Learn how to improve and elevate the cybersecurity of robotic systems and prevent security breaches with the help of our quick cybersecurity glossary.
Robotic systems might seem impenetrable, but reports show that cybercriminals are finding new ways to exploit vulnerabilities, which results in data breaches.
Nowadays, most systems are connected to the internet, and that serves as a gateway to hackers who are always searching for system weaknesses.
Shockingly, a large number of industrial robots rely on old programming languages. It makes it difficult to detect or fix any errors that cybercriminals could misuse. Gaining access to only one online machine could allow hackers to get their hands on confidential data.
Applying the latest security measures and constantly improving the safety of a robotic system is the best way to prevent third parties from creating a breach.
Keep reading to learn how to enrich your cybersecurity glossary and ensure more advanced protection for your robotic systems.
Cybersecurity terms you should know
Understanding why robotic system hacks happen is only part of the process. In order to prevent a cybersecurity attack, you have to understand the aspects related to it.
For this reason, we have prepared a short cybersecurity glossary with some key terms that every engineer or developer who works in the field of robotics should know:
1. Vulnerability assessment
Vulnerability assessment is the process of finding weaknesses and security risks in the robotic system itself. It is usually done by testing the system with the help of ethical hackers, checking the robotic system for outdated software versions, and more.
The main goal is to identify the risks and vulnerabilities and then fix them before cybercriminals can gain access to the robotic system. It is recommended that vulnerability assessment be performed regularly.
2. Intrusion detection system
As the name suggests, an intrusion detection system actively monitors the robotic system to identify security problems in real-time.
Once the detection system learns the normal behavior of the system, it looks for security breaches and deals with them accordingly. It often includes blocking the threats or isolating a specific part of the network.
3. IoT security
Internet of Things, or IoT, is the term that describes a network of connected devices and systems. If a linked device experiences a security breach, the entire network will be in danger.
Robotics have embraced the IoT, so the security of this network should be at the highest level.
IoT security strategy has several aspects that need to be done to ensure the overall safety of the network. For instance, each device that connects to the IoT needs to be authorized before it gains access to the robotic system.
Ensuring that each IoT device is running the latest software is a must. Bigger enterprises should consider network segmentation to prevent unauthorized access to systems.
4. Secure boot
Secure boot is an important part of robotics because it verifies that the installed software, patches, or firmware has no hidden malicious code.
The process verification, which is an integral part of a secure boot, is a bit lengthy because you need to approve components that are booting up.
However, this is essential for the cybersecurity of the system. Secure boot identifies odd code and stops the system from booting, which is crucial for the security of the robotic system as a whole.
5. Machine learning for anomaly detection
The final term in this quick cybersecurity glossary is machine learning for anomaly detection. Machine learning has been all over the news lately, so it’s no wonder it is used for the cybersecurity of robotic systems.
As you might have guessed, this type of anomaly detection uses algorithms to find certain behaviors inside networks.
These abnormalities could suggest that the system is under a security threat or not performing as intended.
Of course, data collection is the foundation of machine learning, so you need to collect the necessary information that will be used to train this security tool. Constantly updating the data is required for better anomaly detection.
