Modelling of Smart Homes Affected by Cyberattacks

Pazouki, Samaneh; Bibek, K C; Alkhwaildi, Hassan A.; Asrari, Arash

doi:10.1109/naps50074.2021.9449777

Cited by 6 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Highly computerized and interconnected facilities open up digital access to equipment, and the benefits of information accessibility come at a price in terms of a higher risk of cyberattack, which poses an increasingly significant threat to industrial networks, e.g., in the energy, robotics, and automotive industries [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. While there are obvious methods to protect against attacks, including the isolation of information technology (IT) and operational technology (OT) networks and the precise definition of network privileges [ 9 ], unauthorized access to critical elements of such networks constantly remains a real threat [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient Cyberattack Detection Methods in Industrial Control Systems

Marusak,

Nebeluk,

Wojtulewicz

et al. 2024

Sensors

View full text Add to dashboard Cite

The article deals with the issue of detecting cyberattacks on control algorithms running in a real Programmable Logic Controller (PLC) and controlling a real laboratory control plant. The vulnerability of the widely used Proportional–Integral–Derivative (PID) controller is investigated. Four effective, easy-to-implement, and relatively robust methods for detecting attacks on the control signal, output variable, and parameters of the PID controller are researched. The first method verifies whether the value of the control signal sent to the control plant in the previous step is the actual value generated by the controller. The second method relies on detecting sudden, unusual changes in output variables, taking into account the inertial nature of dynamic plants. In the third method, a copy of the controller parameters is used to detect an attack on the controller’s parameters implemented in the PLC. The fourth method uses the golden run in attack detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient Cyberattack Detection Methods in Industrial Control Systems

Marusak,

Nebeluk,

Wojtulewicz

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Cyber-attacks pose an increasingly significant threat to industrial networks. This applies to industries such as energy, robotics, and automotive [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. On the one hand, relatively obvious protection methods against such attacks exist, i.e., isolation of information technology (IT) and operational technology (OT) networks, as well as the precise definition of permissions for individual human and hardware elements of the network based on whitelisting [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study

Zarzycki,

Chaber,

Cabaj

et al. 2023

Sensors

View full text Add to dashboard Cite

This work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamical processes. This means that the attacker may not know the physical nature of the process; an LSTM network is sufficient to mislead the process operator. Our experimental studies were conducted in an industrial control network containing a magnetic levitation process. The model training, evaluation, and structure selection are described. The chosen LSTM network very well mimicked the considered process. Finally, based on the obtained results, we formulated possible protection methods against the considered types of cyber-attack.

show abstract