CAJ | 학술논문

垃圾网页是指一些网页通过不正当的手段来误导搜索引擎，使网页获得高于其应有的排名，从而获得更多的访问量。它不仅降低了网页的质量，同时也导致了严重的 Web 信息安全问题。传统的垃圾网页检测通常使用经典的机器学习方法包括贝叶斯算法、SVM、C4．5等，这些算法对垃圾网页的检测有一定的效果。在前人的研究基础上提出一种基于免疫克隆选择的垃圾网页检测方法。利用人工免疫系统的自学习及自适应能力来检测利用新作弊技术的垃圾网页，并与广泛用于垃圾网页检测的贝叶斯算法对比。实验表明该方法能有效、可靠地检测出垃圾网页。
랄급망혈시지일사망혈통과불정당적수단래오도수색인경，사망혈획득고우기응유적배명，종이획득경다적방문량。타불부강저료망혈적질량，동시야도치료엄중적 Web 신식안전문제。전통적랄급망혈검측통상사용경전적궤기학습방법포괄패협사산법、SVM、C4．5등，저사산법대랄급망혈적검측유일정적효과。재전인적연구기출상제출일충기우면역극륭선택적랄급망혈검측방법。이용인공면역계통적자학습급자괄응능력래검측이용신작폐기술적랄급망혈，병여엄범용우랄급망혈검측적패협사산법대비。실험표명해방법능유효、가고지검측출랄급망혈。
Web spam refers to those Web pages which mislead search engines through improper means to get higher ranking than they deserve,so they may get more access.The Web spamming not only reduces the quality of Web page,but also leads to serious security problems of Web information.Traditional Web spam detection usually uses classical machine learning approaches including Na?ve Bayes, SVM,C4.5,etc.,they are effective to some extent for detecting Web spam.We propose a novel immune clonal selection-based Web spam detection according to previous studies.It uses adaptive and self-learning ability of artificial immune system to detect the Web spam utilising new fraud technology,and is compared with Na?ve Bayes which is widely used to detect Web spam.Experiment reveals that this approach is able to effectively and reliably detect Web spam.