Jorge Munilla

HB-MP: A further step in the HB-family of lightweight authentication protocols

A family of lightweight authentication protocols has been developed since Hopper and Blum proposed the HB protocol in 2001. In 2005, the HB^+ protocol was proposed as an improvement of the original HB to overcome the weakness against active attacks. Later, several authors have successfully applied new attacks to both HB and HB^+, resulting in a new modification known as HB^+^+. Again, this protocol has been cryptanalyzed and a new protocol has been presented by Piramuthu in 2006. This kind of protocol is especially suitable for RFID systems in which every tag has to be authenticated by the reader. Taking into account security and performance aspects, we present in this paper a new protocol, named HB-MP, derived from HB^+, providing a more efficient performance and resistance to the active attacks applied to the HB-family.

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimers Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimers disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.

Lightweight RFID authentication with forward and backward security

We propose a lightweight RFID authentication protocol that supports forward and backward security. The only cryptographic mechanism that this protocol uses is a pseudorandom number generator (PRNG) that is shared with the backend Server. Authentication is achieved by exchanging a few numbers (3 or 5) drawn from the PRNG. The lookup time is constant, and the protocol can be easily adapted to prevent online man-in-the-middle relay attacks. Security is proven in the UC security framework.

Secure EPC Gen2 Compliant Radio Frequency Identification

The increased functionality of EPC Class1 Gen2 (EPCGen2) is making this standard a de facto specification for inexpensive tags in the RFID industry. Recently three EPCGen2 compliant protocols that address security issues were proposed in the literature. In this paper we analyze these protocols and show that they are not secure and subject to replay/impersonation and statistical analysis attacks. We then propose an EPCGen2 compliant RFID protocol that uses the numbers drawn from synchronized pseudorandom number generators (RNG) to provide secure tag identification and session unlinkability. This protocol is optimistic and its security reduces to the (cryptographic) pseudorandomness of the RNGs supported by EPCGen2.

Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis.

Alzheimers Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the usefulness of the Sparse Gaussian Graphical models to reveal functional and structural connectivity patterns. This information provided by the sparse inverse covariance matrices is not only used in an exploratory way but we also propose a method to use it in a discriminative way. Regression coefficients are used to compute reconstruction errors for the different classes that are then introduced in a SVM for classification. Classification experiments performed using 68 Controls, 70 AD, and 111 MCI images and assessed by cross-validation show the effectiveness of the proposed method.

Off-line password-guessing attack to Peyravian-Jeffries's remote user authentication protocol

Recently, Peyravian and Jeffries [M. Peyravian, C. Jeffries, Secure remote user access over insecure networks, Computer Communications 29 (2006) 660-667] have proposed two set of protocols to perform remote user authentication and password change in a secure manner. The first set of protocols is based on hash functions, where no symmetric or asymmetric encryption scheme is applied. As Peyravian and Jeffries claim, these protocols suffer from an off-line password-guessing attack. They propose a second set of protocols based on Diffie-Hellman key agreement scheme to overcome the mentioned weakness. However, we show in this paper that this second set of protocols suffers also from the off-line password-guessing attack when a server impersonation attack is performed.

Attacks on a distance bounding protocol

Singelee and Preneel have recently proposed a enhancement of Hancke and Kuhns distance bounding protocol for RFID. The authors claim that their protocol offers substantial reductions in the number of rounds, though preserving its advantages: suitable to be employed in noisy wireless environments, and requiring so few resources to run that it can be implemented on a low-cost device. Subsequently, the same authors have also proposed it as an efficient key establishment protocol in wireless personal area networks. Nevertheless, in this paper we show effective relay attacks on this protocol, which dramatically increase the success probability of an adversary. As a result, the effectiveness of Singelee and Preneels protocol is seriously questioned.

Efficient anonymous authentication with key agreement protocol for wireless medical sensor networks

The use of wireless medical sensor networks (WMSN) in healthcare has led to a significant progress in this area. WMSN can sense patients’ vital signs and transmit sensed signals to health monitoring devices. Health professionals can monitor the status of patients. Confidentiality and patient privacy are the main concern for the WMSN in health care. Recently, He et al. proposed an authentication protocol for the healthcare applications using WMSN. In this paper, we show that He et al.’s scheme is insecure against various attacks. We also present an improved scheme. In the security analysis, we demonstrate that our scheme is secured against various attacks. We use the BAN logic to prove the correctness of the proposed scheme. As a result, the proposed protocol is practical for healthcare applications.

EPCGen2 Pseudorandom Number Generators: Analysis of J3Gen

This paper analyzes the cryptographic security of J3Gen, a promising pseudo random number generator for low-cost passive Radio Frequency Identification (RFID) tags. Although J3Gen has been shown to fulfill the randomness criteria set by the EPCglobal Gen2 standard and is intended for security applications, we describe here two cryptanalytic attacks that question its security claims: (i) a probabilistic attack based on solving linear equation systems; and (ii) a deterministic attack based on the decimation of the output sequence. Numerical results, supported by simulations, show that for the specific recommended values of the configurable parameters, a low number of intercepted output bits are enough to break J3Gen. We then make some recommendations that address these issues.

Cryptanalaysis of an EPCC1G2 Standard Compliant Ownership Transfer Scheme

Recently, Chen and Chien have proposed a novel ownership transfer scheme with low implementation costs and conforming to the EPC Class-1 Generation-2 standard. The authors claimed that the proposed scheme is able to resist all attacks, and hence it has better security and performance than its predecessors. However, in this paper we show that the protocol fails short of its security objectives, and it is even less secure than the previously proposed schemes. In fact, we describe several attacks which allow to recover all the secret information stored in the tag. Obviously, once this information is known, tags can be easily traced and impersonated.