Abstract: Despite extensive study on wireless sensor network
security, defending internal attacks and finding abnormal behaviour
of the sensor are still difficult and unsolved task. The conventional
cryptographic technique does not give the robust security or detection
process to save the network from internal attacker that cause by
abnormal behavior. The insider attacker or abnormally behaved
sensor identificationand location detection framework using false
massage detection and Time difference of Arrival (TDoA) is
presented in this paper. It has been shown that the new framework
can efficiently identify and detect the insider attacker location so that
the attacker can be reprogrammed or subside from the network to
save from internal attack.
Abstract: Intelligent Video-Surveillance (IVS) systems are
being more and more popular in security applications. The analysis
and recognition of abnormal behaviours in a video sequence has
gradually drawn the attention in the field of IVS, since it allows
filtering out a large number of useless information, which guarantees
the high efficiency in the security protection, and save a lot of human
and material resources. We present in this paper ADABeV, an
intelligent video-surveillance framework for event recognition in
crowded scene to detect the abnormal human behaviour. This
framework is attended to be able to achieve real-time alarming,
reducing the lags in traditional monitoring systems. This architecture
proposal addresses four main challenges: behaviour understanding in
crowded scenes, hard lighting conditions, multiple input kinds of
sensors and contextual-based adaptability to recognize the active
context of the scene.