As more industries employ robots to perform critical tasks, the need to secure such robots are increasing. Mobile robots are more vulnerable to being attacked, as these are not always deployed in well-controlled environments. Therefore, security of mobile robots is essential, where cyber- and physical-attacks can lead to catastrophic events like physical injury, even loss of life. In some cases, such mobile robots include limited to no security controls. Preventing such cyber-attacks is not always possible. However, timely detection of such attacks or attempted attacks might lead to the deployment of appropriate response actions, which limit the negative consequences of the attack and potentially block corresponding attack-vector. When considering intrusion detection in mobile robots, it is necessary to monitor both the cyber and physical domain, as by their nature, attacks conducted in the cyber realm can lead to severe damage in the physical realm. However, there is limited research done on how such cyber and physical attacks against mobile robots can be detected. Therefore, in this paper, we developed a system for intrusion detection in mobile robots, using a Machine Learning-based approach. This developed system can detect cyber- and physical-attacks, even when the robot is deployed in a previously unknown environment. Our proposed system shows promising results when evaluated utilizing datasets that we collected from Spot robot. To assess the performance of this system, we performed two physical- and two cyber-attacks against the robot, which were identified as a part of the review on threat landscape for mobile robots. This system can be applicable to all mobile robots, to detect attacks in both the cyber- and physical-domain, as the data used for intrusion detection in the context of this study should be available in other mobile robots.