The Key to Using AI to Enhance IoT Security

Written by: Anthony Moffa

Read Time: 5 min

The combination of AI and IoT has transformed how we leverage technology, bringing both exciting advancements, challenges and serious concerns, particularly when it comes to data security. While generative AI has captured the headlines in recent months, other forms of AI, such as machine and deep learning techniques, have been leveraged in IoT applications for a number of years to improve the accuracy and efficiency of many service processes. In today’s world, there is a fundamental understanding that we need to focus on strong security measures to keep our digital world safe. As more and more of the devices we interact with are added to the realm of remotely monitored and maintained devices, that need goes from important to crucial.

What is the impact of AI in deep learning and machine learning?

Machine learning is a field of AI that focuses on developing computer algorithms that can learn and improve from data without stated instructions. Deep learning is a subset of machine learning that focuses on training artificial neural networks to learn and make decisions on their own.

Deep learning

One area where AI has an impact in deep learning is the improvement in accuracy. AI algorithms have played a key role in developing more sophisticated neural networks that can learn and recognize patterns with more precision, leading to important advancements in image recognition, natural language processing, and speech recognition. Deep learning models can now achieve higher accuracy rates with AI, making them more reliable and effective in various applications.

Machine learning

AI has had a major impact in machine learning in the ability to process and analyze vast amounts of data more efficiently and accurately. AI algorithms can quickly identify patterns and trends within the data, allowing machine learning models to make more accurate predictions and decisions, leading to advancements in any number of industries where machine learning models are used to analyze complex data sets and provide valuable insights.

What areas can AI be used in IoT?

Threat detection

AI algorithms use machine learning techniques to analyze patterns and behaviors within data sets and identify anomalies that may indicate potential threats. By continuously monitoring and analyzing network traffic, AI-powered anomaly detection systems can quickly identify unusual activity, such as unauthorized access attempts or abnormal data transfers, so that organizations can respond promptly and mitigate risk. AI algorithms can also learn and understand normal user behavior patterns by analyzing vast amounts of data to identify insider threats and security breaches.

Access control

AI can be used in access control systems to provide efficient and secure solutions, such as facial recognition software, which can analyze and authenticate individual faces to grant or deny access to specific areas. Facial recognition technology eliminates the need for physical access cards or passwords, improving convenience and security.

User authentication

AI algorithms can analyze the unique characteristics of individual voices, creating a unique voiceprint to verify a user's identity. Voice recognition technology is particularly useful for phone-based authentication systems as a convenient and secure way to authenticate individuals without the need for additional hardware or devices.

Network security

AI can be used in network security to detect and prevent potential threats, analyze and learn from patterns, and automate security processes, all in real time. Algorithms can identify and flag suspicious activities like unauthorized login attempts or unusual network behavior, allowing a quick, proactive response to potential threats before they cause considerable damage.

Vulnerability detection

AI’s advanced algorithms and machine learning capabilities help to proactively identify and address vulnerabilities by analyzing large amounts of data and identifying potential vulnerabilities in systems or networks to detect anomalies that may indicate a security breach or vulnerability. AI also utilizes code analysis, where algorithms examine lines of software code for potential security flaws or vulnerabilities.

Predictive maintenance

Predictive maintenance refers to the use of data and analytics to predict and prevent equipment failures before they result in downtime, thus improving operational efficiency. AI can play a vital role in this process by analyzing massive amounts of historical and real-time data, identifying patterns, and making accurate predictions about when maintenance is required. AI algorithms can continuously monitor key equipment performance indicators by integrating sensors, IoT devices, and other data collection tools

How can AI improve IoT security?

Anomaly detection

Anomaly detection is the process of identifying patterns or data points that deviate significantly from the expected normal behavior. AI can improve anomaly detection by enabling the creation of models that can identify complex patterns and anomalies in large datasets. AI algorithms can adapt to changing conditions and learn from new data, making them more effective in detecting anomalies from historical data in real time that human analysts might miss.

