FamilyGuard: A Security Architecture for Anomaly Detection in Home Networks.

The residential environment is constantly evolving technologically. With this evolution, sensors have become intelligent interconnecting home appliances, personal computers, and mobile devices. Despite the benefits of this interaction, these devices are also prone to security threats and vulnerabilities. Ensuring the security of smart homes is challenging due to the heterogeneity of applications and protocols involved in this environment. This work proposes the FamilyGuard architecture to add a new layer of security and simplify management of the home environment by detecting network traffic anomalies. Experiments are carried out to validate the main components of the architecture. An anomaly detection module is also developed by using machine learning through one-class classifiers based on the network flow. The results show that the proposed solution can offer smart home users additional and personalized security features using low-cost devices.

Electroencephalogram patterns with the presence of an unknown musical stimulus

The cerebral activity presents different behaviors in different situations and levels of consciousness, especially under musical stimulation. Signals of the central nervous system may disclose bioelectrical patterns, since listening to rhythmic sequences activates specific brain areas. In this paper, we analyze 42 neurologically normal Brazilian individuals, submitted to musical stimulation based on a procedure consisting of three different steps, during which the volunteer is kept with closed eyes. The first step is associated with the preliminary control silence period, without any stimulus, as the volunteer remains at rest. The second step consisted of unknown music stimulation. Finally, the third step involves post-music rest. Quantitative signal analysis computes the power spectrum time variations. Results point out stronger changes in gamma and high gamma waves (30 ­ 100 Hz). Even though the clinical rhythms (0 ­ 30 Hz) change throughout the whole period of the experiment, quantitative differences at gamma and high gamma bands are remarkably greater. Particularly, when comparing the initial silent period and the final post-stimulation silent one, bioelectrical differences are only highlighted by gamma and high gamma rhythms. In consequence, this paper points out that the EEG analysis of cognitive issues related to musical perception cannot disregard gamma and high gamma waves.