This study investigates subcarrier and power allocation schemes in an OFDMA downlink system. To consider client demands, a goal programming approach is proposed. The proposed algorithm minimizes the weighted sum of each client's dissatisfaction index. Simulations show that the sum of dissatisfaction indices can be reduced significantly.

Due to the exponentially increasing threat of cyber attacks, many e-commerce organizations around the world have begun to recognize the importance of information security. When considering the importance of security in e-commerce, we need to train e-commerce security experts who can help ensure the reliable deployment of e-commerce. The purpose of this research is to design and evaluate an e-commerce security curriculum useful in training e-commerce security experts. In this paper, we use a phase of the Delphi method and the Analytic Hierarchy Process (AHP) method. To validate our results, we divide the respondents into two groups and compare the survey results.

This letter focuses on uplink transmission in OFDMA systems. A subcarrier and power allocation problem is formulated that maximizes the throughput of OFDMA uplink systems while satisfying each user's power constraints. A greedy algorithm known to be the most efficient algorithm for this problem can provide a high quality near-optimal solution, but has the disadvantage of incurring a long computation time. As this problem should be solved in a real-time environment, computation time is a very important performance measure of algorithms. In this letter, a computationally efficient algorithm that provides a nearly identical quality, near-optimal solution as the greedy algorithm but requires less than 10% of the computation time of the greedy algorithm is proposed.

Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.