In an industrial network, malicious activity can be hard to tell apart from normal data exchange between devices. Malicious actors routinely rely on legitimate tools and the Living-off-the-Land technique, so a single network connection may show no known signs of an attack: the device uses a familiar protocol and transmits ordinary data. An anomaly can hide in subtle details: the data source, the time of access, or the volume of transferred data may be atypical for a particular device. However, to detect this kind of anomaly, you need to understand the roles of the devices at both ends of the connection and know which network activity patterns are typical for them.

But an organization’s asset inventory data is often incomplete, which makes it harder to catch unauthorized connections in time. Suppose monitoring detects a host that regularly exchanges data with programmable logic controllers (PLCs). Its IP address is known, but the inventory does not specify what role it plays: is it an engineering workstation, a process data server, or a piece of contractor’s equipment? The host may also have been on the industrial network for a long time, with its record going out of date after a network upgrade.

In the joint OT asset inventory guidance released by CISA and its partners in 2025, inventorying devices and classifying them by role are treated as part of OT asset management. For organizations, this is one of the most important areas: according to the SANS 2025 ICS security survey, 50% of respondents had deployed or significantly expanded ICS inventory and visibility tools over the previous 12 months, and 54% named them as investment priorities for the next 12–24 months.

Device inventory in Kaspersky Industrial CyberSecurity for Networks

In Kaspersky Industrial CyberSecurity for Networks, the Asset Discovery technology is the foundation of asset visibility. Passive network traffic analysis detects devices and their interactions, while recognition rules help determine the device category and extract available information about it, such as the vendor, model, or software version. Exactly which attributes the system can obtain depends on the protocol and the traffic it observes. We covered this in more detail at the Kaspersky Industrial Cybersecurity Conference.

The product also supports active polling modules (APMs): where polling is allowed, they can request additional configuration and firmware version details from devices. This kind of inventory is far more detailed than a list of IP addresses.

However, even accurate hardware information doesn’t always explain what function a host performs in a specific industrial facility. Gaps appear for various reasons. Some devices send identifying information only at startup or during configuration, while others rarely communicate. Proprietary protocols can’t always be parsed, and encryption may leave only connection metadata for analysis. If the monitoring system doesn’t receive traffic from every segment of the industrial network, some network activity goes unobserved. Active polling doesn’t suit every device or every operating condition.

When there isn’t enough direct information about a device, its category can be refined by looking at the patterns of its recurring network interactions. Analyzing these interactions is the job of Machine Learning Event Processor (MLEP), a service included in Kaspersky Industrial CyberSecurity for Networks 5.0. This machine-learning-based event processor can both refine device categories and detect anomalies in their network behavior.

What data does MLEP analyze?

To compare device behavior, you don’t need to store every packet as a separate observation (a data object) in the ML model. MLEP represents network sessions as a sequence of short 15-second fragments. For each fragment, MLEP stores information about the endpoints (IP, MAC, and port), the direction of data exchange, the protocol, and the number and size of the packets transmitted. Together these make up an event, which then gets a timestamp. Traffic thus becomes a sequence of events showing which hosts a device communicated with, when, and how communication intensity changed.

Aggregating data into events also sets the method’s limits. An aggregated fragment can’t be used to reconstruct the exact order of individual packets or the content of commands. MLEP can draw conclusions about a new protocol only if another KICS technology has managed to recognize it. That said, comparing stable network connections and changes in exchange volume doesn’t require a detailed record of every packet. These observations form the basis of the device’s behavioral history.

From observations to MLEP results

Each object has its own behavioral history, and one object’s history can’t be applied to another. But the principles of behavior analysis, along with some common rules, work across different objects.

Let’s look at how this works in MLEP, which is built on a spiking neural semantic network (NSN).

Event identification

Neurosemantic networks structure information as a multilayer ordered graph, where the data in each consecutive layer builds on the data in the previous one. The nodes of this graph — that is, the units of information in each layer — act as neurons.The NSN creates dedicated channels to handle different event parameters (port, IP, and so on). Unique parameter values for each channel are stored as neurons in layer 0. The event itself, which consists of a set of parameter values, is stored as a neuron in layer 1 of the network. If an event occurs for the first time, neurogenesis creates a new neuron with input connections to the corresponding parameter neurons. This lets the network learn and operate on the event stream at the same time. If the event has occurred before, the network activates the same neuron that was created when the event first appeared. This way, the network identifies each event precisely. Each neuron that corresponds to an event also collects basic statistics: when it was last activated and how many times it has been activated.
 

Event history

In NSN-based event processing, layer 2 and above store sequences of events over time: recurring patterns and the full history. However, categorization and statistical anomaly detection don’t require storing the full packet sequence or a detailed timeline of every network event. As we noted earlier, events are themselves aggregates, which means the order of network packets has already been averaged out. So MLEP keeps history only temporarily, within each episode (30,000 aggregated events by default), which saves computing resources.

Statistics processing in MLEP

MLEP processes statistics mainly through specialized listener neurons on layer −1 (minus one) of the network. Attention heads and monitors are built on these neurons.

When an event arrives, the corresponding neuron is activated, and the network instantly knows whether that neuron is new or was created earlier. Each attention head subscribes to the events or event features it needs and takes in only the activations of the matching neurons. If an event occurs for the first time, all heads are notified and then decide whether to subscribe (grow a connection to the relevant neuron) and receive information about activations of such neurons from then on.

