Industrial Control Systems (ICS) is a critical component of any modern manufacturing facility. They deal with the management of physical devices like valves, motors, and controllers. The process management of these devices is often automated.
The control systems are connected through the Internet. For this reason, Industrial Control Systems are an attractive target for cyber-attacks. AI and ML techniques have increasingly been used to secure ICS. Several cyber-security applications have used such methods. It includes data mining, intrusion detection, and malware detection.
The impact of AI and Machine Learning (ML) can be seen in the industry. It can help in identifying the threats and preventing them. This blog will briefly overview the impact of AI and ML to secure Industrial Control Systems.
AI and ML in Securing Industrial Control Systems
An important and obvious security feature to apply to any system is the ability to log activity. In the case of ICS, this becomes vitally important in several ways. Monitoring is key for not only auditing purposes but also for fault detection and prevention.
So, in the case of ICS, OT security is important. But the question is, what is OT security? OT security is the protection of computer systems used in operational technology, where security is to be applied to systems' ICS.
An ICS is the functional part of a control system which is the hardware, software, and data used to support industrial operations and process control. An OT system is used in a process or plant in "real-time," contrasted with a non-computerized system that records historical data for future use.
It can be done by developing the models using the two-ML and AI. Machine learning is a subset of artificial intelligence, involving algorithms that can learn from experience and data. In a simple form, it is defined as a program that allows computers to learn without being explicitly programmed.
Artificial Intelligence is a branch of computer science. It emphasizes creating machines that can perform tasks normally requiring human intelligence. These tasks include visual perception, speech recognition, decision-making, and translation between languages.
These techniques are used to improve the error rate by using historical data. To make these techniques work, you need large quantities of data, including a large amount of information, and a model. A machine learning model is a program that learns from data.
The model is not pre-programmed. Rather it "learns" and gets smarter as it analyzes more data. Algorithms are involved in the process of learning. The algorithms feed the data to the program, and the program learns from it and constantly reprograms itself.
1. Network Protection
AI and ML secure networks in ICS and other industries by analyzing a large volume of data. For example, ML may notice a pattern of certain computers not staying logged into the network for extended periods.
It will recommend a maintenance overhaul on those machines to diagnose and rectify any issues. The AI then can help in the maintenance process by sending out automatic notifications to the technicians to fix them.
Most modern automated intrusions use brute-force tactics to get past firewalls. By deploying ML algorithms, the likelihood of an attack is greatly reduced because it keeps on learning.
2. Data Protection
Data breaches are a major and growing technology problem. It is even more prevalent as more ICS purchase cloud storage off the web. Unfortunately, these cloud storage services are often not as secure as you would hope.
Even more unfortunate is that there are hardly any solutions to this problem. However, with emerging ML, computers can now learn to identify patterns, make decisions, and dispatch fixes without the need for human intervention.
Machine learning helps to identify the abnormalities in the data, which helps in securing the data. Using the machine learning technique, you can accurately predict the threats or malware attacks. It also helps to protect your data from common security risks.
The advanced software uses historical data to form a hypothesis, or a guess, about what the outcome of a certain action might be.
3. Protection of Programmable Logic Controllers (PLC)
ICS controls industrial machinery such as manufacturing equipment or process control systems. Programmable Logic Controllers (PLC) are used in many different ICS to manage the control of machinery.
Many of these PLCs do not have any security systems to protect their inputs and outputs, making them a potential vulnerability. AI and ML can create an algorithm to protect PLCs from manipulation. It makes it difficult for hackers to gain access and sabotage the system.
The most common implementation of ML is an algorithm in software and hardware that improves its performance with data from experience. The software program books the processor hardware to do the same task repeatedly. With each iteration, the hardware gets better and better at the task, until it reaches a point where there's no further improvement possible. These are Artificial Neural Networks or ANNs.
4. Control Server
Control servers are the centerpiece of ICS and are the core layer of protection that supports Industrial Control Systems. Control servers are centralized information and processing centers. They monitor and control physical processes, business activities, and even life-support systems.
Using a control server is a vital part of managing an ICS. Machine learning can help secure the control server by knowing the files and the users working locally on the system. It will make it easier to manage the systems, and you can easily secure the control server.
Machine learning and AI can also store important data on the system, and easily retrieve data in the case of a disaster. The algorithms will detect cyber-attacks in real-time and block them, so the control server is safe.
Final Thoughts
With an alarming frequency, attacks against ICS are escalating. And ICS compromises are becoming more commonplace. ICS vendors, asset owners, operators, and government agencies can recognize AI and ML's role in securing ICS.
AI and ML can recognize anomalous process behavior. They can identify threat actors within the enterprise and manage the emergent complexities of industrial network operations.
It is particularly true for Industrial Control Systems requiring critical, real-time decision-making. For example, AI techniques are widely used to detect intrusions and anomalies in ICS networks. To address this, we can also develop an ML-based anomaly detection system. It can help to monitor the machine's status and detect potential intrusions.
Top comments (2)
Great article! It's interesting to see how AI and ML can be used to secure industrial control systems. I'm interested to see how AI/ML development services can help to improve the security of ICS systems. Thanks for sharing this informative article!
Great explanation!