A survey on deep learning in medical image analysis

Medical Image Computing and Computer-Assisted Intervention

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Ultra Wideband Signals and Systems in Communication Engineering

Many lightweight authentication protocols based on the LPN (Learning Parity with Noise) problem have been proposed, the first of which is the HB protocol. In 2007, the HB-MP protocol was presented to overcome the vulnerabilities by means of internal rotations. Since then, new protocols have been presented to improve the HB-MP. In this paper, we present a general analysis of the HB-MP related protocols, including the cryptanalysis of HB-MP++, define design guidelines and propose a new protocol following the model.

Lightweight authentication protocols based on rotations and the lpn problem

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.

/pdf/secure-epc-gen2-compliant-radio-frequency-identification-10h0c851i1.pdf

Secure EPC Gen2 compliant Radio Frequency Identification.

Normal and aberrant cognitive functions are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for neurodegenerative disease evolution. Graph theory has been the standard tool to provide biomarkers in imaging connectomics showing the Alzheimer’s disease (AD). We propose a novel concept - graph signal processing - to analyze the evolution of disease graphs leading from mild cognitive impairment (MCI) to AD and derive frequency-based biomarkers representative for this disease. We show that high oscillations derived from the graph Fourier decomposition can provide important discriminatory information. To quantify the qualitative intuition of high oscillations, we use two concepts from signal theory: (1) zero crossings and (2) total variations. We apply these concepts on functional and structural brain connectivity networks for control (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) subjects. Our results applied to functional brain networks suggest that graph signal processing can accurately describe the frequencies of brain networks, and explain how AD is associated with low frequency and localized averaging confirmed by clinical results.

Graph signal processing to identify biomarkers in brain networks in dementia

Sundaresan et al proposed recently a novel ownership transfer protocol for multi-tag multi-owner RFID environments that complies with the EPC Class1 Generation2 standard. The authors claim that this provides individual-owner privacy and prevents tracking attacks. In this paper we show that this protocol falls short of its security objectives. We describe attacks that allow: a) an eavesdropper to trace a tag, b) the previous owner to obtain the private information that the tag shares with the new owner, and c) an adversary that has access to the data stored on a tag to link this tag to previous interrogations (forward-secrecy). We then analyze the security proof and show that while the first two cases can be solved with a more careful design, for lightweight RFID applications strong privacy remains an open problem.

/pdf/attacks-on-secure-ownership-transfer-for-multi-tag-multi-3ij5444uhc.pdf

Attacks on Secure Ownership Transfer for Multi-Tag Multi-Owner Passive RFID Environments.

Conventional authentication is a temporal action that takes place at a specific point in time During the period between this action and when the associated task(s) is (are) executed several events may occur that impact on the task(s), eg, an authenticated user may take a short break without logging out This is a vulnerability that may lead to exploits For applications where such exploits are a concern, authentication should be dynamic with a continuous monitoring loop, where trust is updated while the tasks associated with the authentication are executed Continuous user authentication addresses this issue by using biometric user traits to monitor user behavior In this paper we extend this notion for applications where monitoring mobile objects has to be a continuous process, eg, for liveness probing of unmanned aerial vehicles (UAVs), or to protect UAVs (with WiFi based UAVs an attacker may use a WiFi de-authentication attack to disconnect an authorized operator and then take control of the vehicle while the operator is trying to re-establish connectivity) We propose a lightweight stream authentication scheme for mobile objects that approximates continuous authentication This only requires the user and object to share a loosely synchronized pseudo-random number generator, and is provably secure

Jorge Munilla

Papers

Lightweight authentication protocols based on rotations and the lpn problem

Secure EPC Gen2 compliant Radio Frequency Identification.

Graph signal processing to identify biomarkers in brain networks in dementia

Attacks on Secure Ownership Transfer for Multi-Tag Multi-Owner Passive RFID Environments.

Lightweight Stream Authentication for Mobile Objects