الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
A modern vehicle has approximately 100 electronic control units (ECU) that are connected
through di erent automotive communication protocols (e.g. CAN, LIN, Flexray,
and Ethernet). The connectivity interfaces of the vehicle are growing fast driven by
the integration of advanced technology. This increase in vehicle connectivity makes
the vehicle network vulnerable to cyber-attacks. However, The current network design,
communication protocols (especially CAN protocol), and software practices were never
intended to be used in a potential hostile environment.
Today, Cyber-security plays a vital role in our daily life in protecting our smartphones,
laptops, and even our vehicles. Vehicle security is a great concern for protecting the life
of passengers and pedestrians. However, the vehicle cyber-security does not advance at
the same rate as technological systems integration in the vehicle network. This increases
the vulnerability and potential attacks against the vehicle. Recently, the necessity to
provide solutions to protect the vehicle against cyber-attacks increases signi cantly.
This research helps to protect the vehicle network against cyber-attacks. It introduces
the applied approaches in the automotive AUTOSAR standard for detecting cyberattacks.
Then, it proposes an anomaly detection system for detecting anomalies in
the content of the received messages. The implementation uses the Long Short Term
Memory (LSTM) neural network for detecting anomalies in malicious messages. The
proposed approach relies on training a model to learn the relation and the rate of change
of the signal values in the content of the messages. Then our model can detect anomalies
based on the learned legitimate behavior of the di erent messages. A thorough evaluation
of the proposed model is presented on a generated dataset where di erent types of
data anomalies are introduced.