Artificial Neural Network-Based Stealth Attack on Battery Energy Storage Systems

• As the number of installed Battery Energy Storage Systems (BESSs) increases, the concerns related to possible cyberattacks to these systems rise accordingly. The most of BESS owners knows their systems may be vulnerable, but they often consider only denial of service attacks in their risk assessment. Unfortunately, other, subtler and more dangerous attacks exist. In this paper we show that a stealth attack to BESSs can be performed by applying a Man-in-the-Middle (MitM) approach. The aim of the attack is to stealthily manage the physical system by hiding the actual behavior of the system to its supervisory controller. In this case the attacker would be able to: (i) degrade the BESS by reducing its expected lifetime, (ii) produce economic losses for the prosumer, and (iii) affect the security and stability of the grid. The feasibility of the attack is demonstrated by providing an example of a stealth MitM attack on a real BESS coupled with a photovoltaic power plant. The proposed case study demonstrates that such attack can be performed without being discovered by end-users and shows that its effects can be severe. Finally, possible strategies to avoid or detect such kind of attack are discussed. IEEE

Nyckelord

Batteries

Battery Energy Storage System (BESS)

Cyber-Physical BESS Modelling.

Cyber-Security

Inverters

Man-in-the-Middle (MitM)

Power conversion

Power system stability

Security

State of charge

Stealth Attack

Systems architecture

Data storage equipment

Denial-of-service attack

Electric batteries

Losses

Network security

Neural networks

Photovoltaic cells

Risk assessment

Solar power plants

Battery energy storage systems

Expected lifetime

Man in the middles (MITM)

Photovoltaic power plant

Physical systems

Security and stability

Stealth attacks

Supervisory controllers

Battery storage

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)