  Volunteer computing uses Internet-connected devices (laptops, PCs, smart
devices, etc.), in which their owners volunteer them as storage and computing
power resources, has become an essential mechanism for resource management in
numerous applications. The growth of the volume and variety of data traffic in
the Internet leads to concerns on the robustness of cyberphysical systems
especially for critical infrastructures. Therefore, the implementation of an
efficient Intrusion Detection System for gathering such sensory data has gained
vital importance. In this paper, we present a comparative study of Artificial
Intelligence (AI)-driven intrusion detection systems for wirelessly connected
sensors that track crucial applications. Specifically, we present an in-depth
analysis of the use of machine learning, deep learning and reinforcement
learning solutions to recognize intrusive behavior in the collected traffic. We
evaluate the proposed mechanisms by using KD'99 as real attack data-set in our
simulations. Results present the performance metrics for three different IDSs
namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS),
Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS) and Q-learning based
IDS (QL-IDS) to detect malicious behaviors. We also present the performance of
different reinforcement learning techniques such as
State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference
learning (TD). Through simulations, we show that QL-IDS performs with 100%
detection rate while SARSA-IDS and TD-IDS perform at the order of 99.5%.
