Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201

Deep Learning Based Novelty Detection

Given a set of image instances from known classes, the goal of novelty detection is to determine whether an observed image during inference belongs to one of the known classes. In this thesis, deep learning-based approaches to solve novelty detection are studied under four different settings. In the first two settings, availability of out-of- distributional data (OOD) is assumed. With this assumption, novelty detection can be studied for cases where there are multiple known classes and a single known class separately. The thesis further explores this problem in a more constrained setting where only the data from known classes are considered for training. Finally, we study a practical application of novelty detection in mobile Active Authentication (AA) where latency and efficiency are as important as the detection accuracy

Active detection of age groups based on touch interaction

This paper studies user classification into children and adults according to their interaction with touchscreen devices. We analyse the performance of two sets of features derived from the Sigma-Lognormal theory of rapid human movements and global characterization of touchscreen interaction. We propose an active detection approach aimed to continuously monitorize the user patterns. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. We have used Support Vector Machines algorithm to classify the resulting features into age groups. The sets of features are fused at score level using data from smartphones and tablets. The results, with correct classification rates over 96%, show the discriminative ability of the proposed neuromotorinspired features to classify age groups according to the interaction with touch devices. In active detection setup, our method is able to identify a child using only 4 gestures in averageThis work was funded by the project CogniMetrics (TEC2015-70627-R) and Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017

A Survey on Spoofing and Selective Forwarding Attacks on Zigbee based WSN

The main focus of WSN is to gather data from the physical world. It is often deployed for sensing, processing as well as disseminating information of the targeted physical environments. The main objective of the WSN is to collect data from the target environment using sensors as well as transmit those data to the desired place of choice. In order to achieve an efficient performance, WSN should have efficient as well as reliable networking protocols. The most popular technology behind WSN is Zigbee. In this paper a pilot study is done on important security issues on spoofing and selective forwarding attack on Zigbee based WSN. This paper identifies the security vulnerabilities of Zigbee network and gaps in the existing methodologies to address the security issues and will help the future researchers to narrow down their research in WSN.Keywords: Zigbee, WSN, Protocol Stack, Spoofing and Selective Forwarding

Autenticación continua de usuario basada en interacción táctil

Hoy en día, con el auge continuo de la tecnología, cualquier aspecto relacionado con la seguridad adquiere un grado trascendental de importancia. Disponemos de información vital muy sensible en los nuevos dispositivos tecnológicos, ya sean ordenadores, tablets o smartphones. Dicha información debe ser protegida frente a cualquier usuario que no sea legítimo. Para ello, en los últimos años se han utilizado claves, tokens y otros métodos. La parte negativa es que muchos ofrecen un alto porcentaje de vulnerabilidad, además de ser soluciones difícilmente escalables a una vida diaria en la que debemos gestionar un elevado número de servicios y plataformas que requieren protección. Por lo tanto, el reconocimiento biométrico alcanza significativa importancia en este sector, ya que no solo obtiene grandes resultados de cara a proteger la información, sino que, haciendo uso de una parte única correspondiente a nosotros, elimina la necesidad de memorizar una combinación previa o portar un token determinado. Dentro del reconocimiento biométrico, existen diferentes métodos relacionados con cómo se evalúa y/o monitoriza la identidad del usuario. De especial interés para este trabajo es el denominado autenticación continua. Este procedimiento consiste en aplicar una serie de autenticaciones de usuario periódicas de cara a ofrecer mayor robustez, monitorizando de forma constante si el usuario que hace uso del dispositivo analizado es el correcto. En este trabajo realizado se reflejan detalladamente una serie de estudios y análisis sobre la autenticación de usuarios, focalizándose únicamente en dispositivos con pantalla táctil, en este caso smartphones. Para llevar a cabo este objetivo, se han utilizado medidas obtenidas previamente por diversas fuentes en diferentes bases de datos. Además, se ha hecho uso de algoritmos de clasificación de patrones basados en Máquinas de Vector Soporte y Modelos de Mezclas Gaussianas. Dichos algoritmos explotan la información discriminativa y estadística, para posteriormente combinar sus características mediante la fusión, mejorando de manera notoria los resultados obtenidos. Finalmente, se ha aplicado el algoritmo denominado Quickest Change Detection, el cual incrementa la eficacia del desarrollo en términos de latencia y probabilidad de falsa detección de usuarios. Esto se ha logrado teniendo en cuenta los resultados obtenidos anteriormente al instante en el que el usuario registra nuevos datos en la aplicación.Nowadays, with the continuous rise of technology, any aspect related to security acquires a transcendental degree of importance. We have vital information in the new technological devices, whether computers, tablets or smartphones. This information must be protected against any user that is not legitimate. For this, keys, tokens and other methods have been used in recent years. The negative part is that many offer a high percentage of vulnerability, in addition to being hard to scale solutions to a daily life in which we must manage a large number of services and platforms that require protection. Therefore, biometric recognition reaches significant importance in this sector, since it not only obtains great results in order to protect the information, but, making use of a unique part corresponding to us, eliminates the need to memorize a previous combination or carry a certain token. Within the biometric recognition, there are different methods related to how the identity of the user is evaluated and/or monitored. Of special interest for this work is the so-called continuous authentication. This procedure consists of applying a series of periodic user authentications in order to offer greater robustness, constantly monitoring if the user that makes use of the analyzed device is the correct one. In this work, a series of studies and analyzes on user authentication are reflected in detail, focusing only on touchscreen devices, in this case smartphones. To carry out this objective, previously obtained measurements have been used by different sources in different databases. In addition, pattern classification algorithms based on Vector Support Machines and Gaussian Mixture Models have been used. These algorithms exploit the discriminative and statistical information, to later combine their characteristics by means of fusion, improving in a noticeable way the obtained results. Finally, the algorithm called Quickest Change Detection has been applied, which increases the effectiveness of the development in terms of latency and the probability of false detection of users. This has been achieved by taking into account the results previously obtained at the moment in which the user registers new data in the application

