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Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud's bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.

Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels. In contrast, a universal perturbation is an update that can be added to any image in a broad class of images, while still changing the predicted class label. We study the efficient generation of universal adversarial perturbations, and also efficient methods for hardening networks to these attacks. We propose a simple optimization-based universal attack that reduces the top-1 accuracy of various network architectures on ImageNet to less than 20%, while learning the universal perturbation 13× faster than the standard method.To defend against these perturbations, we propose universal adversarial training, which models the problem of robust classifier generation as a two-player min-max game, and produces robust models with only 2× the cost of natural training. We also propose a simultaneous stochastic gradient method that is almost free of extra computation, which allows us to do universal adversarial training on ImageNet.

Association Link Network (ALN) aims to establish associated relations among various resources. By extending the hyperlink network World Wide Web to an association-rich network, ALN is able to effectively support Web intelligence activities such as Web browsing, Web knowledge discovery, and publishing, etc. Since existing methods for building semantic link on Web resources cannot effectively and automatically organize loose Web resources, effective Web intelligence activities are still challenging. In this paper, a discovery algorithm of associated resources is first proposed to build original ALN for organizing loose Web resources. Second, three schemas for constructing kernel ALN and connection-rich ALN (C-ALN) are developed gradually to optimize the organizing of Web resources. After that, properties of different types of ALN are discussed, which show that C-ALN has good performances to support Web intelligence activities. Moreover, an evaluation method is presented to verify the correctness of C-ALN for semantic link on documents. Finally, an application using C-ALN to organize Web services is presented, which shows that C-ALN is an effective and efficient tool for building semantic link on the resources of Web services. Note to Practitioners-Association Link Network (ALN) aims to establish associated relations among various resources. By extending the hyperlink network World WideWeb to an association-rich network, ALN is able to effectively support Web intelligence activities such as Web browsing, Web knowledge discovery and publishing, etc. Since existing methods for building semantic link on Web resources cannot effectively and automatically organize loose Web resources, effective Web intelligence activities are still challenging. A discovery algorithm of associated resources is first proposed by us to build original ALN for organizing loose Web resources. The properties of different types of ALN are discussed, which show that C-ALN has good performances to support Web intelligence activities. An application using C-ALN to organize Web services is presented, which shows that C-ALN is an effective and efficient tool for building semantic link on the resources of Web services.Index Terms-Association link network (ALN), intelligent browsing, interactive computing, knowledge discovery, semantic Web.

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Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.

Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-andexcitation blocks, multi-map transfer, and max-min pooling for classifying thoracic diseases as well as localizing suspicious lesion regions. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of proposed model and its better performance against the state-of-the-art pipelines. CCS CONCEPTS• Theory of computation → Machine learning theory; • Applied computing → Imaging; KEYWORDS Chest X-ray, computer-aided diagnosis, weakly supervised learning ACM Reference Format: