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.

A distributed denial-of-service (DDoS) attack presents a very serious threat to the stability of the Internet. In a typical DDoS attack, a large number of compromised hosts are amassed to send useless packets to jam a victim or its Internet connection, or both. Defense against DDoS attacks as well as identification of their sources comprise demanding challenges in the realm of Internet security studies. In this paper, effective measures are proposed for detecting attacks in routers through the use of queuing models, which help detect attacks closer to the attack sources. Utilizing these measures, an effective DDoS attack detection and packet-filtering scheme is proposed. The suggested approach is a cooperative technique among routers intended to protect the network from persistent and severe congestion arising from a rapid increase in attack traffic. Through computer simulations, it is shown that the proposed scheme can trace attacks near to the attack sources, and can effectively filter attack packets.

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.