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See detailA Tale of Location-Based User Authentication
Falk, Eric UL; Toth, Vendel; Knaff, Alexandre et al

in IEEE BigComp2019 - The 6th IEEE International Conference on Big Data and Smart Computing (2019)

The attitude towards passwords has drastically changed over the past years. Although they protected workstations from illicit access for decades, with today’s increased computational power, simple ... [more ▼]

The attitude towards passwords has drastically changed over the past years. Although they protected workstations from illicit access for decades, with today’s increased computational power, simple passwords became easy targets for attacks, whereas complex passwords are difficult to remember for the users. It appears as if the classical password protection has become obsolete and has to give way to similarly secured schemes, which are seamless for users. Novel methodologies may be sound and secure from a technical point of view, their success will be challenged by the simple question whether a user feels secure or not. In this work, we propose a proximity based login and session locking scheme, based on bluetooth beacons. We describe the big data architecture required to implement secured location-based services in smart buildings. To round our contribution out, we describe a medium scale user study with 40 participants, conducted to answer the question: Do users feel secure? [less ▲]

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See detailUser-Device Authentication in Mobile Banking using APHEN for Paratuck2 Tensor Decomposition
Charlier, Jérémy Henri J. UL; Falk, Eric UL; State, Radu UL et al

in Proceedings of 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (2018)

The new financial European regulations such as PSD2 are changing the retail banking services. Noticeably, the monitoring of the personal expenses is now opened to other institutions than retail banks ... [more ▼]

The new financial European regulations such as PSD2 are changing the retail banking services. Noticeably, the monitoring of the personal expenses is now opened to other institutions than retail banks. Nonetheless, the retail banks are looking to leverage the user-device authentication on the mobile banking applications to enhance the personal financial advertisement. To address the profiling of the authentication, we rely on tensor decomposition, a higher dimensional analogue of matrix decomposition. We use Paratuck2, which expresses a tensor as a multiplication of matrices and diagonal tensors, because of the imbalance between the number of users and devices. We highlight why Paratuck2 is more appropriate in this case than the popular CP tensor decomposition, which decomposes a tensor as a sum of rank-one tensors. However, the computation of Paratuck2 is computational intensive. We propose a new APproximate HEssian-based Newton resolution algorithm, APHEN, capable of solving Paratuck2 more accurately and faster than the other popular approaches based on alternating least square or gradient descent. The results of Paratuck2 are used for the predictions of users' authentication with neural networks. We apply our method for the concrete case of targeting clients for financial advertising campaigns based on the authentication events generated by mobile banking applications. [less ▲]

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See detailMind Your Dependencies for Semantic Query Optimization
Pena, Eduardo H. M.; Falk, Eric UL; Meira, Jorge Augusto UL et al

in Journal of Information and Data Management (2018)

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See detailVSOC - A Virtual Security Operating Center
Falk, Eric UL; Fiz Pontiveros, Beltran UL; Repcek, Stefan et al

in Global Communications (2017)

Security in virtualised environments is becoming increasingly important for institutions, not only for a firm’s own on-site servers and network but also for data and sites that are hosted in the cloud ... [more ▼]

Security in virtualised environments is becoming increasingly important for institutions, not only for a firm’s own on-site servers and network but also for data and sites that are hosted in the cloud. Today, security is either handled globally by the cloud provider, or each customer needs to invest in its own security infrastructure. This paper proposes a Virtual Security Operation Center (VSOC) that allows to collect, analyse and visualize security related data from multiple sources. For instance, a user can forward log data from its firewalls, applications and routers in order to check for anomalies and other suspicious activities. The security analytics provided by the VSOC are comparable to those of commercial security incident and event management (SIEM) solutions, but are deployed as a cloud-based solution with the additional benefit of using big data processing tools to handle large volumes of data. This allows us to detect more complex attacks that cannot be detected with todays signature-based (i.e. rules) SIEM solutions. [less ▲]

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See detailBig Data Architectures For Robust Systems
Falk, Eric UL

Doctoral thesis (2017)

Monitoring robustness of critical systems/infrastructures is the major use case for anomaly detection. A robust system designates a structure not only safe against intentional attacks, but also capable of ... [more ▼]

