References of "State, Radu 50003137"
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See detailRule Compilation in Multi-Tenant Networks
Blaiech, Khalil; Hamadi, Salaheddine; Hommes, Stefan UL et al

in Rule Compilation in Multi-Tenant Networks (2017, May 18)

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See detailDetecting and predicting outages in mobile networks with log data.
Gurbani, Vijay K.; Kushnir, Dan; Mendiratta, Veena B. et al

in IEEE International Conference on Communications, ICC 2017 (2017, May)

Modern cellular networks are complex systems offering a wide range of services and present challenges in detecting anomalous events when they do occur. The networks are engineered for high reliability and ... [more ▼]

Modern cellular networks are complex systems offering a wide range of services and present challenges in detecting anomalous events when they do occur. The networks are engineered for high reliability and, hence, the data from these networks is predominantly normal with a small proportion being anomalous. From an operations perspective, it is important to detect these anomalies in a timely manner, to correct vulnerabilities in the network and preclude the occurrence of major failure events. The objective of our work is anomaly detection in cellular networks in near real-time to improve network performance and reliability. We use performance data from a 4G LTE network to develop a methodology for anomaly detection in such networks. Two rigorous prediction models are proposed: a non-parametric approach (Chi-Square test), and a parametric one (Gaussian Mixture Models). These models are trained to detect differences between distributions to classify a target distribution as belonging to a normal period or abnormal period with high accuracy. We discuss the merits between the approaches and show that both provide a more nuanced view of the network than simple thresh- olds of success/failure used by operators in production networks today. [less ▲]

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See detailOn non-parametric models for detecting outages in the mobile network
Falk, Eric UL; Camino, Ramiro Daniel UL; State, Radu UL et al

in Integrated Network and Service Management 2017 (2017, May)

The wireless/cellular communications network is composed of a complex set of interconnected computation units that form the mobile core network. The mobile core network is engineered to be fault tolerant ... [more ▼]

The wireless/cellular communications network is composed of a complex set of interconnected computation units that form the mobile core network. The mobile core network is engineered to be fault tolerant and redundant; small errors that manifest themselves in the network are usually resolved automatically. However, some errors remain latent, and if discovered early enough can provide warnings to the network operator about a pending service outage. For mobile network operators, it is of high interest to detect these minor anomalies near real-time. In this work we use performance data from a 4G-LTE network carrier to train two parameter-free models. A first model relies on isolation forests, and the second is histogram based. The trained models represent the data characteristics for normal periods; new data is matched against the trained models to classify the new time period as being normal or abnormal. We show that the proposed methods can gauge the mobile network state with more subtlety than standard success/failure thresholds used in real-world networks today. [less ▲]

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See detailAutomated labeling of unknown contracts in Ethereum
Norvill, Robert UL; Fiz Pontiveros, Beltran UL; State, Radu UL et al

in Computer Communication and Networks (ICCCN), 2017 26th International Conference on (2017)

Smart contracts have recently attracted interest from diverse fields including law and finance. Ethereum in particular has grown rapidly to accommodate an entire ecosystem of contracts which run using its ... [more ▼]

Smart contracts have recently attracted interest from diverse fields including law and finance. Ethereum in particular has grown rapidly to accommodate an entire ecosystem of contracts which run using its own crypto-currency. Smart contract developers can opt to verify their contracts so that any user can inspect and audit the code before executing the contract. However, the huge numbers of deployed smart contracts and the lack of supporting tools for the analysis of smart contracts makes it very challenging to get insights into this eco-environment, where code gets executed through transactions performing value transfer of a crypto-currency. We address this problem and report on the use of unsupervised clustering techniques and a seed set of verified contracts, in this work we propose a framework to group together similar contracts within the Ethereum network using only the contracts publicly available compiled code. We report qualitative and quantitative results on a dataset and provide the dataset and project code to the research community. [less ▲]

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See detailIdentifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
Glauner, Patrick UL; Dahringer, Niklas; Puhachov, Oleksandr et al

in Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017) (2017)

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging ... [more ▼]

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data due to the latter's propensity to suggest a large number of unnecessary inspections. In this paper, we propose a novel system that combines automated statistical decision making with expert knowledge. First, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data. The methodology used is specifically tailored to the level of noise in the data. Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. This work has resulted in appreciable results on a real-world data set of 3.6M customers. Our system is being deployed in a commercial NTL detection software. [less ▲]

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See detailChainGuard - A Firewall for Blockchain Applications using SDN with OpenFlow
Steichen, Mathis UL; Hommes, Stefan UL; State, Radu UL

in ChainGuard - A Firewall for Blockchain Applications using SDN with OpenFlow (2017)

Recently, blockchains have been gathering a lot of interest. Many applications can benefit from the advantages of blockchains. Nevertheless, applications with more restricted privacy or participation ... [more ▼]

