Light-weight self-learning approach for URL classification - Hong-zhou SHA; Zhou ZHOU; Qing-yun LIU; Peng QIN (Tongxin xuebao)
A self-learning light-wight (SLW) is proposed.SLW is the first to introduce access relations and have the char-acteristics of feedback and self-learning.SLW approach starts from the seed set which includes known malicious pages.Then,it automatically figures out users with low credibility based on the seed set and the visit relation database.Finally,the access records of these users are used to identify other malicious pages.Experimental results indicate that SLW ap-proach can significantly improve the efficiency of malicious pages detection and reduce the average detection time com-pared with other conventional methods.