P. Jongsuebsuk

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Naruemon Wattanapongsakorn

King Mongkut's University of Technology Thonburi

Alexander Decker

sairaj dessai

Padre Conceicao College Of Engineering

Sahar Selim

Azam Rashid

Selvakani Kandeeban

Manonmanium Sundaranar University

Abdullah Omar

University of Dhaka, Bangladesh

Adnan Mohsin Abdulazeez

Duhok Polytechnic University

Dima Novikov

Roman Yampolskiy

University of Louisville

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Real-time intrusion detection with fuzzy genetic algorithm

by Naruemon Wattanapongsakorn and P. Jongsuebsuk

2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2013

ABSTRACT In this work, we consider network intrusion detection using fuzzy genetic algorithm to c... more ABSTRACT In this work, we consider network intrusion detection using fuzzy genetic algorithm to classify network attack data. Fuzzy rule is a machine learning algorithm that can classify network attack data, while a genetic algorithm is an optimization algorithm that can help finding appropriate fuzzy rule and give the best/optimal solution. In this paper, we consider both wellknown KDD99 dataset and our own network dataset. The KDD99 dataset is a benchmark dataset that is used in various researches while our network dataset is an online network data captured in actual network environment. We evaluate our IDS in terms of detection speed, detection rate and false alarm rate. From the experiment, we can detect network attack in real-time (or within 2-3 seconds) after the data arrives at the detection system. The detection rate of our algorithm is approximately over 97.5%.

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Network intrusion detection with Fuzzy Genetic Algorithm for unknown attacks

by Naruemon Wattanapongsakorn and P. Jongsuebsuk

The International Conference on Information Networking 2013 (ICOIN), 2013

ABSTRACT In this work, we consider detecting unknown or new network attack types with a Fuzzy Gen... more ABSTRACT 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%.

Real-time intrusion detection with fuzzy genetic algorithm

by Naruemon Wattanapongsakorn and P. Jongsuebsuk

2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2013

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Network intrusion detection with Fuzzy Genetic Algorithm for unknown attacks

by Naruemon Wattanapongsakorn and P. Jongsuebsuk

The International Conference on Information Networking 2013 (ICOIN), 2013

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