It is called a spiking network because activation information is a spike, not a number. This spike tells the head that something it’s interested in has happened.

An attention head is configured according to scenarios — the situations that MLEP should send alerts about. The core of a head’s configuration is a query, which filters incoming information and serves as the subscription criterion. Unlike regular database queries, this query runs on the stream, so it can react to an event instantly.

Attention heads work very much like the semantics of ordinary human language. A query has a subject of attention (one or more grammatical subjects) and conditions (the other parts of the sentence). In the infrastructure of an industrial enterprise, the subjects are devices on the network, and the conditions are various aspects of network communication, such as ports, protocol, and average packet size. If a head has many subjects (for example, if it’s interested in every device on an industrial network), it fans out: it creates a child head for each individual value of the subject of attention, that is, each device. The child head then watches a specific device and reports to the parent head.

Attention heads don’t accumulate or process statistics. They only filter event streams. When an attention head receives a relevant event, it activates and sends a spike to the monitor subscribed to that head.

Monitors, in turn, have stacks for accumulating and processing events. They can also have their own queries and post-filter information from the head. For example, they can select only new events from those filtered by the head.

There are several types of monitors:

• Stat monitors track changes in the average count of specific events (for example, device activity) over time intervals typical for those events, or in the average values of numeric event parameters (for example, changes in device traffic volume).
• Monitors work from the event alone: they fire when an event has occurred a certain number of times within a given interval, or has occurred for the first time.
• Similar-behavior monitors work with heads that can generalize events, that is, extract just a few parameters whose combination is shared (generalized) across many events.

MLEP uses these monitor types to implement scenarios for categorizing devices and detecting their anomalous behavior:

1. For categorization, MLEP uses attention heads and monitors that identify common behavior across different assets and group devices accordingly. MLEP then takes information from Asset Discovery about the known categories of some assets and extends those categories to other assets in the same group. Because a device can show different types of activity, it may land in several groups. MLEP then makes a weighted assessment across all relevant groups.
   

   Groups of devices with similar network behavior

   At the final stage, the system verifies that assigning the category to the asset is permissible. This is again done by special rule monitors loaded into the NSN at system startup. The rules rely on asset metadata available from Asset Discovery and APMs.

2. MLEP takes a similar approach to anomaly detection: an attention head and a monitor define an anomaly scenario, often loosely. When a monitor activates on such a scenario, MLEP generates an alert about unusual behavior.

The current version of MLEP ships with preconfigured heads and monitors.

The functional dynamic diagram below shows how events from devices on an industrial network are processed.

MLEP in practice: how behavior helps complete the asset inventory

At a facility with several similar production lines, device information may be more complete on some lines than on others. On one line, devices are identified as PLCs, while on another, after an upgrade, some hosts show up only as addresses: the available traffic didn’t provide enough information to determine their category. Still, these devices communicate with the same types of HMIs and process data collection servers. Their communication directions, frequency, and volumes may be similar.

That’s where MLEP comes in. It takes the categories of already known assets and matches recurring features of their network behavior against those of uncategorized devices. When the observed interactions turn out to be similar, MLEP can add a category for that host to the inventory.

Once the category is refined, the industrial network map provides more context about the equipment on the network. When analyzing network activity, the analyst immediately sees the device’s suggested category and can skip looking up each IP address in the design diagrams. This approach is especially useful where equipment changes faster than the documentation is updated.

The suggested category is also checked against existing device information and defined constraints. This helps filter out some contradictory options, but prediction quality still depends on how much of the traffic is visible, especially if the device communicates only rarely.

Anomalous activity and streaming statistics

Investigations of real-world attacks show that attackers actively use both standard tools and permitted protocols. You don’t always need to know an attacker’s specific technique in advance to spot potentially malicious activity. Signature-based detection helps find known signs of an attack, but it doesn’t assess how typical an observed interaction is for a particular device. MLEP compares current activity with the device’s usual network profile: which hosts it communicates with, which protocols it uses, when and how much data it transmits, its average packet size, and so on. The changes it detects help guide the direction of the investigation. Correlating these changes with other events and the operational context helps determine whether malicious actions could be behind them. When investigating an incident that has already been detected, anomalies help reconstruct the sequence of actions.

The first version of MLEP comes with six preconfigured monitors, one for each type of deviation from the device’s usual network profile:

Anomaly	How it helps with event analysis
New external host	Flags a new external connection that wasn’t previously seen in the device’s observed behavior, even if the IP address isn’t listed among known IoCs. Helps check whether this connection appeared at the same time as other suspicious events.
New combination of destination address and port	Shows that the device has begun using a service it hasn’t used before. Once an incident has been detected, this helps trace new connections after the more critical events have been analyzed.
Unusually large volume of data sent	Highlights data transfers that are atypical for the device and helps correlate their timing and direction with other events.
Atypical activity time	Shows that the device was active at a time that’s unusual for it. In an already detected incident, this helps reconstruct the timeline of actions around the main alert.
New protocol	Helps spot a new method of communication, even when the device is communicating with familiar hosts.
Activity with signs of scanning	Shows that the device has started contacting an unusually large number of neighboring hosts. This signal helps reveal possible reconnaissance ahead of attempts to access equipment.

