An Effective Classification for DoS Attacks in Wireless Sensor Networks

Intrusion Detection Systems (IDSs) have an important role in detecting and preventing security attacks. An IDS should be in Wireless Sensor Networks (WSN) to ensure the security and dependability of WSN service. In this paper, we present an approach method to detect types of DoS attacks in WSN. In particular, we apply Random Forest model to detect type of DoS attacks on WSN-DS dataset. The proposed approach achieves the best performance with F1-score of attacks are 99%, 96%, 98%, 100%, and 96% for Blackhole, Flooding, Grayhole, Normal, and Scheduling (TDMA) attacks, respectively.

[1]  Jingsha He,et al.  Group-based intrusion detection system in wireless sensor networks , 2008, Comput. Commun..

[2]  Zahir Tari,et al.  Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling , 2013, J. Parallel Distributed Comput..

[3]  Yassine Maleh,et al.  A Global Hybrid Intrusion Detection System for Wireless Sensor Networks , 2015, ANT/SEIT.

[4]  Leo Lebanov,et al.  Random Forests machine learning applied to gas chromatography - Mass spectrometry derived average mass spectrum data sets for classification and characterisation of essential oils. , 2020, Talanta.

[5]  Tae Ho Cho,et al.  An energy-efficient routing method with intrusion detection and prevention for wireless sensor networks , 2014, 16th International Conference on Advanced Communication Technology.

[6]  Shahaboddin Shamshirband,et al.  Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks , 2014, J. Netw. Comput. Appl..

[7]  Steven Furnell,et al.  D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks , 2014 .

[8]  王淑卿,et al.  An Integrated Intrusion Detection System for Cluster-based Wireless Sensor Networks , 2011 .

[9]  Mousa Al-Akhras,et al.  WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks , 2016, J. Sensors.

[10]  Shahaboddin Shamshirband,et al.  Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks , 2014, Eng. Appl. Artif. Intell..

[11]  Carlos León,et al.  Using artificial intelligence in routing schemes for wireless networks , 2007, Comput. Commun..

[12]  Rachid Latif,et al.  Intrusion detection system in Wireless Sensor Network based on mobile agent , 2014, 2014 Second World Conference on Complex Systems (WCCS).

[13]  J.A. Stankovic,et al.  Denial of Service in Sensor Networks , 2002, Computer.

[14]  Zubair A. Baig.  Pattern recognition for detecting distributed node exhaustion attacks in wireless sensor networks , 2011, Comput. Commun..