Monitoring robustness of critical systems/infrastructures is the major use case for anomaly detection. A robust system designates a structure not only safe against intentional attacks, but also capable of stemming internal failures. These systems face two primary risks: cyber attacks fall into the first category, whereas failing hardware components are part of the second category. In both cases, fast decision making is crucial. Hence, streaming data processing is the decisive asset to consider. With this background, in this thesis, we investigate two scenarios from the fields of mobile network sanity monitoring and cyber-physical security. Our contribution is threefold: We display how the real-time requirements of the two use cases push existing frameworks to their utter limits; We show which anomaly detection methods can be used to facilitate instant assessment rendering; We blueprint the extensions we contributed to big data frameworks, which are powering major silicon valley companies, to make them capable of supporting our use cases. The data-sets issued by our monitoring systems yield different properties than data from internet companies such as Google, Facebook or LinkedIn. In this work we establish our use cases, illustrate the mathematical models employed for the decision taking, and examine how big data architectures have to be altered to support our scenarios. [less ▲]

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See detailYour Moves, Your Device: Establishing Behavior Profiles Using Tensors
Falk, Eric UL; Charlier, Jérémy Henri J. UL; State, Radu UL

in Advanced Data Mining and Applications - 13th International Conference, ADMA 2017 (2017, November)

Smartphones became a person's constant companion. As the strictly personal devices they are, they gradually enable the replacement of well established activities as for instance payments, two factor ... [more ▼]

Smartphones became a person's constant companion. As the strictly personal devices they are, they gradually enable the replacement of well established activities as for instance payments, two factor authentication or personal assistants. In addition, Internet of Things (IoT) gadgets extend the capabilities of the latter even further. Devices such as body worn fitness trackers allow users to keep track of daily activities by periodically synchronizing data with the smartphone and ultimately with the vendor's computational centers in the cloud. These fitness trackers are equipped with an array of sensors to measure the movements of the device, to derive information as step counts or make assessments about sleep quality. We capture the raw sensor data from wrist-worn activity trackers to model a biometric behavior profile of the carrier. We establish and present techniques to determine rather the original person, who trained the model, is currently wearing the bracelet or another individual. Our contribution is based on CANDECOMP/PARAFAC (CP) tensor decomposition so that computational complexity facilitates: the execution on light computational devices on low precision settings, or the migration to stronger CPUs or to the cloud, for high to very high granularity. This precision parameter allows the security layer to be adaptable, in order to be compliant with the requirements set by the use cases. We show that our approach identifies users with high confidence. [less ▲]

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See detailQuery-able Kafka: An agile data analytics pipeline for mobile wireless networks
Falk, Eric UL; Gurbani, Vijay K.; State, Radu UL

in Proceedings of the 43rd International Conference on Very Large Data Bases 2017 (2017, August), 10

Due to their promise of delivering real-time network insights, today's streaming analytics platforms are increasingly being used in the communications networks where the impact of the insights go beyond ... [more ▼]

Due to their promise of delivering real-time network insights, today's streaming analytics platforms are increasingly being used in the communications networks where the impact of the insights go beyond sentiment and trend analysis to include real-time detection of security attacks and prediction of network state (i.e., is the network transitioning towards an outage). Current streaming analytics platforms operate under the assumption that arriving traffic is to the order of kilobytes produced at very high frequencies. However, communications networks, especially the telecommunication networks, challenge this assumption because some of the arriving traffic in these networks is to the order of gigabytes, but produced at medium to low velocities. Furthermore, these large datasets may need to be ingested in their entirety to render network insights in real-time. Our interest is to subject today's streaming analytics platforms --- constructed from state-of-the art software components (Kafka, Spark, HDFS, ElasticSearch) --- to traffic densities observed in such communications networks. We find that filtering on such large datasets is best done in a common upstream point instead of being pushed to, and repeated, in downstream components. To demonstrate the advantages of such an approach, we modify Apache Kafka to perform limited \emph{native} data transformation and filtering, relieving the downstream Spark application from doing this. Our approach outperforms four prevalent analytics pipeline architectures with negligible overhead compared to standard Kafka. [less ▲]

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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 ▲]

Detailed reference viewed: 87 (8 UL)