In this paper, we propose and evaluate the application of unsupervised machine learning to
anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep …

Log messages are now broadly used in cloud and software systems. They are important for
classification and anomaly detection as millions of logs are generated each day. In this …

A common method: build a predictive model, eg, AR, LDS, RNN models: x (t)= f (x {t− p: t− 1},
u {t− p: t− 1}; θ)► x {t− p: t− 1} the sensor measurements from time t− p to t− 1► u {t− p: t− 1} …

Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all
communicating over a network; if any subset becomes compromised, an attacker could …

Cyber-Physical Systems (CPS) are susceptible to various anomalies during their operations.
Thus, it is important to detect such anomalies. Detecting such anomalies is challenging …

The threat of attack faced by cyber-physical systems (CPSs), especially when they play a
critical role in automating public infrastructure, has motivated research into a wide variety of …

The extreme imbalanced data problem is the core issue in anomaly detection. The amount
of abnormal data is so small that we cannot get adequate information to analyze it. The …

Digital twins are increasingly developed to support the development, operation, and
maintenance of cyber-physical systems such as industrial elevators. However, industrial …

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated
research into a multitude of attack detection mechanisms, including anomaly detectors …

In this paper authors propose a new approach to security assesment for cyberphysical
systems. Existing security methodology applicable to information systems is ineffective for …