DFCL: DYNAMIC FUZZY LOGIC CONTROLLER FOR INTRUSION DETECTION

Abdulrahim Haroun Ali, Shahaboddin Shamshirband, Nor Badrul Anuar, Dalibor Petković

DOI Number
-
First page
183
Last page
193

Abstract


Intrusions are a problem with the deployment of Networks which give misuse and abnormal behavior in running reliable network operations and services. In this work, a Dynamic Fuzzy Logic Controller (DFLC) is proposed for an anomaly detection problem, with the aim of solving the problem of attack detection rate and faster response process. Data is collected by PingER project. PingER project actively measures the worldwide Internet’s end-to-end performance. It covers over 168 countries around the world. PingER uses simple ubiquitous Internet Ping facility to calculate number of useful performance parameters. From each set of 10 pings between a monitoring host and a remote host, the features being calculated include Minimum Round Trip Time (RTT), Jitter, Packet loss, Mean Opinion Score (MOS), Directness of Connection (Alpha), Throughput, ping unpredictability and ping reachability. A set of 10 pings is being sent from the monitoring node to the remote node every 30 minutes. The received data shows the current characteristic and behavior of the networks. Any changes in the received data signify the existence of potential threat or abnormal behavior. D-FLC uses the combination of parameters as an input to detect the existence of any abnormal behavior of the network. The proposed system is simulated in Matlab Simulink environment. Simulations results show that the system managed to catch 95% of the anomalies with the ability to distinguish normal and abnormal behavior of the network.

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References


Feizolah, A., Anuar, N.B., Salleh, R., Mat Kiah, K.L., 2013, Anomaly Detection Using Cooperative Fuzzy Logic Controller, Intelligent Robotics Systems: Inspiring the NEXT, 376, pp. 220-231.

Lim, H.H., Qiu, B., 2001, Fuzzy logic traffic control in broadband communication networks, The 10th IEEE International Conference on Fuzzy Systems, 1(5), pp. 99-102.

Zadeh, L., 2008, Is there a need for fuzzy logic?, Annual Meeting of the North American Fuzzy Information Processing Society – NAFIPS, 8(9), pp. 1-3.

Giannini, J.A., Kilgus, C., 1997, A fuzzy logic technique for correcting climatological ionospheric models, IEEE Transactions on Geoscience and Remote Sensing, 35(2), pp. 470-474. 5. Cottrell, W.M., Matthews, W., 2000, The PingER Project: Active Internet Performance Monitoring for the HENP Community, IEEE Communications Magazine, 38(5), pp. 130-136. 6. Postel, J., 1981, Internet Control Message Protocol, RFC Editor, United States. 7. Mathis M., 1997, The Macroscopic Behavior of the TCP Congestion Avoidance Algorithm, Computer Communication Review, 27(3), pp. 67-82.

Petković, D., Issa, M., Pavlović, D. N., Zentner L., 2013, Intelligent Rotational Direction Control of Passive Robotic Joint with Embedded Sensors, Expert Systems with Applications, 40(4), pp. 1265-1273.

Shamshirband, S., Petković, D., Ćojbašić, Ž., Nikolić, V., Anuar, N.B., Mohd Shuib, N.L., Mat Kiah, M.L., Akib, S., 2014, Adaptive neuro-fuzzy optimization of wind farm project net profit, Energy Conversion and Management, 80(4), pp. 229–237.

Zakaria, R., Sheng, O.Y., Wern, K., Shamshirband, S., Petković, D., Pavlović, T.N., 2014, Adaptive neuro-fuzzy evaluation of the tapered plastic multimode fiber based sensor performance with and without silver thin film for different concentrations of calcium hypochlorite, IEEE Sensors Journal, DOI: 10.1109/JSEN.2014.2329333.

Shamshirband, S., Petković, D., Anuar, N.B., Mat Kiah, M.L., Akib, S., Gani, A., Ćojbašić, Ž., Nikolić, V., 2014, Adaptive neuro-fuzzy generalization of wind turbine wake added turbulence models, Electrical Power and Energy Systems, 62, pp. 490–495.

Petković, D., Shamshirband, S., Iqbal, J., Anuar, N.B., Pavlović, D.N., Mat Kiah, M.L., 2014, Adaptive neuro-fuzzy prediction of grasping object weight for passively compliant gripper, Applied Soft Computing, 22, pp. 424–431.

Shamshirband, S., Petković, D., Anuar, N.B., Gani, A., 2014, Adaptive neuro-fuzzy generalization of wind turbine wake added turbulence models, Renewable and Sustainable Energy Reviews, 36, pp. 270–276.

Petković, D., Shamshirband, S., Ćojbašić, Ž., Nikolić, V., Anuar, N.B., Md Sabri, A.Q., Akib S., 2014, Adaptive neuro-fuzzy estimation of building augmentation of wind turbine power, Computers & Fluids, 97, pp. 188–194.


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ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

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