Cross-domain vulnerability detection using graph embedding and domain adaptation

A major cause of security incidents such as cyber attacks is rooted in software vulnerabilities. These vulnerabilities should ideally be found and fixed before the code gets deployed. Machine learning-based approaches achieve state-of-the-art performance in capturing vulnerabilities. These methods are predominantly supervised. Their prediction models are trained on a set of ground truth data where the training data and test data are assumed to be drawn from the same probability distribution. However, in practice, the test data often differs from the training data in terms of distribution because they are from different projects or they differ in the types of vulnerability. In this paper, we present a new system for Cross Domain Software Vulnerability Discovery (CD-VulD) using deep learning (DL) and domain adaptation (DA). We employ DL because it has the capacity of automatically constructing high-level abstract feature representations of programs, which are likely of more cross-domain useful than the handcrafted features driven by domain knowledge. The divergence between distributions is reduced by learning cross-domain representations. Firstly, given software program representations, CD-VulD converts them into token sequences and learns the token embeddings for generalization across tokens. Next, CD-VulD employs a deep feature model to build abstract high-level presentations based on those sequences. Then, the metric transfer learning framework (MTLF) technique is employed to learn cross-domain representations by minimizing the distribution divergence between the source domain and the target domain. Finally, the cross-domain representations are used to build a classifier for vulnerability detection. Experimental results show that CD-VulD outperforms the state-of-the-art vulnerability detection approaches by a wide margin. We make the new datasets publicly available so that our work is replicable and can be further improved.

Owing to the ubiquity of computer software, software vulnerability detection (SVD) has become an important problem in the software industry and computer security. One of the most crucial issues in SVD is coping with the scarcity of labeled vulnerabilities in projects that require the laborious manual labeling of code by software security experts. One possible solution is to employ deep domain adaptation (DA) which has recently witnessed enormous success in transferring learning from structural labeled to unlabeled data sources. Generative adversarial network (GAN) is a technique that attempts to bridge the gap between source and target data in the joint space and emerges as a building block to develop deep DA approaches with state-of-the-art performance. However, deep DA approaches using the GAN principle to close the gap are subject to the mode collapsing problem that negatively impacts the predictive performance. Our aim in this paper is to propose Dual Generator-Discriminator Deep Code Domain Adaptation Network (Dual-GD-DDAN) for tackling the problem of transfer learning from labeled to unlabeled software projects in SVD to resolve the mode collapsing problem faced in previous approaches. The experimental results on real-world software projects show that our method outperforms state-of-the-art baselines by a wide margin.

PurposeThe study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.Design/methodology/approachIn this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.FindingsThe experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.Originality/valueExperiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

Data-driven fault classification methods are receiving great attention as they can be applied to many real-world applications. However, they work under the assumption that training data and testing data are drawn from the same distribution. Practical scenarios have varying operating conditions, which results in a domain-shift problem that significantly deteriorates the diagnosis performance. Recently, domain adaptation (DA) has been explored to address the domain-shift problem by transferring the knowledge from labeled source domain (e.g., source working condition) to unlabeled target domain (e.g., target working condition). Yet, all the existing methods are working under single-source single-target (1S1T) settings. Hence, a new model needs to be trained for each new target domain. This shows limited scalability in handling multiple working conditions since different models should be trained for different target working conditions, which is clearly not a viable solution in practice. To address this problem, we propose a novel adversarial multiple-target DA (AMDA) method for single-source multiple-target (1SmT) scenario, where the model can generalize to multiple-target domains concurrently. Adversarial adaptation is applied to transform the multiple-target domain features to be invariant from the single-source-domain features. This leads to a scalable model with a novel capability of generalizing to multiple-target domains. Extensive experiments on two public datasets and one self-collected dataset have demonstrated that the proposed method outperforms state-of-the-art methods consistently. Our source codes and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mohamedr002/AMDA</uri> .

This paper addresses the problem of land-cover map updating by classification of multitemporal remote-sensing images in the context of domain adaptation (DA). The basic assumptions behind the proposed approach are twofold. The first one is that training data (ground reference information) are available for one of the considered multitemporal acquisitions (source domain) whereas they are not for the other (target domain). The second one is that multitemporal acquisitions (i.e., target and source domains) may be characterized by different sets of classes. Unlike other approaches available in the literature, the proposed DA Bayesian classifier based on maximum a posteriori decision rule (DA-MAP) automatically identifies whether there exist differences between the set of classes in the target and source domains and properly handles these differences in the updating process. The proposed method was tested in different scenarios of increasing complexity related to multitemporal image classification. Experimental results on medium-resolution and very high resolution multitemporal remote-sensing data sets confirm the effectiveness and the reliability of the proposed DA-MAP classifier.

Robust and reliable drivetrain is important for preventing electromechanical (e.g., wind turbine) downtime. In recent years, advanced machine learning (ML) techniques including deep learning have been introduced to improve fault diagnosis performance for electromechanical systems. However, electromechanical systems (e.g., wind turbine) operate in varying working conditions, meaning that the distribution of the test data (in the target domain) is different from the training data used for model training, and the diagnosis performance of an ML method may become downgraded for practical applications. This paper proposes a joint distribution optimal deep domain adaptation approach (called JDDA) based auto-encoder deep classifier for fault diagnosis of electromechanical drivetrains under the varying working conditions. First, the representative features are extracted by the deep auto-encoder. Then, the joint distribution adaptation is used to implement the domain adaptation, so the classifier trained with the source domain features can be used to classify the target domain data. Lastly, the classification performance of the proposed JDDA is tested using two test-rig datasets, compared with three traditional machine learning methods and two domain adaptation approaches. Experimental results show that the JDDA can achieve better performance compared with the reference machine learning, deep learning and domain adaptation approaches.