During an investigation, anomalies help reconstruct the sequence of changes: when a specific host established new connections, switched protocols, increased its data transfer volume, or shifted its operating hours. Matching events by device and time shows an analyst how activity unfolded around an incident that has already been detected.

This is especially useful when some of the attacker’s actions fell outside the monitored scope or didn’t trigger other KICS technologies. Correlating these changes with the device’s role, protocol analysis data, and events from other technologies gives the analyst a more complete timeline. Behavioral anomalies show exactly where to look for the missing links and which network interactions need attention.

In the event list, anomalies detected by MLEP are marked with an AI label. Each event shows its name, source, destination, timestamp, and the ID of the triggered scenario.

Conclusion

In an industrial network, whether behavior is anomalous depends on the host where it occurs and that host’s place in the industrial process. The repetitive nature of many communications makes a change in normal data exchange easier to spot, while knowing a device’s function and criticality helps gauge the possible consequences. Taken together, these observations make network traffic more than a list of connections: it helps assess new events in the context of a specific facility, even without an attack scenario known in advance. Machine learning technologies are well suited to this kind of comprehensive analysis, as they can process and organize huge streams of heterogeneous data. Starting with version 5.0, Kaspersky Industrial CyberSecurity for Networks handles this function with MLEP, which works effectively alongside the product’s existing functionality.

All threats

In Q2 2026, the percentage of ICS computers on which malicious objects were blocked continued to decrease, falling to 19.15%, its lowest level since 2022.

Regionally, the percentages ranged from 8.1% in Northern Europe to 27.9% in Africa.

The figures increased in five regions over the quarter, most notably in East Asia (by 2.0 pp) and Africa (by 0.5 pp).

East Asia saw increases in percentages for all threats except miners. The region ranked first in terms of growth for malicious scripts and phishing pages, spyware, and viruses. East Asia also led in terms of growth in threats from the internet. The percentage of ICS computers on which email threats were blocked also increased.

Selected industries

The biometrics sector (26.44%) has traditionally led the rankings of industries and OT infrastructures surveyed in this report in terms of the percentage of ICS computers on which malicious objects were blocked. Biometric systems are characterized by the availability of internet access, extensive email use for data exchange and approvals (e.g. access granting), and, in many cases, minimal cybersecurity controls within the organizations that use them.

The biometrics sector ranked first among industries in terms of the following threat categories: malicious scripts and phishing pages, malicious documents, spyware, ransomware, and worms. The sector is also leading among industries in terms of email threats. At the same time, unlike other industries, the percentage of affected ICS computers for email threats in biometrics exceeds that for internet threats.

In all selected industries, the global average follows a downward trend.

Threat categories

In Q2 2026, Kaspersky security solutions blocked malware from 10,904 different malware families of various categories on industrial automation systems.

Over the quarter, the percentage of ICS computers on which malicious objects of the following categories were blocked increased: denylisted internet resources, malicious documents, worms, ransomware, and malware for AutoCAD.

Malicious scripts and phishing pages (JS and HTML)

Malicious scripts and phishing pages remained in first place in the threat category rankings based on the percentage of ICS computers on which the respective threats were blocked. In Q2 2026, the global average dropped to 5.42%.

Over the quarter, the figure for this category only increased in East Asia, rising by 0.93 pp to 4.86%. This is the second-highest figure in the region in the last three years.

In East Asia, the percentage of ICS computers affected by malicious scripts and phishing pages increased in all the industries surveyed, except construction. The highest figures were recorded for biometrics (9.01%) and building automation (6.49%).

Denylisted internet resources

In Q2 2026, denylisted internet resources rose in the threat category rankings from third to second place, displacing spyware. Globally, the percentage of ICS computers on which denylisted internet resources were blocked has been increasing for two quarters in row and reached 4.31%.

The figures increased in all regions over the quarter, most notably in Russia (by 1.33 pp). Moreover, Russia ranked first (5.17%) among the regions in terms of denylisted internet resources. Since 2022, the region has topped these rankings twice before, both times in Q2: in 2022 and 2024.

Among the selected industries in Russia, the highest figures for the denylisted internet resources were in the electric power (6.61%) and engineering and ICS integration (5.62%) industries.

Malicious documents (MSOffice + PDF)

Malicious documents ranked fourth in the threat category rankings by the percentage of ICS computers on which they were blocked. The percentage for this category decreased over the previous three quarters, reaching its lowest level in three years. However, in Q2 2026, it increased to 1.77%.

Over the quarter, the figures for malicious documents increased in seven regions, most notably in South America (by 1.35 pp) and Southern Europe (by 0.48 pp). These two regions are among the top three in terms of malicious documents, malicious scripts and phishing pages, as well as threats from email clients.

South America ranked second in the rankings of regions in terms of malicious documents. In Q2 2026, the percentage of ICS computers in the region on which this threat was blocked was 3.56%, which was the fourth highest in three years.

Among the selected industries in South America, the highest percentage of ICS computers on which malicious documents were blocked was in biometrics (6.67%).

Southern Europe ranked first in the rankings of regions in terms of malicious documents. In the previous quarter, the percentage of ICS computers in the region on which this threat was blocked was the lowest in three years, but in Q2 2026 it increased to 3.63%.

Among the selected industries in Southern Europe, the highest percentage of ICS computers on which malicious documents were blocked was once again in biometrics (11.48%).