Host Based Intrusion Detection for VANETs: A statistical approach to Rogue Node Detection

In this paper, an intrusion detection system (IDS) for vehicular ad hoc networks (VANETs) is proposed and evaluated. The IDS is evaluated by simulation in the presence of rogue nodes (RNs) that can launch different attacks. The proposed IDS is capable of detecting a false information attack using statistical techniques effectively and can also detect other types of attacks. First, the theory and implementation of the VANET model that is used to train the IDS is discussed. Then, an extensive simulation and analysis of our model under different traffic conditions is conducted to identify the effects of these parameters in VANETs. In addition, the extensive data gathered in the simulations are presented using graphical and statistical techniques. Moreover, RNs are introduced in the network, and an algorithm is presented to detect these RNs. Finally, we evaluate our system and observe that the proposed application-layer IDS based on a cooperative information exchange mechanism is better for dynamic and fast-moving networks such as VANETs, as compared with other techniques available

Requirements of a middleware for managing a large, heterogeneous programmable network

Programmable networking is an increasingly popular area of research in both industry and academia. Although most programmable network research projects seem to focus on the router architecture rather than on issues relating to the management of programmable networks, there are numerous research groups that have incorporated management middleware into the programmable network router software. However, none seem to be concerned with the effective management of a large heterogeneous programmable network. The requirements of such a middleware are outlined in this paper. There are a number of fundamental middleware principles that are addressed in this paper; these include management paradigms, configuration delivery, scalability and transactions. Security, fault tolerance and usability are also examined—although these are not essential parts of the middleware, they must be addressed if the programmable network management middleware is to be accepted by industry and adopted by other research projects

Detecting Rogue Nodes In Vehicular Ad-hoc Networks (DETER)

Vehicular ad hoc Networks (VANETs) are self-organizing networks of vehicles equipped with radios and processors. VANETs are very promising as they can make driving safer by improving road awareness through sharing of information from sensors. Vehicles communicate with each other wirelessly to exchange information and this exchange of information is susceptible to attacks of different kinds. There are some very important issues that need to be resolved before VANETs can be deployed on large scale. Security and privacy issues are undoubtedly the most important factors that need to be resolved. Amongst various problems to be solved in VANETs is the issue of rogue nodes and their impact on the network. This thesis discusses the problems associated with the security and privacy of vehicular networks in the presence of rogue nodes. The rogue nodes can share / inject false data in the network which can cause serious harm. The techniques proposed make VANETs secure and prevent them from the harmful impact of rogue nodes. The proposed work makes the network safer by making it fault tolerant and resilient in the presence of rogue nodes that can be detected and reported. As VANETs are highly dynamic and fast moving so, a data centric scheme is proposed that can determine if a node is rogue or not just by analysing its data. The work then enhances the developed mechanism by applying hypothesis testing and other statistical techniques to detect intrusions in the network by rogue nodes. The technique is simulated using OMNET++, SUMO and VACAMobil and the results obtained have been presented, discussed and compared to previous works. In order to prevent rogue nodes from becoming part of the VANETs this thesis also presents a novel framework for managing the digital identity in the vehicular networks. This framework authenticates the user and the vehicle separately from two authorities and allows him to communicate securely with the infrastructure using IBE (Identity Based Encryption). The proposed technique also preserves the privacy of the user. The proposed scheme allows traceability and revocation so that users can be held accountable and penalised. The results have been compared to previous works of similar nature. The thesis also discusses the Sybil attack and how to detect them using game theory in a VANET environment