The evolving necessity of the Internet increases the demand on the bandwidth. Therefore, this demand opens the doors for the hackers' community to develop new methods and techniques to gain control over networking systems. Hence, the intrusion detection systems (IDS) are insufficient to prevent/detect unauthorized access the network. Network Intrusion Detection System (NIDS) is one example that still suffers from performance degradation due the increase of the link speed in today's networks. In This paper we proposed a novel algorithm to detect the intruders, who's trying to gain access to the network using the packets header parameters such as; source/destination address, source/destination port, and protocol without the need to inspect each packet content looking for signatures/patterns. However, the "Packet Header Matching" algorithm enhances the overall speed of the matching process between the incoming packet headers against the rule set. We ran the proposed algorithm to proof the proposed concept in coping with the traffic arrival speeds and the various bandwidth demands. The achieved results were of significant enhancement of the overall performance in terms of detection speed.
Introduction:
Stemming is an important preprocessing step in text classification, and could contribute in
increasing text classification accuracy. Although many works proposed stemmers for English language, few stemmers
were proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need is
vital to further improve Arabic text classification.
Method:
This work combined the use of the recently proposed P-Stemmer with various classifiers to find the optimal
classifier for the P-stemmer in term of Arabic text classification. As part of this work, a synthesized dataset was collected.
Result:
The previous experiments show that the use of P-Stemmer has a positive effect on classification. The degree of
improvement was classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the
experiments show that the best classifier with the P-Stemmer was NB. This is an interesting result as this classifier is wellknown for its fast learning and classification time.
Discussion:
First, the continuous improvement of the P-Stemmer by more optimization steps is necessary to further
improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing
the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient
Arabic datasets, especially large ones, is still an issue.
Conclusion:
In this work, an improved P-Stemmer was proposed by combining its use with various classifiers. In order to
evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various
online news pages. Next, the P-Stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, KNearest Neighbor, and K-Star.
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