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In this paper we proposed new set of features to improve the detection ofthe Arabic spam web pages. These Features include: Global Popular Keywords (GPK) features, Character N-Gram Graph (CNGG)features and Sentence Level Frequent Words (SLFW) features. We denoted the new proposed set of features as features B in contrast to the state-of-art featured which denoted by features A. We combined our features (B) with the state-of-art features (A) to get features( AB) and then fed the resulting features AB into different classification algorithms include Ensemble Decision Tree with Bagging and Boosting ensemble methods, Decision Tree J48 , and Random Forest classifiers to achieve our results. In our results we achieved an F-measure of about 99.54% with the Random Forest classifier. We applied our new features on a dataset of about 15962 Arabic web pages which include spam and non-spam pages. We also compared our results with results of a previous study in the field of Arabic spam web pages and we found that, our results (F-measure of 99.64%) have outperformed their results (98%) with the same dataset they used in their study (Dataset 2010).

Improvement of Arabic Spam Web pages Detection Using New Robust Features