Spyware

Spyware ranked third in the threat category rankings based on the percentage of ICS computers on which it was blocked. The percentage for this category (3.30%) is the lowest since 2022.

Over the quarter, the figures increased in three regions, most notably in East Asia (by 0.53 pp) and Southeast Asia (by 0.42 pp).

East Asia ranked third based on the figures for spyware (4.77%), behind Africa and Southeast Asia. This is the region’s highest rate since Q2 2025. Among the countries and territories in the region, the highest percentage of ICS computers on which spyware was blocked was in mainland China (6.61%). Among the selected industries in East Asia, the highest figures for spyware were in the electric power (11.75%) and manufacturing (5.87%) industries. In all the industries surveyed, the figures are higher than the regional average.

Southeast Asia ranked second after Africa in the ranking of regions in terms of spyware, with 5.32%. Among the selected industries in Southeast Asia, the highest figures for spyware were in biometrics (8.93%) and manufacturing (7.32%). The figures increased in all industries over the quarter.

Ransomware

The percentage of ICS computers on which ransomware was blocked decreased in the previous three quarters but increased to 0.16% in Q2 2026.

During the quarter, the percentage increased in all regions, except Western and Southern Europe and North America (Canada). Africa led the ranking in terms of growth for this metric.

In Q2 2026, Africa ranked first among the regions in terms of the percentage of ICS computers on which ransomware was blocked (0.29%). The only time the figure in the region was higher in the past three years was Q2 2025 (0.31%).

Among the selected industries in Africa, the highest figures for ransomware were in the electric power industry (0.72%) and biometrics (0.52%). Over the quarter, the figures increased in all industries, except manufacturing and construction. The biggest increase was recorded in the electric power industry.

In Russia, the percentage of ICS computers on which ransomware was blocked in biometric systems has increased for three consecutive quarters, reaching 1.22%. This is the highest level of ransomware across all industries in all regions.

Miners

In Q2 2026, the percentage of ICS computers on which miners were blocked was the lowest since 2021, for both miners in the form of executable files for Windows (0.48%) and web miners running in browsers (0.14%).

The figures for both categories decreased in all regions, except for Africa where figures for miners in the form of executable files for Windows increased slightly.

On average, the oil and gas industry led the rankings among the selected industries both in terms of miners in the form of executable files for the Windows OS (0.66%) and in terms of web miners (0.34%).

Worms

In Q2 2026, the percentage of ICS computers on which worms were blocked increased to 1.43%.

In Q2 2026, the Middle East (2.11%) was second (after Africa) in the rankings of regions in terms of worms, displacing Central Asia and the South Caucasus.

Among the selected industries in the Middle East, the highest percentage of ICS computers on which worms were blocked was in building automation (2.90%). Over the quarter, the figures increased in all industries.

Australia and New Zealand ranked 12th among the regions in terms of the percentage of ICS computers on which worms were blocked (0.41%). Over the past three years, the figure in this region was only higher in Q2 2024 (0.42%). The figures increased in all the surveyed industries in the region, most notably in manufacturing and electric power. As a result, for these industries they exceeded the regional average by 2.9 and 2.3 times, respectively.

Viruses

In Q2 2026, the percentage of ICS computers on which viruses were blocked decreased to 1.29%.

The top three regions for this metric remain unchanged: Southeast Asia (6.03%), Africa (4.22%), and East Asia (3.14%). These same regions lead the rankings in terms of malware for AutoCAD.

The figures increased in three regions: East Asia, Australia and New Zealand, and Africa, where it has been growing for four consecutive quarters and reached its highest value since 2022.

Among the selected industries in Africa, the highest percentage of ICS computers on which viruses were blocked was in construction (5.47%).

East Asia ranked third among the regions in terms of viruses, reaching the highest level in the region for the past three years. Among the countries and administrative regions of East Asia, mainland China is the clear leader in terms of viruses (5.07%).

Among the selected industries in East Asia, the highest percentage of ICS computers on which viruses were blocked was in construction (5.93%).

In Australia and New Zealand, the increase in the percentage of ICS computers on which viruses were blocked was primarily due to a 4.3-fold increase in the figure for the electric power industry: from 0.29% to 1.24%. For a region where the percentage of attacked ICS computers for all threats is 0.12%, this is a very high value.

Malware for AutoCAD

In Q2 2026, the percentage of ICS computers on which malware for AutoCAD was blocked increased to 0.31%.

The most notable increase over the quarter was observed in Africa. After more than doubling in the previous quarter, the figure for the region continued to rise (although not so dramatically), reaching 1.02%.

Among the selected industries across all regions, the highest percentage of ICS computers on which malware for AutoCAD was blocked was in construction in East Asia (6.38%) and in Southeast Asia (4.05%).

Main threat sources

In Q2 2026, of all the threat sources, the percentage increased only for email.

Internet

The percentage of ICS computers on which threats from the internet were blocked decreased to 7.61%, reaching its lowest level since 2021.

Over the quarter, the percentage increased in three regions: East Asia by 0.8 pp (to 6.3%), South Asia by 0.3 pp (to 10.4%), and Russia by 0.3 pp (to 6.4%).

Among the selected industries across all regions, the highest percentage of ICS computers on which threats from the internet were blocked was in biometrics (13.03%) and engineering and ICS integration (12.16%) in South Asia.

