Detection and Defense from False Data Injection Attacks In Aviation Cyber-Physical Systems Using Artificial Immune Systems

Abstract

In recent years, there has been a rapid expansion in the development of Cyber-Physical Systems (CPS), which allows the physical components and the cyber components of a system to be fully integrated and interacted with each other and with the physical world. The commercial aviation industry is shifting towards Aviation Cyber-Physical Systems (ACPS) framework because it allows real-time monitoring and diagnostics, real-time data analytics, and the use of Artificial Intelligent technologies in decision making. Inevitably, ACPS is not immune to cyber-attacks due to integrating a network system, which introduces serious security threats. False Data Injection (FDI) attack is widely used against CPS. It is a serious threat to the integrity of the connected physical components. In this paper, we propose a novel security algorithm for detecting FDI attacks in the communication network of ACPS using Artificial Immune System (AIS). The algorithm was developed based on the negative selection approach. The negative selection algorithm is used to detect malicious network packets and drop them. Then, a Nonlinear Autoregressive Exogenous (NARX) network is used to predict packets that dropped by the negative selection algorithm. The developed algorithm was implemented and tested on a networked control system of commercial aircraft as an Aviation Cyber-physical system.