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Articles by M.N. Sulaiman
Total Records ( 2 ) for M.N. Sulaiman
  S.C. Ng , M.N. Sulaiman and M.H. Selamat
  This study discusses the implementation of machine learning approach in negotiation agents that can learn their opponent’s preferences and constraints during one-to-many negotiations. A novel mechanism in learning negotiation is introduced. The genetic-based model of multi-attribute one-to-many negotiation, namely GA Improved-ITA is proposed. The GA Improved-ITA agents first utilize Genetic-Based Machine Learning (GBML) to identify their opponent’s preferable negotiation issues. It is then followed by branch and bound search to search for the best value for each of the issues. The performance of GA Improved-ITA is promising when it is compared with the results of one-to-many negotiations obtained by Bayesian learning model and heuristic search algorithm.
  Z. Muda , W. Yassin , M.N. Sulaiman and N.I. Udzir
  Intrusion Detection Systems (IDS) have become an important building block of any sound defense network infrastructure. Malicious attacks have brought more adverse impacts on the networks than before, increasing the need for an effective approach to detect and identify such attacks more effectively. In this study two learning approaches, K-Means Clustering and Naïve Bayes classifier (KMNB) are used to perform intrusion detection. K-Means is used to identify groups of samples that behave similarly and dissimilarly such as malicious and non-malicious activity in the first stage while Naïve Bayes is used in the second stage to classify all data into correct class category. Experiments were performed with KDD Cup ‘99 data sets. The experimental results show that KMNB significantly improved and increased the accuracy, detection rate and false alarm of single Naïve Bayes classifier up to 99.6, 99.8 and 0.5%.
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