Using tensorflow GradientTape, which you encountered in the exercise on variational autoencoders, we implement the FGSM below to generate adversarial images for this target model. Given source domain data and target domain data with different attacks, the model explores the common latent space, in which the original structure of the data is preserved while the discriminative examples are still far apart. Although all the methods had a lower F1 score in 10% attack data, HeTL and CeHTL boosted the F1 by 50% when adding another 10% of attack data, and the metric kept rising with increasing the attack data. There are several ways to determine the optimum hyper-parameters: (a) the similarity confidence can be determined by computing the similarity or distance between the source and target data, (b) the optimal number of both parameters can be found by enumerating the number of parameters, or (c) the parameters can be set empirically. . These experiments motivate our label-blind attack wherein the attacker has no knowledge of the victim networks architecture or parameters. Transfer learning, used in machine learning, is the reuse of a pre-trained model on a new problem. c Probe R2L, Study of parameter k sensitivity on the three main detection tasks, sample = 1000. a DoS Probe. At the core, transfer learning is using a deep learning model trained for one problem as a starting point to solve another. The algorithm can store and access knowledge. At the same time, in only studies, it is not considered that adding disturbance to the position of the image can improve the migration of . In a traditional machine learning model, the primary goal is to generalise to . Adv. Proceedings. To run the script you can download the data from the original source. demonstrate successful transfer attacks against a victim network using Basically, Transfer Learning is a technique that takes a pre-trained machine learning model and applies this model to an entirely new problem. Evolutionary algorithms for classification of malware families through different network behaviors (ACMNew York, 2014), pp. 193200. There already exists a network that is pre-trained on a similar task, which is usually trained on massive amounts of data. Both the data and problem domain on which the model has been trained is different from the new problem. IEEE Trans. H. Abdi, L. J. Williams, Principal component analysis. Next, its important to get a feeling of how our data looks like, so lets plot a few samples. HeTL can find new feature representations for source and target domain by transforming them on a common latent space. Training machine learning models can be a challenging data science tasks. on Machine Learning. In our previous work, we have proposed a transfer learning-enabled framework and approach, called HeTL, which can find the common latent subspace of two different attacks and learn an optimized representation, which was invariant to attack behaviors changes. It helps solve complex problems with pre-existing knowledge. Built In is the online community for startups and tech companies. on Knowledge Discovery and Data Mining. Performance comparison on heterogeneous spaces on DoS R2L. Conventional signature-based detection approaches may fail to address the increased variability of todays cyber attacks. Here, we describe our attack on transfer learning, beginning with the attack model. We assumed that attacks in a source domain are already known and labeled, and attacks in a target domain are new and different than the source. Transfer learning facilitates the training of task-specific classifiers using pre-trained models as feature extractors. Fortunately, deep learning can extract features automatically. c Probe R2L. 8188. SS contributed to the conception, experiment design, and evaluation of the proposed approach and results and helped draft the manuscript. One example of a common transfer learning problem is the ImageNet 1000 task, a massive dataset full of 1000 different classes of objects. If the model was trained on a large and generic enough dataset, the hope is that these intermediate feature maps would have picked relevant features required for the task. By combining their respective decisions, we illustrate that combining multiple detectors can further improve the detectability of adversarial . The . Stat.2(4), 433459 (2010). CeHTL achieved the best area under ROC curves (AUC) in 2 DoS Probe and Probe R2L (CeHTL 0.93 and 0.91 AUC vs. HeTL 0.82 and 0.65 AUC). We also do the same with a whitebox strategy as well as with random noise. Cookies policy. Notice that the output is extremely large. Sun et al. As a concept, it works by transferring as much knowledge as possible from an existing model to a new model designed for a similar task. From a practical standpoint, source and target domains can represent different or the same network environments with different attacks captured at different times and at separate instances. This experiment is to evaluate the proposed transfer learning approaches for detecting new variants of attacks. What are some example applications of transfer learning? B. Kulis, K. Saenko, T. Darrell, in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conf. This knowledge can be in various forms depending on the problem and the data. This work was supported by the DARPA GARD, DARPA QED4RML programs, and National Science Foundation DMS division. Similarly, a model trained for autonomous driving of cars can be used for autonomous driving of trucks. Cem's work in Hypatos was covered by leading technology publications like TechCrunch like Business Insider. Imagine you want to solve task A but dont have enough data to train a deep neural network. California Privacy Statement, This is very useful in the data science field since most real-world problems typically do not have millions of labeled data points to train such complex models. In the future, we aim to apply the model to various attack domains, such as malware detection. We then find a perturbation that causes these features to shift toward a centroid further away than the nearest class centroid. What Is Transfer Learning and