Abstract:
Wireless Sensor Networks (WSNs) are increasingly deployed in Internet of Things (IoT)
systems for applications such as smart transportation, telemedicine, smart health monitoring and fall
detection systems for the elderly people. Given that huge amount of data, vital and critical information
can be exchanged between the different parts of a WSN, good management and protection schemes
are needed to ensure an efficient and secure operation of the WSN. To ensure an efficient management
of WSNs, the Software-Defined Wireless Sensor Network (SDWSN) paradigm has been recently
introduced in the literature. In the same vein, Intrusion Detection Systems, have been used in the
literature to safeguard the security of SDWSN-based IoTs. In this paper, three popular Artificial
Intelligence techniques (Decision Tree, Naïve Bayes, and Deep Artificial Neural Network) are trained
to be deployed as anomaly detectors in IDSs. It is shown that an IDS using the Decision Tree-based
anomaly detector yields the best performances metrics both in the binary classification and in the
multinomial classification. Additionally, it was found that an IDS using the Naïve Bayes-based
anomaly detector was only adapted for binary classification of intrusions in low memory capacity
SDWSN-based IoT (e.g., wearable fitness tracker). Moreover, new state-of-the-art accuracy (binary
classification) and F-scores (multinomial classification) were achieved by introducing an end-to-end
feature engineering scheme aimed at obtaining 118 features from the 41 features of the Network
Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset. The state-of-the-art
accuracy was pushed to 0.999777 using the Decision Tree-based anomaly detector. Finally, it was
found that the Deep Artificial Neural Network should be expected to become the next default
anomaly detector in the light of its current performance metrics and the increasing abundance of
training data.