Real-time threat detection

AI algorithms greatly enhance real-time threat detection by analyzing vast amounts of data to identify patterns or anomalies that may signal a security breach or a potential threat and responding in real time. Continuous monitoring of network traffic, user behavior, and system logs detects abnormalities. Threat detection capabilities can improve over time by leveraging machine learning algorithms to learn and adapt to new threats and provide early warning signs of potential attacks.

Malware detection

AI has the potential to learn to recognize and detect new malware variations and types by training algorithms on large datasets of known malware, allowing cybersecurity systems to thwart cybercriminals by quickly identifying and mitigating emerging threats. AI can also enhance malware detection with behavior-based analysis, enabling AI systems to monitor the behavior of files, processes, and network activities to identify suspicious or malicious activities that may be indicative of malware.

Behavioral analysis

AI algorithms can identify language patterns and trends in behavioral analysis to provide more accurate and detailed insights into human behavior. Information gleaned from analyzing text data from sources like social media posts, customer reviews, and online forums can be invaluable for organizations to better understand target markets. AI-powered behavioral analysis can identify customer preferences, expectations, and sentiments, allowing businesses to tailor their products or services accordingly.

What are some drawbacks to using AI-powered IoT security?


Implementing AI-powered IoT security systems can be a major barrier for small and medium-sized businesses with limited financial resources to allocate toward advanced security measures. The cost of maintaining and upgrading AI-powered IoT security systems can be ongoing as AI technology continues to evolve. Businesses will need to invest in regular software updates, patches, and training for personnel to keep their systems updated and ensure effective protection.

False-positive and false-negative outcomes

False-positive outcomes are instances when an AI system incorrectly identifies a legitimate action or behavior as malicious or harmful, leading to unnecessary alerts or actions, inconvenience, and potential disruptions for users. False-negative outcomes result from failure of an AI system to identify real threats or malicious activities, mistakenly classifying them as benign. False-positive and false-negative outcomes can be attributed to a range of factors like limitations in AI algorithms, data quality, and evolving attack techniques.

Needs substantial amounts of data

AI algorithms need to collect and analyze large amounts of data from various devices and sensors to train and optimize their models. Organizations may find it complex and costly to collect and store so much data, with the added burden of ensuring data quality and accuracy, as any inaccuracies or biases in the training data can lead to flawed AI models and security vulnerabilities, which can be especially taxing for smaller organizations with limited resources.

Risk of attacks targeting AI

Attackers compromise AI-powered IoT security to gain unauthorized access to connected devices and sensitive data, leading to data breaches or unauthorized control of critical infrastructure. Even with the intelligent and adaptive design of AI systems, there are still certain limitations that make them susceptible to skilled hackers. Adversarial attacks coordinated to feed the AI system with manipulated data can cause to objects to be misclassified or fail to detect anomalies, putting the entire network at risk.

Final thoughts

AI and IoT have transformed the way we live and work, but they also present significant security challenges. AI and IoT security are critical in safeguarding our digital infrastructure, privacy, and data. As AI and IoT continue to advance and we come to rely on them more in our working and daily lives, it is important for both individuals and organizations to prioritize security to mitigate the risks and ensure a safe and secure digital ecosystem. This includes robust encryption, access control mechanisms, continuous monitoring, threat intelligence, and proactive security measures to defend against ever evolving threats.

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Tags: Connected Devices Thingworx Industrial Equipment Industrial Internet of Things

About the Author

Anthony Moffa

Anthony Moffa is a Senior Director within PTC’s ThingWorx Product Management team.  He has extensive experience, designing, manufacturing and implementing diagnostic systems in a variety of industries including aerospace, nuclear power and petrochemical.  Prior to joining PTC he was responsible for the design and implementation of 2 IoT programs, one in life safety and the other in the life sciences arenas.  He has been a long-time contributor to service research advisory councils managed by Aberdeen and The Service Council, holds a Mechanical Engineering Degree from Villanova University and has multiple Six Sigma certifications.