Domain consistency regularization for unsupervised multi-source domain adaptive classification

Traditional machine learning methods rely on the training data and target data having the same feature space and data distribution. The performance may be unacceptable if there is a difference in data distribution between the training and target data, which is called cross-domain learning problem. In recent years, many domain adaptation methods have been proposed to solve this kind of problems and make much progress. However, existing domain adaptation approaches have a common assumption that the number of the data in source domain (labeled data) and target domain (unlabeled data) is matched. In this paper, the scenarios in real manufacturing site are considered, that the target domain data is much less than source domain data at the beginning, but the number of target domain data will increase as time goes by. A novel method is proposed for fault diagnosis of rolling bearing with online imbalanced cross-domain data. Finally, the proposed method which is tested on bearing dataset (CWRU) has achieved prediction accuracy of 95.89% with only 40 target samples. The results have been compared with other traditional methods. The comparisons show that the proposed online domain adaptation fault diagnosis method has achieved significant improvements. In addition, the deep transfer learning model by adaptive- network-based fuzzy inference system (ANFIS) is introduced to interpretation the results.

The automatic identification of entities like objects, people or their actions in visual data, such as images or video, has significantly improved, and is now being deployed in access control, social media, online retail, autonomous vehicles, and several other applications. This visual recognition capability leverages supervised learning techniques, which require large amounts of labeled training data from the target distribution representative of the particular task at hand. However, collecting such training data might be expensive, require too much time, or even be impossible. In this work, we introduce several novel approaches aiming at compensating for the lack of target training data. Rather than leveraging prior knowledge for building task-specific models, typically easier to train, we focus on developing general visual recognition techniques, where the notion of prior knowledge is better identified by additional information, available during training. Depending on the nature of such information, the learning problem may turn into domain adaptation (DA), domain generalization (DG), leaning using privileged information (LUPI), or domain adaptation with privileged information (DAPI).;When some target data samples are available and additional information in the form of labeled data from a different source is also available, the learning problem becomes domain adaptation. Unlike previous DA work, we introduce two novel approaches for the few-shot learning scenario, which require only very few labeled target samples, and even one can be very effective. The first method exploits a Siamese deep neural network architecture for learning an embedding where visual categories from the source and target distributions are semantically aligned and yet maximally separated. The second approach instead, extends adversarial learning to simultaneously maximize the confusion between source and target domains while achieving semantic alignment.;In complete absence of target data, several cheaply available source datasets related to the target distribution can be leveraged as additional information for learning a task. This is the domain generalization setting. We introduce the first deep learning approach to address the DG problem, by extending a Siamese network architecture for learning a representation of visual categories that is invariant with respect to the sources, while imposing semantic alignment and class separation to maximize generalization performance on unseen target domains.;There are situations in which target data for training might come equipped with additional information that can be modeled as an auxiliary view of the data, and that unfortunately is not available during testing. This is the LUPI scenario. We introduce a novel framework based on

Supervised learning methods require sufficient labeled examples to learn a good model for classification or regression. However, available labeled data are insufficient in many applications. Active learning (AL) and domain adaptation (DA) are two strategies to minimize the required amount of labeled data for model training. AL requires the domain expert to label a small number of highly informative examples to facilitate classification, while DA involves tuning the source domain knowledge for classification on the target domain. In this paper, we demonstrate how AL can efficiently minimize the required amount of labeled data for DA. Since the source and target domains usually have different distributions, it is possible that the domain expert may not have sufficient knowledge to answer each query correctly. We exploit our active DA framework to handle incorrect labels provided by domain experts. Experiments with multimedia data demonstrate the efficiency of our proposed framework for active DA with noisy labels.

As a branch of domain adaptation (DA), multi-source DA (MSDA) is a challenging issue that aims to transfer knowledge from multiple well-labeled source domains to a target domain for target tasks. However, most existing related works focus on single-target domain adaptation, and multiple target domain adaptation is not accounted for. We believe that multiple target domains provide valuable knowledge. Meanwhile, in multi-source and multi-target adaptation scenarios, feature generators with static parameters have difficulty generating deep features of each individual domain. In this paper, we propose a Dynamic Generator With Attention (DGWA) method for multi-source and multi-target domain adaptation to adapt domain-agnostic deep features in a multi-source and multi-target domain scenario. The feature generator with dynamic parameters can dynamically change its parameters with data input from different domains, which greatly improves the generalization of the feature pools. An attention mechanism is used in our DGWA to learn more transferable information from different domains. To demonstrate the performance of DGWA, we conduct extensive experiments on several popular domain adaptation datasets, including the digits, Office+Caltech10, Office-Home, and ImageCLEF-DA datasets. The experimental results demonstrate that our method performs better than state-of-the-art methods.

A unified framework for visual domain adaptation with covariance matching

Wheel condition assessment of high-speed trains under various operational conditions using semi-supervised adversarial domain adaptation

Joint predictive model and representation learning for visual domain adaptation

In recent years, smart contract vulnerability detection methods mostly view smart contract source code as natural language for processing, which cannot fully capture the semantic and structural features of the source code and has a high rate of false positives and missing positives. To improve the accuracy of vulnerability detection, this paper uses Graph Neural Network to obtain the semantic and structural information of the source code and Convolutional Neural Network to assist learning. We propose a graph neural network-based vulnerability detection model for smart contracts, which transforms smart contracts into control flow graphs, learns graph embedding using graph neural networks, and introduces Convolutional Neural Networks to learn the node order information of control flow graphs, and finally performs vulnerability detection using graph embedding and node order information. Experimenting on real datasets, our accuracy and F1 values are improved and the model can effectively detect smart contract vulnerabilities.