank cover can cause misclassification by a DNN, but the bandit equipped with the DNN must misclassify the modified tank as a class other than certain avoided classes, such as a tank, armored vehicle, or self-propelled gun. That is, selective untargeted adversarial examples are needed that will not be perceived as certain classes, such as tanks, armored vehicles, or self-propelled guns. In this study, we propose a selective untargeted adversarial example that exhibits 100% attack success with minimum distortions. The proposed scheme creates a selective untargeted adversarial example that will not be classified as certain avoided classes while minimizing distortions in the original sample. To generate untargeted adversarial examples, a transformation is performed to minimize the probability of certain avoided classes and distortions in the original sample. As experimental datasets, we used MNIST and CIFAR-10, including the Tensorflow library. The experimental results demonstrate that the proposed scheme creates a selective untargeted adversarial example that exhibits 100% attack success with minimum distortions (1.325 and 34.762 for MNIST and CIFAR-10, respectively).

We present a new algorithm to train a robust malware detector. Malware is a prolific problem and malware detectors are a front-line defense. Modern detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations to the malware code to decrease the chance of being detected whilst preserving the functionality and realism of the malware. Adversarial learning is effective in improving robustness but generating functional and realistic adversarial malware samples is non-trivial. Because: i) in contrast to tasks capable of using gradient-based feedback, adversarial learning in a domain without a differentiable mapping function from the problem space (malware code inputs) to the feature space is hard; and ii) it is difficult to ensure the adversarial malware is realistic and functional. This presents a challenge for developing scalable adversarial machine learning algorithms for large datasets at a production or commercial scale to realize robust malware detectors. We propose an alternative; perform adversarial learning in the feature space in contrast to the problem space. We prove the projection of perturbed, yet valid malware, in the problem space into feature space will always be a subset of adversarials generated in the feature space. Hence, by generating a robust network against feature-space adversarial examples, we inherently achieve robustness against problem-space adversarial examples. We formulate a Bayesian adversarial learning objective that captures the distribution of models for improved robustness. To explain the robustness of the Bayesian adversarial learning algorithm, we prove that our learning method bounds the difference between the adversarial risk and empirical risk and improves robustness. We show that Bayesian neural networks (BNNs) achieve state-of-the-art results; especially in the False Positive Rate (FPR) regime. Adversarially trained BNNs achieve state-of-the-art robustness. Notably, adversarially trained BNNs are robust against stronger attacks with larger attack budgets by a margin of up to 15% on a recent production-scale malware dataset of more than 20 million samples. Importantly, our efforts create a benchmark for future defenses in the malware domain.

As is known to all, deep neural networks have shown superior performance in many image classification tasks, but it is vulnerable to attack by adversarial examples, which are added tiny perturbations on the original examples to fool deep neural networks. Many previous defenses focused on mdifying model structure to enhance the robustness of the neural networks. In this paper, an image squeezing method proposed can help to classify images correctly, before inputing images to deep neural networks, image webp squeezng can destory the perturbation of adversarial examples. Firstly, it is necessary to generate corresponding adversarial examples of the neural networks, which can completely fool classifier, then apply webp squeezing to adversarial examples, and put them into the classifier to regain the correct image classification results. Our experiments on CIFAR-10 dataset demonstrated that webp image squeezing can help neural networsk classify adversarial examples correctly. And our method proposed in this paper can effectively resist FGSM, One-pixel attack and C&W attack, and its recognition accuracy is improved by more than 5% compared with the HGD method.

Recent years, Machine Learning has been widely used in malware analysis and achieved unprecedented success. However, deep learning models are found to be highly vulnerable to adversarial examples, which leads to the machine learning-based malware analysis methods vulnerable to malware makers. Exploring the attack algorithm can not only promote the generation of more effective malware analysis methods, but also can promote the development of the defense algorithm. Different machine learning models use different malware features as their classification basis, and accordingly there will be different attack methods against them. For malware visualization method, corresponding effective adversarial attack has not yet appeared. Most existing malware adversarial examples for malware visualization are generated at the feature level, and do not consider whether the generated adversarial examples can be executed and complete their original functions. In this paper, we explored how to modify an Android executable file without affecting its original functions and made it become an adversarial example. We proposed an executable adversarial examples attack strategy for machine learning-based malware visualization analysis. Experimental result shows that the executable adversarial examples we generated can be normally run on Android devices without affecting its original functions, and can confuse the malware family classifier with 93% success rate. We explored possible defense methods and hope to contribute to building a more robust malware classification method.

Face identification systems based on deep neural networks (DNNs) have been widely introduced in various areas. The key component of the systems is a feature extractor trained to map a face image into a feature space to distinguish a large number of unspecified individuals. Several works revealed that the feature extractor is vulnerable to carefully crafted adversarial examples. These works indicate that we could manipulate the outputs of the feature extractor by perturbing a source image. However, they might require large perturbations to ensure that the perturbed image is recognized incorrectly. This raises a question: how can we craft adversarial examples which leads miss-identification into a target identity with high-confidence and small perturbation? In this paper, to obtain adversarial examples with high confidence and small perturbation, we first introduce a condition which adversarial examples against the face identification systems should satisfy, then introduce a new method called GlassMasq to create adversarial examples based on the condition. In our evaluations, we demonstrate that our generated adversarial examples can deceive the face identification systems with higher confidence and at most 62.8% smaller perturbation than existing methods. The proposed method enables us to evaluate robustness of the face identification system against adversarial examples in a more appropriate manner.