Elements of information theory
Elements of information theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
How to Own the Internet in Your Spare Time
Proceedings of the 11th USENIX Security Symposium
Mining anomalies using traffic feature distributions
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Detecting anomalies in network traffic using maximum entropy estimation
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Improving accuracy of immune-inspired malware detectors by using intelligent features
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
Worms spread by scanning for vulnerable hosts across the Internet. In this paper we report a comparative study of three classification schemes for automated portscan detection. These schemes include a simple Fuzzy Inference System (FIS) that uses classical inductive learning, a Neural Network that uses back propagation algorithm and an Adaptive Neuro Fuzzy Inference System (ANFIS) that also employs back propagation algorithm. We carry out an unbiased evaluation of these schemes using an endpoint based traffic dataset. Our results show that ANFIS (though more complex) successfully combines the benefits of the classical FIS and Neural Network to achieve the best classification accuracy.