Industrial control systems (ICSs) such as supervisory control and data acquisition (SCADA) systems necessitate an analytical process for the delineation of security countermeasures, grounded in a rigorous assessment of associated risks. Nevertheless, the process is impeded due to the requirement of extensive manual labor hours by engineering professionals. In response to this formidable challenge, we herein propose an innovative model expressly for the determination of pertinent security countermeasures for ICSs. Our proposed model is meticulously developed based on the principles and framework of both the “Cyber Kill-Chain” and the “Defense-in-Depth” models. Through application of our model, it is possible to efficiently identify and prioritize the crucial points that necessitate immediate address. This is achieved without the prerequisite of having detailed models of the security countermeasures available at hand. The model thereby facilitates the identification of cost-effective security measures with a degree of efficiency and accuracy.

Rotational motion estimation of a 3-D rotating object, i.e. estimating rotational posture and angular velocity at each time point from a sequence of images of the object is an important and challenging task. Previous research use feature extraction algorithms, but information extracted from these kinds of algorithms is not guaranteed to be optimal for rotation estimation. For this reason, as a method for taking the entire image as input and automatically extracting features from the given image, we use an image-based deep auto-encoder such that the latent variable can be interpreted as rotational representation by adding some constraints to the latent variable. Combining with Extend Kalman filter, we estimate not only rotational posture but also angular velocity and inertia ratio of the object. This method is validated using simulation data and it is shown that rotational motion can be estimated well. Also we explore ways to reduce the amount of labelled data used in the training dataset when training this model. Our findings indicate that the labelled data can be decreased to as low as 1% of the total training data without undermining the model's performance significantly.

In this paper, we aim to classify multi-agent systems which consist of moving bodies, such as biological swarms and robot swarms, based on their trajectory data. Generally, the number of agents differs from system to system and the dimension of trajectory data is not constant, making it difficult to use the data as a direct input for a neural network classifier. In addition, the index in the data is unrelated to the connection between agents, and thus it is difficult to reduce the dimension of data using a simple algorithm. To solve these problems, we develop a regression model whose dimension of parameters is constant despite number of agents, and use the parameters of the model as an input data of a neural network classifier. In order to make the dimension of parameters constant, the proposed regression model uses the same parameter to calculate the interaction with another agent. The performance is shown through numerical experiments with observed data of biological swarms and computer simulation data.