Email

The percentage of ICS computers on which email threats were blocked increased to 2.84%.

In Q2 2026, the percentage of ICS computers on which email threats were blocked increased in South America by 1.0 pp (to 5.2%) and in Africa by 0.7 pp (to 4.3%).

Among the selected industries across all regions, the highest percentage of ICS computers on which email threats were blocked was in biometrics (19.14%) and building automation (12.49%) in Southern Europe.

Removable media

The percentage of ICS computers on which threats from removable media were blocked continued to decrease, reaching 0.24%, the lowest value for the period under review.

Among the selected industries across all regions, the highest percentage of ICS computers on which threats from removable media were blocked was in the electric power industry in East Asia (1.34%) and biometrics in Africa (1.29%).

Network folders

The percentage of ICS computers on which threats from network folders were blocked continued to decrease. In Q2 2026, it was the lowest for the period under review, at 0.023%.

The only region to see an increase in the percentage of ICS computers on which threats from network folders were blocked during the quarter was Africa. This was mainly due to an increase in the building automation figure to 0.05%.

Among the selected industries across all regions, the highest percentage of ICS computers on which threats from network folders were blocked was in biometrics (0.23%), building automation (0.17%), and engineering and ICS integration (0.13%) in East Asia.

For more information on industrial threats see the full version of the report.

All threats

The percentage of ICS computers on which malicious objects were blocked continued to decrease, reaching 19.6% in Q1 2026. This is the lowest value in three years, and it is 1.4 times lower than in Q2 2023.

Regionally, the percentages ranged from 9.1% in Northern Europe to 27.4% in Africa.

The percentage of ICS computers on which malicious objects were blocked increased in five regions over the quarter, most notably in Southern Europe, Northern Europe, and Russia.

In Q1 2026, Southern Europe led the way in growth for internet and email threats. The region also saw the fastest growth in spyware, as well as malicious scripts and phishing pages.

In Russia, the percentage of ICS computers on which malicious objects were blocked exceeded the figures for the previous two quarters. Russia saw an increase in the percentage for threats from the internet, and a slight increase in the figure for threats from email clients (Russia is one of three regions where this figure did not decrease).

Among the threat categories, the greatest increases were observed in the percentages for denylisted internet resources, as well as spyware (distributed in the region via the internet and email clients).

Selected industries

Biometric systems (26.4%) traditionally rank top among the industries and OT infrastructure types covered in this report in terms of the percentage of ICS computers on which malicious objects were blocked. These systems are characterized by internet access, extensive email use for data exchange and approvals (such as access granting), and, in many cases, minimal cybersecurity controls within the organizations that use these systems.

Biometric systems rank first among industries in terms of email threats. At the same time, unlike other industries, the percentage for email threats in biometric systems exceeds that for internet threats.

In all selected industries, the global average follows a downward trend. In Q1 2026, the percentage of ICS computers on which malicious objects were blocked increased only in the manufacturing sector — by 1.0 pp. The percentages for this industry increased across 10 regions, with the most notable increases in Western Europe, Northern Europe, and Russia.

Threat categories

In Q1 2026, Kaspersky security solutions blocked malware from 10,052 different malware families of various categories on industrial automation systems.

Over the quarter, the percentage of ICS computers on which denylisted internet resources were blocked increased (after decreasing over the previous two quarters), and there was a slight increase in the percentage for AutoCAD malware.

Malicious scripts and phishing pages (JS and HTML)

Malicious scripts and phishing pages retained their to spot among threat categories by the percentage of ICS computers on which these threats were blocked. The global average in Q1 2026 was 6.56%.

Over the quarter, the percentages increased in four regions. The most significant change was observed in Southern Europe (9.85%, +0.94 pp). The figures for malicious scripts in the region increased over three consecutive quarters.

Among the selected industries, across all regions, the highest percentages for the malicious scripts and phishing pages category were recorded for biometric systems (19.59%) and building automation (15.43%) in Southern Europe. These same industries lead in similar rankings for malicious documents and spyware.

Spyware

The percentage of ICS computers on which spyware was blocked decreased over two consecutive quarters, dropping to 3.73%. Despite the decline, spyware has ranked second among threat categories by the percentage of attacked computers for three consecutive quarters.

The percentages increased in five regions over the quarter, most notably in Southern Europe (5.46%, +0.35 pp) and Russia (2.84%, +0.24 pp).

In Southern Europe, the percentage of ICS computers on which spyware was blocked increased in all the selected industries except manufacturing. The greatest increase was observed in biometric systems.

Among the selected industries, the highest percentage of spyware in Russia was recorded in biometric systems. That said, the percentage of ICS computers on which spyware was blocked increased in all industries in the region except construction. The percentage figure has been increasing for two consecutive quarters in the oil and gas industry (by a factor of 1.63 over six months), and for three consecutive quarters in engineering and ICS integration, as well as electric power. In the remaining sectors, the values have been fluctuating.

Denylisted internet resources

The percentage of ICS computers on which denylisted internet resources were blocked increased to 3.54%.

The most notable increase over the quarter occurred in Southeast Asia (4.58%, +0.65 pp). Among the industries in the region, the highest percentage figures for this threat category were recorded in electric power and construction. Over the quarter, the largest increases in percentages figures were observed in the electric power and manufacturing industries.

