Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

State-of-the-art and gaps for deep learning on limited training data in remote sensing

Deep learning usually requires big data, with respect to both volume and variety. However, most remote sensing applications only have limited training data, of which a small subset is labeled. Herein, we review three state-of-the-art approaches in deep learning to combat this challenge. The first topic is transfer learning, in which some aspects of one domain, e.g., features, are transferred to another domain. The next is unsupervised learning, e.g., autoencoders, which operate on unlabeled data. The last is generative adversarial networks, which can generate realistic looking data that can fool the likes of both a deep learning network and human. The aim of this article is to raise awareness of this dilemma, to direct the reader to existing work and to highlight current gaps that need solving.Comment: arXiv admin note: text overlap with arXiv:1709.0030

Doctor of Philosophy

dissertationMachine learning is the science of building predictive models from data that automatically improve based on past experience. To learn these models, traditional learning algorithms require labeled data. They also require that the entire dataset fits in the memory of a single machine. Labeled data are available or can be acquired for small and moderately sized datasets but curating large datasets can be prohibitively expensive. Similarly, massive datasets are usually too huge to fit into the memory of a single machine. An alternative is to distribute the dataset over multiple machines. Distributed learning, however, poses new challenges as most existing machine learning techniques are inherently sequential. Additionally, these distributed approaches have to be designed keeping in mind various resource limitations of real-world settings, prime among them being intermachine communication. With the advent of big datasets machine learning algorithms are facing new challenges. Their design is no longer limited to minimizing some loss function but, additionally, needs to consider other resources that are critical when learning at scale. In this thesis, we explore different models and measures for learning with limited resources that have a budget. What budgetary constraints are posed by modern datasets? Can we reuse or combine existing machine learning paradigms to address these challenges at scale? How does the cost metrics change when we shift to distributed models for learning? These are some of the questions that have been investigated in this thesis. The answers to these questions hold the key to addressing some of the challenges faced when learning on massive datasets. In the first part of this thesis, we present three different budgeted scenarios that deal with scarcity of labeled data and limited computational resources. The goal is to leverage transfer information from related domains to learn under budgetary constraints. Our proposed techniques comprise semisupervised transfer, online transfer and active transfer. In the second part of this thesis, we study distributed learning with limited communication. We present initial sampling based results, as well as, propose communication protocols for learning distributed linear classifiers

Hierarchical Network with Label Embedding for Contextual Emotion Recognition

Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recognition, a label embedding matrix is trained by joint learning, which contributes to the final prediction. Comparison experiments are conducted on Chinese emotional corpus RenCECps, and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task

PiCoCo: Pixelwise Contrast and Consistency Learning for Semisupervised Building Footprint Segmentation

Building footprint segmentation from high-resolution remote sensing (RS) images plays a vital role in urban planning, disaster response, and population density estimation. Convolutional neural networks (CNNs) have been recently used as a workhorse for effectively generating building footprints. However, to completely exploit the prediction power of CNNs, large-scale pixel-level annotations are required. Most state-of-the-art methods based on CNNs are focused on the design of network architectures for improving the predictions of building footprints with full annotations, while few works have been done on building footprint segmentation with limited annotations. In this article, we propose a novel semisupervised learning method for building footprint segmentation, which can effectively predict building footprints based on the network trained with few annotations (e.g., only 0.0324 km2 out of 2.25-km2 area is labeled). The proposed method is based on investigating the contrast between the building and background pixels in latent space and the consistency of predictions obtained from the CNN models when the input RS images are perturbed. Thus, we term the proposed semisupervised learning framework of building footprint segmentation as PiCoCo, which is based on the enforcement of Pixelwise Contrast and Consistency during the learning phase. Our experiments, conducted on two benchmark building segmentation datasets, validate the effectiveness of our proposed framework as compared to several state-of-the-art building footprint extraction and semisupervised semantic segmentation methods

Fortschritte im unüberwachten Lernen und Anwendungsbereiche: Subspace Clustering mit Hintergrundwissen, semantisches Passworterraten und erlernte Indexstrukturen

Over the past few years, advances in data science, machine learning and, in particular, unsupervised learning have enabled significant progress in many scientific fields and even in everyday life. Unsupervised learning methods are usually successful whenever they can be tailored to specific applications using appropriate requirements based on domain expertise. This dissertation shows how purely theoretical research can lead to circumstances that favor overly optimistic results, and the advantages of application-oriented research based on specific background knowledge. These observations apply to traditional unsupervised learning problems such as clustering, anomaly detection and dimensionality reduction. Therefore, this thesis presents extensions of these classical problems, such as subspace clustering and principal component analysis, as well as several specific applications with relevant interfaces to machine learning. Examples include password guessing using semantic word embeddings and learning spatial index structures using statistical models. In essence, this thesis shows that application-oriented research has many advantages for current and future research.In den letzten Jahren haben Fortschritte in der Data Science, im maschinellen Lernen und insbesondere im unüberwachten Lernen zu erheblichen Fortentwicklungen in vielen Bereichen der Wissenschaft und des täglichen Lebens geführt. Methoden des unüberwachten Lernens sind in der Regel dann erfolgreich, wenn sie durch geeignete, auf Expertenwissen basierende Anforderungen an spezifische Anwendungen angepasst werden können. Diese Dissertation zeigt, wie rein theoretische Forschung zu Umständen führen kann, die allzu optimistische Ergebnisse begünstigen, und welche Vorteile anwendungsorientierte Forschung hat, die auf spezifischem Hintergrundwissen basiert. Diese Beobachtungen gelten für traditionelle unüberwachte Lernprobleme wie Clustering, Anomalieerkennung und Dimensionalitätsreduktion. Daher werden in diesem Beitrag Erweiterungen dieser klassischen Probleme, wie Subspace Clustering und Hauptkomponentenanalyse, sowie einige spezifische Anwendungen mit relevanten Schnittstellen zum maschinellen Lernen vorgestellt. Beispiele sind das Erraten von Passwörtern mit Hilfe semantischer Worteinbettungen und das Lernen von räumlichen Indexstrukturen mit Hilfe statistischer Modelle. Im Wesentlichen zeigt diese Arbeit, dass anwendungsorientierte Forschung viele Vorteile für die aktuelle und zukünftige Forschung hat