It's Working. Neural networks are powerful tools for solving computer vision problems, but training them from scratch requires huge amounts of data and compute time [7, 19]. Optimized invariant representation of network traffic for detecting unseen malware variants (USENIX AssociationAustin, 2016), pp. For most of the values, the centroid-based attack outperforms the transferred PGD attack by a small amount; for =4/255 we see better performance from the transferred PGD attack. One way around this is to find a related task B with an abundance of data. 10, CeHTL shows a significant improvement and stays stable from 0, because the correspondence has been automatically computed and involved in the transfer learning, so should be set larger than 0. K. Bartos, M. Sofka, V. Franc, in USENIX Security 2016. We compared our transfer learning approach with the baselines. Another advantage of feature-based approaches is its flexibility to adopt different base classifiers according to different cases, which motivated us to derive a feature-based transfer learning approach for our network attack detection study. 807822. Our threat model assumes that the victims feature extractor is known to the attacker, but the last layer, including the class-label space, is unknown to the attacker. Wikipediadefines transfer learning as follows: Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. Deep learning models are vulnerable to attacks by adversarial examples. A new linear classifier head is then trained to classify images using a small task-specific dataset and the corresponding number of outputs. Advances in neural information processing systems, Fortify Machine Learning Production Systems: Detect and Classify The main idea is to learn the optimized representation to be invariant to the changes of attack behaviors from labeled training sets and non-labeled testing sets, which contain different types of attacks and feed the representation to a supervised classifier. In an attempt to detect those adversarial attacks, we design and implement multiple transfer learning-based adversarial detectors, each receiving a subset of the information passed through the IDS. B. In the feature mapping process of transfer learning, the loss of source domain information is very serious, which leads to the loss of knowledge in inverse mapping. classification head; we call these headless attacks. We find that targeted attacks, which target more likely classes, transfer more successfully than when the target classes are chosen as less likely labels. The framework consists of a machine learning pipeline, which includes the following stages: (i) extracting features from raw network traffic data, (ii) learning representations with feature-based transfer learning, and (iii) classification. She worked as an Associate Research Professor from 2012 to 2015 in Chinese Network Information Center, Chinese Academy of Sciences. Security 2019, 1 (2019). To use it for our binary classification task we should add an additional layer on top of it. DNN Design: The problem of insufficient labeled instances of zero-day attacks is circumvented with the phases of 'manifold alignment' and 'target soft labels generation' of the proposed framework. Abstract: The digital revolution has substantially changed our lives in which Internet-of-Things (IoT) plays a prominent role. Alternatively, changingand retraining different task-specific layers and the output layer is a method to explore. A significant advantage of our approach is its ability to identify an unknown attack that has not been previously investigated. Finally, we carried out the second experimental setting, where the source domain and target domain have different feature spaces. 2002 International Conference On, vol. Using these distances as synthetic logits we minimize the cross-entropy loss for the ground-truth class. This record was broken just after five days when a DDoS attack of size 1.7 Tbps was performed against a US based telecommunication company (Skottler, 2018). We present a family of transferable adversarial attacks against such classifiers, generated without access to the classification head; we call these headless attacks. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. 10. One category of methods in transfer-based black-box attack utilizes several image transformation operations to improve the transferability of adversarial examples, which is effective, but fails to take the specific characteristic of the input image into consideration. Results showed that proposed HeTL and CeHTL improved the performance remarkably. For better understanding, here is a figure showing how transfer learning differs from traditional machine learning methods: In some cases, data scientists may decide to create a new model to transfer its knowledge to the main task. http://dl.acm.org/citation.cfm?id=1283383.1283494. In this paper, we study the security vulnerabilities introduced by simple transfer learning strategies. It uses the knowledge of known threats in the source domain (labelled data) to detect . in which a pre-trained network (usually trained on a large labeled dataset like ImageNet) is used to extract low-dimensional features from images. What are its Use Cases & Benefits? For example, traffic feature is more distinguishable for DoS attack. We studied the impact of different parameter settings on the performance of detecting attacks. We hope this work raises awareness of such security vulnerabilities, and expect these results to encourage practitioners to avoid the practice of using publicly-available pre-trained networks for sensitive applications without adequate precautions. Performance comparison of accuracy on unknown network attacks detection, sample size = 1000, Performance comparison of F1 score on unknown network attacks detection, sample size = 1000, Performance comparison of ROC curves on the three transfer learning datasets. Such a model achieves 77.90% accuracy on the up-sampled CIFAR-10 test set. This is a realistic setting fine-tuning of classifiers on pre-trained networks is a widespread practice.
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