Jan. 28, 2013 to Jan. 30, 2013
In this work, we consider detecting unknown or new network attack types with a Fuzzy Genetic Algorithm approach. The fuzzy rule is a supervised learning technique and genetic algorithm make fuzzy rule able to learn new attacks by itself. Moreover, this technique has high detection rate and robust. Therefore, we apply the fuzzy genetic algorithm approach to our real-time intrusion detection system implementation i.e. the data is detected right after it arrived to the detection system. In our experiments, various denial of service (DoS) attacks and Probe attacks are considered. We evaluate our IDS in terms of detection time, detection rate and false alarm rate. From the experiment, we obtain the average detection rate approximately over 97%.
Genetic algorithms, Probes, Intrusion detection, Training, Classification algorithms, Testing, Feature extraction,unknown detection, network intrustion detection, IDS, fuzzy genetic algorithm
"Network intrusion detection with Fuzzy Genetic Algorithm for unknown attacks", ICOIN, 2013, The International Conference on Information Networking 2014 (ICOIN2014), The International Conference on Information Networking 2014 (ICOIN2014) 2013, pp. 1-5, doi:10.1109/ICOIN.2013.6496342