Recently, blockchains have been gathering a lot of interest. Many applications can benefit from the advantages of blockchains. Nevertheless, applications with more restricted privacy or participation requirements cannot rely on public blockchains. First, the whole blockchain can be downloaded at any time, thus making the data available to the public. Second, anyone can deploy a node, join the blockchain network and take part in the consensus building process. Private and consortium blockchains promise to combine the advantages of blockchains with stricter requirements on the participating entities. This is also the reason for the comparably small number of nodes that store and extend those blockchains. However, by targeting specific nodes, an attacker can influence how consensuses are reached and possibly even halt the blockchain operation. To provide additional security to the blockchain nodes, ChainGuard utilizes SDN functionalities to filter network traffic, thus implementing a firewall for blockchain applications. ChainGuard communicates with the blockchain nodes it guards to determine which origin of the traffic is legitimate. Packets from illegitimate sources are intercepted and thus cannot have an effect on the blockchain. As is shown with experiments, ChainGuard provides access control functionality and can effectively mitigate flooding attacks from several sources at once. [less ▲]

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See detailConfirmation Delay Prediction of Transactions in the Bitcoin Network
Fiz Pontiveros, Beltran UL; Hommes, Stefan UL; State, Radu UL

in Advances in Computer Science and Ubiquitous Computing (2017)

Bitcoin is currently the most popular digital currency. It operates on a decentralised peer-to-peer network using an open source cryptographic protocol. In this work, we create a model of the selection ... [more ▼]

Bitcoin is currently the most popular digital currency. It operates on a decentralised peer-to-peer network using an open source cryptographic protocol. In this work, we create a model of the selection process performed by mining pools on the set of unconfirmed transactions and then attempt to predict if an unconfirmed transaction will be part of the next block by treating it as a supervised classification problem. We identified a vector of features obtained through service monitoring of the Bitcoin transaction network and performed our experiments on a publicly available dataset of Bitcoin transaction. [less ▲]

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See detailRecurrent Dynamical Projection for Time series-based Fraud detection
Antonelo, Eric Aislan UL; State, Radu UL

in ICANN 2017, Part II, LNCS 10614 (2017)

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See detailThe Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Glauner, Patrick UL; Meira, Jorge Augusto UL; Valtchev, Petko UL et al

in International Journal of Computational Intelligence Systems (2017), 10(1), 760-775

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science ... [more ▼]

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future. [less ▲]

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See detailDistilling Provider-Independent Data for General Detection of Non-Technical Losses
Meira, Jorge Augusto UL; Glauner, Patrick UL; State, Radu UL et al

in Power and Energy Conference, Illinois 23-24 February 2017 (2017)

Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in ... [more ▼]

Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in emerging countries. Developed countries, even though usually in smaller amounts, have to deal with NTL issues as well. In these countries the estimated annual losses are up to six billion USD. These facts have directed the focus of our work to the NTL detection. Our approach is composed of two steps: 1) We compute several features and combine them in sets characterized by four criteria: temporal, locality, similarity and infrastructure. 2) We then use the sets of features to train three machine learning classifiers: random forest, logistic regression and support vector vachine. Our hypothesis is that features derived only from provider-independent data are adequate for an accurate detection of non-technical losses. [less ▲]

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See detailThe Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
Glauner, Patrick UL; Du, Manxing UL; Paraschiv, Victor et al

in Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017) (2017)

Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we ... [more ▼]

Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research. [less ▲]

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See detailFinding Suspicious Activities in Financial Transactions and Distributed Ledgers
Camino, Ramiro Daniel UL; State, Radu UL; Montero, Leandro UL et al

in Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017) (2017)

Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in ... [more ▼]

Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in the popularity of novel financial technologies such as digital currencies, social trading platforms and distributed ledger payments, but there is a lack of approaches to enforce the aforementioned regulations accordingly. Software tools are developed to detect suspicious transactions usually based on knowledge from experts in the domain, but as new criminal tactics emerge, detection mechanisms must be updated. Suspicious activity examples are scarce or nonexistent, hindering the use of supervised machine learning methods. In this paper, we describe a methodology for analyzing financial information without the use of ground truth. A user suspicion ranking is generated in order to facilitate human expert validation using an ensemble of anomaly detection algorithms. We apply our procedure over two case studies: one related to bank fund movements from a private company and the other concerning Ripple network transactions. We illustrate how both examples share interesting similarities and that the resulting user ranking leads to suspicious findings, showing that anomaly detection is a must in both traditional and modern payment systems. [less ▲]

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See detailBotGM: Unsupervised Graph Mining to Detect Botnets in Traffic Flows
Lagraa, Sofiane UL; François, Jérôme; Lahmadi, Abdelkader et al

in CSNet 2017 Conference Proceedings (2017)

Botnets are one of the most dangerous and serious cybersecurity threats since they are a major vector of large-scale attack campaigns such as phishing, distributed denial-of-service (DDoS) attacks ... [more ▼]