In North America (Canada), denylisted internet resources (2.14%) showed the greatest increase among all categories — by a factor of 1.22.

Among the selected industries across all regions, the highest percentage figures for the denylisted internet resources category were in the electric power (7.11%) and construction (6.25%) industries in Southeast Asia.

Malicious documents (Microsoft Office + PDF)

The percentage figure for this category decreased over two consecutive quarters, reaching its lowest value (1.56%) for the entire period of observations in Q1 2026. It increased just in two regions: Australia and New Zealand (1.12%, +0.04 pp), and Russia (0.62%, +0.01 pp).

Among the selected industries across all regions, the highest percentages for malicious documents were recorded for biometric systems (9.02%) and building automation (6.97%) in Southern Europe. These same industries also lead in similar rankings for malicious scripts and spyware.

Ransomware

The percentage of ICS computers on which ransomware was blocked has decreased for two consecutive quarters, dropping to 0.14%. This is the lowest value among all categories.

The percentage increased in two regions: North America (Canada) (0.11%, +0.04 pp) and slightly in Northern Europe (0.06%, +0.01 pp).

Among the selected industries across all regions, the highest percentages for ransomware were recorded in the oil and gas and manufacturing industries (0.92% and 0.65%, respectively) in Central Asia and the South Caucasus, and in biometric systems (0.89%) in Russia.

Miners in the form of executable files for Windows

The percentage of ICS computers on which miners in the form of executable files for Windows were blocked decreased to 0.59%.

The percentage increased in seven regions. The largest increase was observed in Africa (0.63%, +0.16 pp). Among the selected industries, the largest increases in the region were in the manufacturing and oil and gas industries.

Among the selected industries across all regions, the highest percentages for miners in the form of executable files were recorded in construction (1.99%), biometric systems (1.98%), and the oil and gas industry (1.97%) in Central Asia and the South Caucasus.

Web miners

The percentage of ICS computers on which web miners were blocked has been declining for a year, and in Q1 2026, it reached the lowest value for the entire period under review (0.22%).

At the same time, the percentage increased in seven regions. The largest increases were observed in South Asia (0.28%, +0.11 pp), the Middle East (0.31%, +0.09 pp), and Africa (0.34%, +0.08 pp). Despite the increases, the percentages in these regions for Q1 2026 did not exceed those observed in 2023–2024 and in Q1 2025.

Among the selected industries across all regions, the highest percentages for web miners were recorded for biometric systems (0.97%) in Russia. Biometric systems in South Asia (0.79%) ranked second, and the electric power sector in Southeast Asia (0.76%) ranked third.

Worms

The percentage of ICS computers on which worms were blocked decreased to 1.33%.

The percentage decreased across all regions following an increase in the previous quarter (due to a wave of phishing attacks that distributed the Backdoor.MSIL.XWorm backdoor worm across all regions of the world).

Among the selected industries across all regions, the highest percentage figure for worms was recorded for biometric systems (4.80%) in Central Asia and the South Caucasus. Two industries in Africa – biometric systems (4.04%) and electric power (3.53%) – took the second and third spots, respectively.

Viruses

The percentage of ICS computers on which viruses were blocked decreased to 1.31%.

The top 3 regions by this figure remained the same: Southeast Asia (6.11%, first by a wide margin), Africa (4.15%), and East Asia (2.97%). These same regions are also among the leaders by the percentage of systems affected by AutoCAD malware. The largest increase in this figure was observed in Africa (+0.41 pp).

Among the selected industries across all regions, the highest percentages for viruses were recorded in the construction industry (6.35%) and building automation (5.50%) in Southeast Asia.

Malware for AutoCAD

The percentage of ICS computers on which malware for AutoCAD was blocked increased to 0.30%.

The most notable increase over the quarter was observed in Africa, with the region’s percentage figure rising by 0.47 pp, a very significant increase for this category, and almost doubling (to 0.91%).

Among the selected industries across all regions, the highest percentages for AutoCAD malware were recorded in the construction industry in East Asia (5.58%) and Southeast Asia (3.87%).

Main threat sources

In Q1 2026, the average percentages across all threat sources, except threats from the internet, decreased globally.

Internet

The percentage of ICS computers on which threats from the internet were blocked increased to 7.88%. However, over the past three years, the percentage figure for internet threats has followed a downward trend.

The largest increases in the percentages were recorded in Southern Europe (8.59%, +0.59 pp), Southeast Asia (10.16%, +0.55 pp), and Northern Europe (4.47%, +0.51 pp).

Among the selected industries across all regions, the highest percentages for threats from the internet were recorded in electric power (13.16%) and construction (12.55%) in Southeast Asia, and in the engineering and ICS integration sector (12.33%) in South Asia.

Email clients

The percentage of ICS computers on which threats delivered via email clients were blocked decreased to 2.59%. This is a three-year low.

The percentage of this threat source increased in three regions: Southern Europe (6.54%, +0.2 pp), East Asia (1.5%, +0.09 pp), and slightly in Russia (0.7%, +0.04 pp).

Among the selected industries across all regions, the highest percentages for email threats were recorded for biometric systems (19.78%) and building automation (12.34%) in Southern Europe. In these two industries, the percentage of ICS computers on which email threats are blocked is higher than the percentage for threats from the internet. A similar situation was observed in two other instances, both in biometric systems (in South America and Southeast Asia).

