Security strategies and applications of AI in the Industrial Internet of Things (IIoT)

Author | Chen Jun

Reviewer| Chonglou

In an open industrial Internet environment, millions of terminals and intermediate devices based on the Internet of Things, need to continuously communicate and stay online around the clock. However, these devices often have various security holes and hidden dangers in terms of confidentiality, integrity, availability, scalability, and interoperability due to initial design limitations. . At the same time, for such equipment hardware itself, running software applications, and ## of the communication network #Internal/ExternalDifferent threats can also cause various unauthorized accesses, data tampering, interruption of production operations, and even damage to networked devices. Cause damage. Among them, our common threat types include: distributed Denial of Service (DDoS) attacks, information scanning and theft, and false data injection , and locking terminals or files, etc., will put the company into the dilemma of stopping production . These often bring a fatal blow to production-oriented enterprises.

Internet Mode

First,

Let us have a basic understanding of the operating model of the Industrial Internet of Things. Extending the related models of cloud services, the Industrial Internet of Things uses four types of interconnection: platform as a service (PaaS), software as a service (SaaS), application as a service (AaaS), and data as a service (DaaS). Intercommunication methods. By collecting and storing data in real time, they make it easier for enterprises to control the quality of data from variousheterogeneous platforms andmaintainconsistency , and then predict the output and control the process and material costs. It is worth mentioning that AaaS is an on-demand delivery of applications over the Internet, and on-demand Or services that charge consumers based on time periods. Since it is hosted on a cloud server, all updates, configuration, and security of the application are done on the server side and not on the terminal. Data as a Service (DaaS) can ensure that enterprise terminal devices can perform data processing anywhere that can access the cloud, realizing the concept of so-called Master Data Management (MDM). In other words, all technical, transactional, commercial, logistics, marketing, and multimedia data will be merged together to maintain global consistency and updates.

Reinforcement requirementsIn industrial IoT network systems, as we increase And as more and more

IoT endpoints are used to collect different types of industrial data, the connections between endpoints and cloud services, etc. are becoming increasingly important. For cloud services, the responsibility for security hardening mainly lies with the host. For edge computing endpoints that carry part of the work of data flow collection, dense generation, distributed computing, and local storage, because at the beginning of the design, cost, availability, and network connectivity are often the primary considerations. aspect, so

IoT endpoint devices are often less secure. As mentioned earlier, some communication protocols running on edge devices (including: sensors, actuators, power modules, and monitoring/aggregation devices, etc.) , mobile applications, local storage, calling interface, and even the hardware itself may have vulnerabilities. In this regard, enterprises must prioritize implementing appropriate device management (such as policy-driven configuration enforcement) and processing, both during installation and deployment, and during

operations. And the security of storage resources, including: timely patching and updating of software/hardware, encryption of data at rest and in transit, etc. #In recent years, as attack methods supported by artificial intelligence continue to emerge, enterprises’ security reinforcement methods also need to follow suit Update iteration. As the saying goes “ defeat magic with magic”, only by introducing

artificial intelligence-related defense technologies can we successfully repel attacks and avoid production interruptions and data loss. .

Risks and Opportunities

##Advantage

Disadvantages

Internal system

Automated pattern analysis
Customized processing method

Need to ensure that there is sufficient data volume and appropriate high-quality data
Need to have an understanding of the current production model and operating environment

opportunity

Threats

#External to the system

Comprehensive and intelligent security management

1. Become a pioneer in the industry, Obtaining market recognition
2. The price is high and experts are scarce
3. There are few cases to refer to , the industrial professional attribute is too strong, and it is easy to create a dense and sparse situation

##

Artificial Intelligence is not a new concept for the Industrial Internet of Things. We can use the traditional SWOT (Strengths, Weaknesses, Opportunities, Threats, strengths, weaknesses, opportunities and threats) analysis methods to find artificial intelligence drivers The link between system security and increased industrial productivity. The following is the SWOT analysis conclusion drawn by foreign scholars on the implementation of artificial intelligence security management in the industrial Internet of Things:

Application status

At present, in terms of the security control needs of the Industrial Internet of Things, the advantages of artificial intelligence technology that can be adopted and implemented mainly include the following aspects:

1. Unified automated risk and threat management
2. Access management, including: artificial intelligence-based biometrics and combating Denial of Service (DoS) attacks, etc.
3. System and application level vulnerability detection
4. Prevent data loss and leakage
5. Enforce virus protection related policies
6. Targeted fraud detection
7. Intrusion Detection and Prevention

Usually, a complete set of industrial IoT enterprise systems are often It is based on three basic components: hardware, software and services. In this regard, the industry has successfully implemented one or more artificial intelligence technologies into the following different application scenarios:

##Naïve BayesK nearest neighbor (K-NN) Euclidean##Fuzzy logicPerform language data analysis, capture incomplete and uncertain data, and perform trend analysis. fractal analysis

Artificial Intelligence Technology	##Application scenario
##Decision tree	Analyzes individual data fragments according to different rules, classifying them as "no change" or "suspected attack", and has the ability to automatically formulate new rules.
)Classify the abnormal activities based on the target activity category.
Discover patterns in large data sets and create new ones based on the	distance between existing and new data that has been classified category. ##Traditional artificial neural network
	Suitable for early automated anomaly detection, which can identify, classify and estimate the losses caused by security vulnerabilities.
##Machine Learning	Use various data-driven methods to process data, verify hypotheses, and automatically extract rules while ensuring sufficient data quantity and quality.
##Deep Learning	Solve problems that are much more complex than other technologies, such as analyzing images or multi-modal data.
Estimate the "smoothness" of patterns and mirror data, analyze trends and their changes.	Natural Language Processing (NLP)
Process and analyze large amounts of natural language data, including human-to-human, human-computer interaction, and emotional computing.	AI layeringImagination Recently, experts in the industry have pointed out that the above artificial intelligence technology may be Applied to the scenario of the industrial IoT environment, it is conceived and proposed to add a fog computing (F##) between the connection between the IoT edge device and the cloud service. #og Computing) Security layering. Relying on related technologies and models of artificial intelligence, this layer can not only understand the basic status of the edge endpoints connected to it and the industrial network system environment where it is located , and can learn and isolate new attacks more easily and quickly from the directly connected cloud service side through its own AI-empowered self-learning ability, and In near real-time, countermeasures are creatively generated to significantly improve the security of data access and adaptability against cyber attacks. Of course, this layer can also provide a log interface to facilitate the dumping of event processing information for subsequent analysis and follow-up by human experts. Summary At this stage, the integration of artificial intelligence and industrial Internet of Things has It is the key to improving the timeliness of production system problem diagnosis and the accuracy of automated prevention processes. These are often inseparable from continuous analysis, pattern recognition, anomaly detection and risk prediction of different attack sources. For example, intelligent and automated firmware updates will ensure that edge endpoints are protected from external network attacks during the firmware update process. Use advanced artificial intelligence algorithms to improve intrusion detection systems (IDS) and intrusion prevention systems (IPS) to accurately detect and prevent new threats in IoT environments in real time. #At the same time, with the compliance requirements for personal privacy data in recent years, artificial intelligence also needs to be implemented through on-demand IoT systems and cloud services. Policy adjustments ensure that only authorized people or devices can access appropriate data. In short, we need to find a balance between the security, compliance, and energy efficiency of industrial IoT systems through the application of artificial intelligence. Author IntroductionJulian Chen , 51CTO community editor, has more than ten years of experience in IT project implementation, is good at managing and controlling internal and external resources and risks, and focuses on disseminating network and information security knowledge and experience.

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