Self-Organizing Maps versus Growing Neural Gas in Detecting Data Outliers for Security Applications
Our previous work has demonstrated that clustering-based outlier detection approach offers numerous advantages for detecting attacks in Wireless Sensor Networks, above all adaptability and the possibility to detect unknown attacks. In this work we provide a comparison of Self-organizing maps (SOM) and Growing Neural Gas (GNG) used for this purpose. Our results reveal that GNG is superior to SOM when it comes to the level of presence of anomalous data during the training, as GNG is capable of detecting the attack even with small portion of normal data during the training, while SOM need the majority of the training data to be normal in order to detect it. On the other hand, after both being trained with normal data, SOM performs somewhat better as the attack becomes more aggressive, i.e. it exhibits higher detection rate, although both are capable of detecting the attack in each case.
KeywordsSelf-organizing maps growing neural gas outliers wireless sensor networks
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