Intrusion detection (ID) is an interesting approach that could be used to improve the security of network systems. IDS detects suspected patterns of network traffic on the remaining open parts through monitoring user activities (runtime gathering of data from system operations), and the subsequent analysis of these activities. The purpose of this work is to contribute ideas of finding a solution to detect attacks (Intrusion) through building artificial detection system using feedforward neural networks to detect attacks with low false negative rate (which is the most important point), and low false positive rate. To do so, two feedforward neural networks architectures (one for non fuzzified data, the other for fuzzified data) are suggested, and their behaviors in detecting the attacks are studied. In this research, the suggested IDS not only has the ability to distinguish if the access is normal or attack, but also capable of distinguishing the attack type.

Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information (written in Arabic language) on the web. In most text classification processes, feature selection is crucial task since it highly affects the classification accuracy. Generally, two types of features could be used: Statistical based features and semantic and concept features. The main interest of this paper is to specify the most effective semantic and concept features on Arabic text classification process. In this study, two novel features that use lexical, semantic and lexico-semantic relations of Arabic WordNet (AWN) ontology are suggested. The first feature set is List of Pertinent Synsets (LoPS), which is list of synsets that have a specific relation with the original terms. The second feature set is List of Pertinent Words (LoPW), which is list of words that have a specific relation with the original terms. Fifteen different relations (defined in AWN ontology) are used with both proposed features. Naïve Bayes classifier is used to perform the classification process. The experimental results, which are conducted on BBC Arabic dataset, show that using LoPS feature set improves the accuracy of Arabic text classification compared with the well-known Bag-of-Word feature and the recent Bag-of-Concept (synset) features. Also, it was found that LoPW (especially with related-to relation) improves the classification accuracy compared with LoPS, Bagof-Word and Bag-of-Concept.

No abstract

No abstract

No abstract