This paper looks into the problem of detecting network anomalies by analyzing NetFlow records. While many previous works have used statistical models and machine learning techniques in a supervised way, such solutions have the limitations that they require large amount of labeled data for training and are unlikely to detect zero-day attacks. Existing anomaly detection solutions also do not provide an easy way to explain or identify attacks in the anomalous traffic. To address these limitations, we develop and present GEE, a framework for detecting and explaining anomalies in network traffic. GEE comprises of two components: (i) Variational Autoencoder (VAE) -an unsupervised deep-learning technique for detecting anomalies, and (ii) a gradient-based fingerprinting technique for explaining anomalies. Evaluation of GEE on the recent UGR dataset demonstrates that our approach is effective in detecting different anomalies as well as identifying fingerprints that are good representations of these various attacks.

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This article focuses on the problem of bandwidth allocation to users of Cloud data centers. An interesting approach is to use advance bandwidth reservation. Such systems usually assume all requests demand either bandwidth-guarantee (BG) or timeguarantee (TG), but not both. Hence the solutions are tailored for one type of requests. A BG request demands guarantee on bandwidth; whereas a TG request demands guarantee on time for transfer of data of specified volume. We define a new model that allows users to not only submit both kinds of requests, but also specify flexible demands. We tie up the problem of bandwidth allocation with differential pricing, that gives discounts to users based on the flexibility in their requests. We propose a two-phase, adaptive and flexible bandwidth allocator (A-FBA) that, in one phase admits and allocates minimal bandwidth to dynamically arriving user requests, and in another phase, allocates additional bandwidth for accepted requests maximizing revenue. The problem formulated in first phase is N P-hard, while the second phase can be solved in polynomial time. We show that, in comparison to a traditional deterministic model, the A-FBA not only increases the number of accepted requests significantly, but also does so by generating higher revenues.

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