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.
Highlights
With the advent of new technologies on the horizon, such as the fifth generation of mobile communication (5G), the fourth industrial revolution, Intelligent Transportation Systems (ITS), smart cities or the Internet of Things (IoT), the number of users and range of applications for wireless communications are continuously increasing
In order to develop the Linear Shift-Invariant (LSI) model for LC − IDSij, we focus on the g-th routing attack and we consider each neighboring node vj ∈ Ni as an LSI system, which has been linearized for a small time-window around a given instant, τ
We have developed a general mathematical framework based on the theory of dynamical systems, to identify routing attacks and anomalous behaviors from the local perspective of an individual node in Reconfigurable wireless networks (RWN)
Summary
With the advent of new technologies on the horizon, such as the fifth generation of mobile communication (5G), the fourth industrial revolution, Intelligent Transportation Systems (ITS), smart cities or the Internet of Things (IoT), the number of users and range of applications for wireless communications are continuously increasing. As the number and range of use case scenarios for mobile communications grows, so the technical challenges associated with the network operation do. In order to meet the user and network demands for such a wide range of applications, wireless networks must be adaptable and reconfigurable. At the network layer, decentralized topologies are considered, and the data routing must adapt to the data traffic conditions and to the dynamic network topology caused by different network phenomena, such as channel fading, or node mobility
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