Enhanced depth imaging optical coherence tomography of congenital cavitary optic disc anomaly (CODA).

AIM To report the finding of extension of the 4th hyper-reflective band and retinal tissue into the optic disc in patients with cavitary optic disc anomalies (CODAs). METHODS In this observational study, 10 patients (18 eyes) with sporadic or autosomal dominant CODA were evaluated with enhanced depth imaging optical coherence tomography (EDI-OCT) and colour fundus images for the presence of 4th hyper-reflective band extension into the optic disc. RESULTS Of 10 CODA patients (18 eyes), five patients (8 eyes) showed a definite 4th hyper-reflective band (presumed retinal pigment epithelium (RPE)) extension into the optic disc. In these five patients (seven eyes), the inner retinal layers also extended with the 4th hyper-reflective band into the optic disc. Best corrected visual acuity ranged from 20/20 to 20/200. In three patients (four eyes), retinal splitting/schisis was present and in two patients (two eyes), the macula was involved. In all cases, the 4th hyper-reflective band extended far beyond the termination of the choroid into the optic disc. The RPE extension was found either temporally or nasally in areas of optic nerve head excavation, most often adjacent to peripapillary pigment. Compared with eyes without RPE extension, eyes with RPE extension were more myopic (mean dioptres -0.9±2.6 vs -8.8±5, p=0.043). CONCLUSIONS The RPE usually stops near the optic nerve border separated by a border tissue. With CODA, extension of this hyper-reflective band and retinal tissue into the disc is possible and best evaluable using EDI-OCT or analogous image modalities. Whether this is a finding specific for CODA, linked to specific gene loci or is also seen in patients with other optic disc abnormalities needs further evaluation.

An ecological approach to anomaly detection: the EIA Model.

The presented work proposes a new approach for anomaly detection. This approach is based on changes in a population of evolving agents under stress. If conditions are appropriate, changes in the population (modeled by the bioindicators) are representative of the alterations to the environment. This approach, based on an ecological view, improves functionally traditional approaches to the detection of anomalies. To verify this assertion, experiments based on Network Intrussion Detection Systems are presented. The results are compared with the behaviour of other bioinspired approaches and machine learning techniques.

PUE attack detection in CWSNs using anomaly detection techniques

Cognitive wireless sensor network (CWSN) is a new paradigm, integrating cognitive features in traditional wireless sensor networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in cognitive wireless sensor networks is an important problem since these kinds of networks manage critical applications and data. The specific constraints of WSN make the problem even more critical, and effective solutions have not yet been implemented. Primary user emulation (PUE) attack is the most studied specific attack deriving from new cognitive features. This work discusses a new approach, based on anomaly behavior detection and collaboration, to detect the primary user emulation attack in CWSN scenarios. Two non-parametric algorithms, suitable for low-resource networks like CWSNs, have been used in this work: the cumulative sum and data clustering algorithms. The comparison is based on some characteristics such as detection delay, learning time, scalability, resources, and scenario dependency. The algorithms have been tested using a cognitive simulator that provides important results in this area. Both algorithms have shown to be valid in order to detect PUE attacks, reaching a detection rate of 99% and less than 1% of false positives using collaboration.