Performing Joint Learning for Passive Intrusion Detection in Pervasive Wireless Environments
Recent years have witnessed increasing interests in passive intrusion detection for wireless environments, e.g., asset protection in industrial facilities and emergency rescue of trapped people. Most previous studies have focused primarily on exploiting a single intrusion indicator, such as moving variance, for capturing an intrusion pattern at a time. However, in real-world, there are many intrusion patterns which may be only detectable by combining different intrusion indicators and performing detection jointly. To this end, the authors propose a joint intrusion learning approach, which has the ability in combining the detection power of several complementary intrusion indicators and detects different intrusion patterns at the same time.