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We introduced a three-tier architecture of intrusion detection system which consists of a blacklist, a whitelist and a multi-class support vector machine classifier. The first tier is the blacklist that will filter out the known attacks from the traffic and the whitelist identifies the normal traffics. The rest traffics, the anomalies detected by the whitelist, were then be classified by a multi-class SVM classifier into four categories: PROBE, DoS, R2L and U2R. Many data mining and machine learning techniques were applied here. We design this three-tier IDS based on the KDD'99 benchmark dataset. Our system has 94.71% intrusion detection rate and 93.52% diagnosis rate. The average cost for each connection is 0.1781. All of these results are better than those of KDD'99 winner's. Our three-tier architecture design also provides the flexibility for the practical usage. The network system administrator can add the new patterns into the blacklist and allows to do fine tuning of the whitelist according to the environment of their network system and security policy.

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Internet phishing attack is increasing and causing enormous economic loss in recent years. The network security literacy learning for educating students to defense phishing attack becomes more important. However, with the growing of attacking tricks, the anti-phishing knowledge is continuously increasing. The traditional learning platform with antiphishing technical documents is not sufficient for students to prevent the phishing attacks. To transform the continuously increasing anti-phishing knowledge to learning materials, the Bloom's taxonomy of cognitive domain was used to classify anti-phishing knowledge into different levels of learning skills. Since traditional anti-phishing documents are difficult to read for students, the Anti-phishing Education Game was developed to provide an interactive narrative learning environment for learning by doing. Thus, the game missions can be adaptively provided to students based on their learning achievements and engage students' attention. The experimental result shows that students have significant progress in identifying phishing page and high learning motivation by using our approach.

According to the latest Taiwan’s energy plan, nuclear power that provides approximately 16% of total electricity will be replaced by renewable energy sources by 2025. Wind power is of particular interest because Taiwan’s maritime climate and constant monsoons make it a feasible alternative that potentially generate a considerable amount of electricity. To better understand how wind power can provide stable electricity output and sequester CO2 emissions, this study employs the Weibull distribution with a threshold regression model to estimate the electricity potential for 370 scheduled wind farm sites and refine electricity estimation according to observed data from all existing wind farms. The results show that, compared to the theoretical estimation models, our proposed refinement method can, in average, reduce estimating error by 87%. The results indicate that construction of all scheduled sites are not a cost-effective approach, and the government may focus on construction of stations that can generate electricity of more than 12 million kWh per year, if capital rationing do exist. Our insightful results thus convey constructive suggestions regarding sites selection, stability of wind speed, and electricity potential of each site, all of which can be helpful in decision making. It is also noteworthy to point out that unless future climate is far deviated from the observed data, wind power can be an effective substitute of nuclear power.