Deep learning for the analysis of network traffic measurements

The application of machine learning models to the analysis of network traffic measurements has largely increased in recent years. In the networking domain, shallow models are usually applied, where a set of expert handcrafted features are needed to fix the data before training. There are two main problems associated with this approach: firstly, it requires expert domain knowledge to select the input features, and secondly, different sets of custom-made input features are generally needed according to the specific target (e.g., network security, anomaly detection, traffic classification). On the other hand, the power of machine learning models using deep architectures (i.e., deep learning) for networking has not been yet highly explored. These models have had huge success in various domains, notably in computer vision, natural language processing, machine translation, and more recently in gaming. The main goal of this work is to explore the power of deep learning models to enhance the analysis of network tra c measurements. To this end, the specific problem of detection and classi cation of network attacks is studied. As a major advantage with respect to the state-of-the-art in the field, the evaluation of different raw-traffic input representations, including packet and ow-level ones, is considered. Different deep learning architectures are explored, including convolutional neural networks and long short-term memory recurrent neural networks as core layers. In addition, three different datasets are crafted from publicly available network traffic captures and used for calibrating the considered input representations, as well as training and validating the proposed models. Different deep learning models are compared to a random forest model - commonly accepted as a highly accurate model for network traffic analysis, using the same raw input representations. In the malware detection task, a detection accuracy of 77.6% and 98.5% was achieved for packet and ow input representations respectively. For the malware classification task, an overall accuracy of 76.5% was achieved. In all evaluation tasks, the proposed deep learning models outperform the random forest ones. These initial results suggest that deep learning can be used to enhance malware detection without requiring expert domain knowledge to handcraft input features, opening the door to a broad set of potential applications for deep learning in networking

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201

Text Classification With Deep Neural Networks

The thesis explores different extensions of Deep Neural Networks in learning underlying natural language representations and how to apply them in Natural Language Processing tasks. Novel methods of learning lower or higher level features of natural languages are given in which word and phrase dense representations are derived from unlabelled corpora. Word representations are learned by training Deep Neural Networks to predict context from each sentence while phrase representations are learned by unsupervised learning with Convolutional Restricted Boltzmann Machine. It is shown that word representations learned from architectures which preserve text input as sequences have better word similarity and relatedness than bag-of-word approaches. Additionally phrase representations learned with Convolutional Restricted Boltzmann Machine when combined with bag-of-word features improve results of text classification tasks over only bag-of-word features. Beside learning word and phrase representations, to the best of my knowledge, the work in the thesis is first to explore Deep Neural Networks in Adverse Drug Reaction detection task where my architectures when used with pre-trained word representations significantly outperform the state-of-the-art models. In addition, outputs from my proposed attentional architecture can be used to highlight important word spans without explicit training labels. In the future I propose the learned representations to be used with the discussed Deep Neural Networks in different NLP tasks such as Dialog Systems, Machine Translation or Natural Language Inference