Removable media

The percentage of ICS computers on which threats were detected when connecting removable media continued to decrease, reaching its lowest value for the period under review (0.26%).

Among the selected industries across all regions, the highest percentages for removable media threats blocked on ICS computers were observed in the electric power sector in Central Asia and the South Caucasus (1.45%), East Asia (1.34%), and Africa (1.16%).

Network folders

The percentage of ICS computers on which threats are blocked in network folders is steadily decreasing. In Q1 2026, it was the lowest for the period under review (0.029%).

East Asia has traditionally led by a wide margin. The percentage for East Asia (0.135%) is 27 times higher than the lowest regional value (recorded in Northern Europe).

The largest increases in the percentages for threats from network folders were observed in Africa (0.037%, +0.006 pp) and South America (0.013%, +0.006 pp).
Among the selected industries across all regions, the construction industry in East Asia, at 0.36%, holds the top positions in the ranking by the percentage of ICS computers on which threats are blocked in network folders.

For more information on industrial threats see the full version of the report.

About the vulnerability

The CVE-2024-2658 vulnerability was discovered in 2024 within the FlexNet Publisher component of the Schneider Electric Floating License Manager. This software handles license management across various Schneider Electric products used for comprehensive industrial automation ranging from PLC programming to centralized control room implementation. Below, we break down how a single flaw can jeopardize an entire industrial facility, how to detect it on your workstations, and how to minimize the risks.

This vulnerability is a CWE-427: Uncontrolled Search Path Element issue. It stems from a system application referencing an OpenSSL configuration file at a hardcoded path without proper access controls.

This behavior allows a local non-administrator to craft a custom OpenSSL configuration file and force the lmadmin.exe system process – the core service that handles licensing – to load a third-party DLL. Consequently, the attacker’s code executes within the context of the service rather than a standard user account. Under specific conditions, this paves the way for further privilege escalation to the NT AUTHORITY\SYSTEM level.

Once NT AUTHORITY\SYSTEM access is achieved, the adversary can gain full control over local configuration files, sensitive system data, and secrets stored on the host. The potential for lateral movement to other nodes in the industrial network – such as engineering workstations – hinges on network connectivity, availability of stored credentials, and overall network architecture. Furthermore, the attacker can disrupt the operation of the license server itself, directly impacting the availability of engineering software and maintenance.

We will examine the role the FlexNet Publisher component plays within the Schneider Electric Floating License Manager (Schneider Electric FLM), why the hardcoded openssl.cnf path inside the application creates a critical hazard, what the exploit chain looks like, and the necessary mitigation steps to secure your environment.

The role of FlexNet Publisher in Schneider Electric FLM

FlexNet Publisher is a third-party commercial product by Flexera Software. The engineers behind Schneider Electric FLM, along with developers of numerous other industry solutions, integrate FlexNet Publisher as a library to manage product licensing. The underlying issue within FlexNet Publisher is that up to and including version 11.19.6.0, the library failed to restrict low-privileged users from modifying or replacing the openssl.cnf file. This is a textbook example of an Uncontrolled Search Path Element vulnerability (CWE-427).

Schneider Electric FLM relies on a combination of tightly integrated components:

1. lmadmin.exe: a lightweight 32-bit daemon that services license requests coming from PLCs, HMIs, and SCADA modules. During installation, this component is automatically registered as a Windows service named lmadminSchneider. The service is configured to launch automatically and executes under the NT AUTHORITY\LOCAL SERVICE account.
   

   Properties of lmadminSchneider: the executable file and security context of the service

   Properties of lmadminSchneider: the executable file and security context of the service

2. The openssl.cnf configuration file located in the OpenSSL-contrib subdirectory. This configuration file can specify a path to engine-module, a custom DLL file that FlexNet can automatically load into the lmadmin.exe process space. The application references this configuration file via a hardcoded path built into the binary:

   C:\cygwin\home\nightly\LMADMI~1.4\tier1\lmadmin\contrib\openssl\_RELEA~1\openssl\openssl.cnf

   LMADMI~1.4 is the directory name in MS-DOS 8.3 filename format. Any user authorized to create directories in the root of C:\ can recreate this structure – by default, all authenticated users are allowed to do that. A clean, default installation of Schneider Electric FLM does not actually create that folder.

3. The FlexNet Publisher component (historically known as FLEXlm): the license management library that reads openssl.cnf, specifically parsing the [engine] section. If a dynamic_path parameter is defined within that section, FlexNet Publisher will load the specified DLL module at the specified path without performing any checks.
4. The Schneider Electric FLM web portal is a lightweight HTTP server embedded directly within the lmadmin.exe process. It provides access to two main areas: Dashboard (accessible without authentication) and Administration (password-protected). Because the web portal and lmadmin.exe share the same address space, any code loaded via FlexNet Publisher executes directly inside the lmadmin.exe process. This allows an attacker to easily intercept credentials for the Administration portal and leverage them to expand their attack surface across other systems.