Botnets are one of the most dangerous and serious cybersecurity threats since they are a major vector of large-scale attack campaigns such as phishing, distributed denial-of-service (DDoS) attacks, trojans, spams, etc. A large body of research has been accomplished on botnet detection, but recent security incidents show that there are still several challenges remaining to be addressed, such as the ability to develop detectors which can cope with new types of botnets. In this paper, we propose BotGM, a new approach to detect botnet activities based on behavioral analysis of network traffic flow. BotGM identifies network traffic behavior using graph-based mining techniques to detect botnets behaviors and model the dependencies among flows to traceback the root causes then. We applied BotGM on a publicly available large dataset of Botnet network flows, where it detects various botnet behaviors with a high accuracy without any prior knowledge of them. [less ▲]

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See detailDeep Learning on Big Data Sets in the Cloud with Apache Spark and Google TensorFlow
Glauner, Patrick UL; State, Radu UL

Scientific Conference (2016, December 09)

Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine ... [more ▼]

Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to selflearn feature hierarchies from the data rather than modeling hand-crafted features. It has proven to significantly improve performance in challenging data analytics problems. In this tutorial, we will first provide an introduction to the theoretical foundations of neural networks and Deep Learning. Second, we will demonstrate how to use Deep Learning in a cloud using a distributed environment for Big Data analytics. This combines Apache Spark and TensorFlow, Google’s in-house Deep Learning platform made for Big Data machine learning applications. Practical demonstrations will include character recognition and time series forecasting in Big Data sets. Attendees will be provided with code snippets that they can easily amend in order to analyze their own data. A related, but shorter tutorial focusing on Deep Learning on a single computer was given at the Data Science Luxembourg Meetup in April 2016. It was attended by 70 people making it the most attended event of this Meetup series in Luxembourg ever since its beginning. [less ▲]

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See detailBehavior Profiling for Mobile Advertising
Du, Manxing UL; State, Radu UL; Brorsson, Mats et al

in Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (2016, December)

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See detailInterpreting Finite Automata for Sequential Data
Hammerschmidt, Christian UL; Verwer, S.; Lin, Q. et al

in Interpretable Machine Learning for Complex Systems: NIPS 2016 workshop proceedings (2016)

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See detailEfficient Learning of Communication Profiles from IP Flow Records
Hammerschmidt, Christian UL; Marchal, Samuel; Pellegrino, Gaetano et al

Poster (2016, November)

The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information ... [more ▼]

The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information often comes with using simpler models on aggregated data (e.g. IP flow records) due to time and space constraints. A step towards utilizing IP flow records more effectively are stream learning techniques. We propose a method to collect a limited yet relevant amount of data in order to learn a class of complex models, finite state machines, in real-time. These machines are used as communication profiles to fingerprint, identify or classify hosts and services and offer high detection rates while requiring less training data and thus being faster to compute than simple models. [less ▲]

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See detailLoad Forecasting with Artificial Intelligence on Big Data
Glauner, Patrick UL; State, Radu UL

Scientific Conference (2016, October 09)

In the domain of electrical power grids, there is a particular interest in time series analysis using artificial intelligence. Machine learning is the branch of artificial intelligence giving computers ... [more ▼]

In the domain of electrical power grids, there is a particular interest in time series analysis using artificial intelligence. Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to self-learn feature hierarchies from the data rather than modeling hand-crafted features. It has proven to significantly improve performance in challenging signal processing problems. In this tutorial, we will first provide an introduction to the theoretical foundations of neural networks and Deep Learning. Second, we will demonstrate how to use Deep Learning for load forecasting with TensorFlow, Google’s in-house Deep Learning platform made for Big Data machine learning applications. The advantage of Deep Learning is that the results can easily be applied to other problems, such as detection of nontechnical losses. Attendees will be provided with code snippets that they can easily amend in order to perform analyses on their own time series. [less ▲]

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See detailFlexible State-Merging for learning (P)DFAs in Python
Hammerschmidt, Christian UL; Loos, Benjamin Laurent UL; Verwer, Sicco et al

Scientific Conference (2016, October)

We present a Python package for learning (non-)probabilistic deterministic finite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular ... [more ▼]

We present a Python package for learning (non-)probabilistic deterministic finite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular \texttt{scikit-learn} package, it is easy to use and new learning methods are easy to add. It provides PDFA learning as an additional tool for sequence prediction or classification to data scientists, without the need to understand the algorithm itself but rather the limitations of PDFA as a model. With applications of automata learning in diverse fields such as network traffic analysis, software engineering and biology, a stratified package opens opportunities for practitioners. [less ▲]

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See detailBehavioral Clustering of Non-Stationary IP Flow Record Data
Hammerschmidt, Christian UL; Marchal, Samuel; State, Radu UL et al

Poster (2016, October)

Detailed reference viewed: 118 (5 UL)