LC-IDS: Loci-Constellation-Based Intrusion Detection for Reconfigurable Wireless Networks

Abstract

Detection accuracy of current machine-learning approaches to intrusion detection depends heavily on feature engineering and dimensionality-reduction techniques (e.g., variational autoencoder) applied to large datasets. For many use cases, a tradeoff between detection performance and resource requirements must be considered. In this paper, we propose Loci-Constellation-based Intrusion Detection System (LC-IDS), a general framework for network intrusion detection (detection of already known and previously unknown routing attacks) for reconfigurable wireless networks (e.g., vehicular ad hoc networks, unmanned aerial vehicle networks). We introduce the concept of ‘attack-constellation’, which allows us to represent all the relevant information for intrusion detection (misuse detection and anomaly detection) on a latent 2-dimensional space that arises naturally by considering the temporal structure of the input data. The attack/anomaly-detection performance of LC-IDS is analyzed through simulations in a wide range of network conditions. We show that for all the analyzed network scenarios, we can detect known attacks, with a good detection accuracy, and anomalies with low false positive rates. We show the flexibility and scalability of LC-IDS that allow us to consider a dynamic number of neighboring nodes and routing attacks in the ‘attack-constellation’ in a distributed fashion and with low computational requirements.