Internet security: firewalls and beyond
Communications of the ACM
Towards a taxonomy of intrusion-detection systems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on computer network security
Code-Red: a case study on the spread and victims of an internet worm
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
IEEE Security and Privacy
Intrusion Prevention System Design
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Machine Learning and Data Mining for Computer Security: Methods and Applications (Advanced Information and Knowledge Processing)
IFTS: Intrusion Forecast and Traceback based on Union Defense Environment
ICPADS '05 Proceedings of the 11th International Conference on Parallel and Distributed Systems - Volume 01
Data warehousing and data mining techniques for intrusion detection systems
Distributed and Parallel Databases
Information sharing for distributed intrusion detection systems
Journal of Network and Computer Applications
DDoS attack detection method using cluster analysis
Expert Systems with Applications: An International Journal
Probabilistic techniques for intrusion detection based on computer audit data
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.