The exploit path

To exploit this vulnerability, the attacker must first have the ability to execute arbitrary code locally on the machine hosting the vulnerable service. The primary weakness exploited here stems from standard Windows NTFS permissions: by default, the root directory of the system drive often allows the Authenticated Users group to create new folders. If these permissions (ACLs) have not been tightened, a non-privileged attacker can manually reconstruct the exact directory structure that lmadmin.exe checks for its OpenSSL configuration:

C:\cygwin\home\nightly\LMADMI~1.4\tier1\lmadmin\contrib\openssl\_RELEA~1\openssl\

Next, within this newly created openssl.cnf file, the attacker adds a parameter pointing to a malicious engine module hosted in a writeable directory, for example: dynamic_path = C:\\Users\\public\\malicious.dll. When FlexNet Publisher initializes OpenSSL, it parses this section. Upon finding the dynamic_path parameter, it uses it to load the attacker’s DLL. Because vulnerable versions of FlexNet are allowed to read an OpenSSL configuration from an initially non-existent, untrusted path, the lmadmin.exe process accepts the rogue configuration file as legitimate.

For lmadmin.exe to parse the openssl.cnf file and execute the malicious DLL, the lmadminSchneider service must be restarted. This can happen under any of the following conditions:

• After a regular reboot of the host machine.
• If the user possesses permissions to restart the service – by default, the Authenticated Users group is not allowed to do that.

Upon lmadmin.exe service startup, FlexNet Publisher initializes OpenSSL, opens the openssl.cnf configuration file, and parses the [engine] section. If a dynamic_path is specified, OpenSSL loads the corresponding DLL module directly into the lmadmin.exe process space. Once loaded, the code within the DLL executes in the context of lmadmin.exe. Because the service runs under NT AUTHORITY\LOCAL SERVICE, the malicious code inherits its privileges.

While the NT AUTHORITY\LOCAL SERVICE account is highly restricted by design, a significant risk for further privilege escalation remains. This exposure exists because, in a standard configuration, the service process is granted SeImpersonatePrivilege. This privilege allows the process to impersonate a client’s security context after authentication. Given an appropriate interaction scenario via RPC, COM, or named pipes, an attacker can exploit this to achieve local privilege escalation to NT AUTHORITY\SYSTEM. This is commonly done with tools from the Potato exploit family or similar impersonation abuse techniques. Consequently, while each individual step may appear unremarkable, their combination forms a complete exploit chain: moving from a low-privileged local user to executing code within a service context, and under the right conditions, to achieving a full escalation to NT AUTHORITY\SYSTEM privileges.

Mitigating CVE-2024-2658

• If your organization does not rely on floating licenses, we recommend completely removing Schneider Electric FLM or avoiding its installation on workstations where it isn’t strictly required. Whenever feasible, use licenses tied to specific machines instead.
• If still required, Schneider Electric FLM should be hosted on a dedicated server with strictly controlled user access.
• Use an administrative account when creating the C:\cygwin directory and explicitly deny write permissions to the Authenticated Users group for this folder. This prevents staging a rogue OpenSSL configuration file along the hardcoded path.
• Finally, update Schneider Electric FLM to version 3.0.0.0 or later.

Detection with Kaspersky solutions

Kaspersky Industrial CyberSecurity successfully detects exploitation attempts targeting this vulnerability. The KICS Vulnerability Manager module flags the presence of the vulnerable software version on endpoints, while the behavioral analysis engine tracks each stage of the attack chain – from the creation of the rogue configuration file on disk to the vulnerable service’s attempt to load the malicious library.

The KICS alert card consolidates details on the exploitation attempt alongside recommended defensive actions.

Description
The Exploit Prevention component of the EPP application detected attempts to exploit a vulnerability in a protected process. This type of attack may lead to malicious code execution, unauthorized access, or system integrity compromise.

EPP application data
●	Object name: C:\Program Files (x86)\Schneider Electric\Floating License Manager\FLEXnet Publisher License Server Manager\lmadmin.exe.
●	Status: Untreatable.
●	MD5 hash: c3f57667d9e8e1b2375ba09cdf71cac8.
●	SHA256 hash: 9dab845704d1999ec8ed089594cfd2173a08057f1caf9a2346c22c81039dbb7a.

Mitigations
●	Analyze the event and identify the source of the startup or interaction with the process.
●	Make sure that this vulnerability is relevant to your system (check the software version and installed updates).
●	If a vulnerability exploit is confirmed, isolate the device and search for signs of compromise (suspicious files and their checksums, unknown processes/services, or queries to external IPs/FQDNs). Also, check for similar traces on other devices.
●	Install security updates for the attacked software or operating system and run a full scan of the device if necessary.

Additionally, to proactively identify the presence of the vulnerability on a host as part of a continuous vulnerability management process, organizations can utilize the OVAL scanning task within KICS products. The following screenshot illustrates how KICS for Nodes highlights the presence of the vulnerable software version.

Conclusion

The CVE-2024-2658 vulnerability is a prime example of the consequences that dependency-loading mechanisms lacking proper validation can have. In the case of the Schneider Electric Floating License Manager, a local non-administrator can position a rogue openssl.cnf configuration file at the hardcoded path to inject a malicious DLL directly into the lmadmin.exe service context. Given a certain system configuration, this chain can be leveraged to escalate privileges to the NT AUTHORITY\SYSTEM level.

To remediate this vulnerability, organizations must immediately upgrade the affected component (FlexNet Publisher) to a patched version and restrict write permissions for non-privileged users to the C:\cygwin directory. Implementing these controls will significantly reduce exploitation risk, while Kaspersky Industrial CyberSecurity solutions can provide an added layer of defense by detecting anomalous behavior at the earliest stages of the attack.