Detection of Cyber-attacks in Food Industry using Multi-Layer Perceptron and Convolution Neural Network
I Beulah Rani, Vinodh Ewards S.E., G. Matthew Palmer, G. Jaspher W. Kathrine · 2023
Food insecurity refers to the lack of access to sufficient food, and this is a critical problem that many countries are currently tackling. While there are various causes contributing to this issue, cyberattacks are identified as the primary factor impacting the food industry after the concept of Industry 4.0 was introduced. The impact of cyberattacks on the food industry has been documented by several authors based on the technologies and the devices used in real-time, revealing the need for better detection methods. This study employs the use of three deep learning algorithms, multi-layer perceptron, Artificial neural network and convolution neural networks, to detect cyber-attacks based on the equipment used in food industry. The study achieves its objective by using multiple hidden layers trained for accurate detection. Although MLP has 96.04% accuracy CNN has the highest accuracy rate of 96.77%.