Key data security issues in industrial IoT (IIoT) systems
According to Statista, by 2025, the Internet of Things devices are expected to rise up to 75 billion units. In fact, every second new 127 devices are connected to such a system.
What is more, it is important to mention that statistics from 2019 show that 82% of companies operating in Industry 4.0 have planned to use or have already adopted IoT networks for business purposes. And taking into consideration the fact that big loads of business data are generated from industrial operations, we can confirm the increasing importance of IoT as a tool for operational success and development.
Nonetheless, in order to take the best advantage of those technology systems and guarantee their long-term reliability, it is crucial to establish a high level of data security. The first step towards such cybersecurity is acknowledging and studying the challenges and risks that the IoT systems bear in every aspect.
And according to the book "Industry 4.0: Managing The Digital Transformation", IoT networks are divided into four main levels, each of which is characterized by different physical devices and distinct cybersecurity issues accordingly.
Let's see them.
1. Perception layer
This aspect of IoT represents the data transactions between the physical devices, such as barcodes, RFID tags and readers, BLE devices, GPS, sensors, etc. The data security risks here refer primarily to unauthorized physical or logical access to the above-mentioned network appliances.
For example, malicious devices, sniffers, or special sensors can be places to illegally extract data information from the IoT systems. This is a major issue in terms of confidentiality, as our private business data may become publicly available, may end up in possession of competitors, or hostile third parties.
Besides, if there is unauthorized access to the data generated through industrial operations, we risk the availability of those data within the organization. For instance, the IoT network may partially or fully cease working or may start transmitting erroneous data under external manipulation, which to brings more damage than advantages to our business operations.
Another security threat relates to this particular IoT layer won't be a surprise: malicious code injections and software. Those can manipulate the devices on a deep level and eventually cause them to collapse or fail.
And the injection of cyber viruses is even easier when our IoT system is transmitting Noisy Data: that is when data travels between devices, which are placed in a very large distance one from another. In this case, the attacker has enough time to disturb the connection between appliances and introduce the malicious code.
2. Network layer
The components in this layer are non-surprisingly networks, including WSN (wireless sensor networks), WLAN, Cloud, and social networks. Those connect the IoT devices and "enable them to become environmentally aware."
This layer is especially vulnerable for cyberattacks, because of the availability of big loads of data inside the networks. The risks that industrial companies bear in this case are mainly in terms of network disruptions and failures.
For example, the most common security issue is DoS (denial of service), which directs malicious commands that deny the usage of the particular network (which is targeted). The result may be error messages, faulty requests, or denial of access.
Another disruption is the routing of data. Instead of completing data transactions from point A to point B, this cybersecurity issue prolongs the traveling route of industrial data within the available networks. As a result, certain datasets may be lost, stolen, manipulated, interrupted, blocked, or changed with the purpose of establishing error-based BI.
Besides, another significant data security risk categorized in this IoT layer is Data breach. If an untrusted party has access to any of our IoT networks, the data may end up in a malicious environment where it can be taken advantage of, resulting in compromising our company's financial and operational information, plans, and revealing any competitive advantage.
3. Service layer
This layer consists of service management, databases, and service APIs. In other words, this level of IoT is based on middleware technology for data management and communication.
The cybersecurity threats here are related to access and manipulation of information services. This way, a risk for the quality of data transactions is imposed, and the information cannot safely travel from one system to another.
In some cases, the information is being returned to the sending system in order for the attacker to spoof the receiver. That is why this particular attack is called Spoofing.
Besides, gaining unauthorized access gives cybercriminals the opportunity to maliciously track information, abuse data services, breach privacy, and eventually use DoS to cease the flow of information throughout the IIoT systems.
The components of this top layer of industrial IoT are smart applications and management interfaces. And even though the level of security depends mainly on the types of apps, there are some common data security threats.
The most crucial one is in terms of app configuration: either failing or incorrect configuration of applications and interfaces. This disables the access and usage of industrial data and traps information at isolated locations, to limit its availability to the business entity. This issue is even more significant when the problem takes place in remote nods of the IoT system, which are unreachable by the data managers.
Apart from this, malware attacks interfere with the proper functions of the various applications to intentionally modify them, block any outcome, or track the behavior of their users.
Last, phishing attacks. Those are directed to the users of the IoT management interfaces and aim to possess sensitive information, gain access to the management panel, and eventually cause harm to the whole system layer.
All four layers of IoT systems impose great data security risks to every industrial organization. But knowing about those is the first step to mitigating them, and even though the risks are many, the solution is only one: High-quality data governance policy.