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Enhancing IoT Security with Artificial Intelligence

This article explores the role of AI powered IoT security and its industry impact. It covers AI's applications in threat detection, access control, authentication, network security, vulnerability detection, and predictive maintenance. Additionally, it addresses limitations of AI in IoT security, including data requirements, false positives/negatives, AI system vulnerabilities, and implementation costs.

The Internet of Things (IoT), which connects objects and provides us with valuable information, has become an essential part of our daily lives. However, as the number of IoT devices continues to grow, ensuring their security against cyber-attacks has become a significant challenge.

According to research report by Checkpoint, in the first two months of 2023, there has been a 41% rise in the weekly average of attacks per organization aimed at IoT devices, as compared to 2022. On a weekly basis, approximately 54% of organizations face attempted cyber-attacks on their IoT devices.

Artificial intelligence (AI) can play a pivotal role in securing the IoT by enabling real-time threat detection and response, identifying vulnerabilities, and enhancing security protocols. This article delves into the significance of AI powered IoT security and examines its influence on the industry. Typically, an Arduino board or Raspberry Pi board is used to integrate various sensors and servo motors into an IoT system. The selected item, once equipped with this integrated circuit, gains the capability to generate data, whether structured or unstructured. By leveraging the analysis of this data, these devices become intelligent. This is where Artificial Intelligence comes into action.

The traditional focus of vulnerability management businesses has been on identifying vulnerabilities in network settings and devices. As device identification and data collection capabilities become increasingly important, vulnerability management remains a critical aspect of IoT security. However, it is worth noting that traditional vulnerability management companies were not initially designed to address IoT device sensing, even though they are now expanding their capabilities to accommodate a wider range of devices. Consequently, specialized companies like CyberX and others, specifically dedicated to IoT security, particularly in the operational technology (OT) space, have emerged to meet the market demand.

As the development of a defence-in-depth solution for IoT progresses, these specialized options present viable choices that warrant thoughtful consideration from the market. While it may be an oversimplification to consider IoT devices merely as another type of device that should not be inherently trusted, adopting this strategy can create an environment that enhances the security of all devices, including IoT.

AI Can Be Applied in Various Areas of IoT Security, Such As

  1. Threat detection: AI can detect known and unknown threats in real-time by analyzing network traffic, system logs, and other data sources. Additionally, it can identify malware and other malicious activities.
  2. Access control: Enhance access control mechanisms in IoT devices by scrutinizing user behavior and detecting anomalies with AI. With AI, you can also identify and prevent unauthorized access to IoT devices.
  3. User authentication: Biometric identity and multi-factor authentication are two examples of user authentication systems that AI can improve. It can examine user behavior and identify irregularities, such as login attempts coming from unusual locations.
  4. Network security: Monitor network traffic to identify suspicious activities and potential threats and examine data from various sources, including firewalls and intrusion detection systems, to detect and respond to attacks in real-time with artificial intelligence.
  5. Vulnerability detection: AI can identify vulnerabilities in IoT devices and applications by analyzing code and configuration files. It can also simulate attacks to uncover weaknesses and provide recommendations for security updates and patches.
  6. Predictive maintenance: Predict potential device failures and security vulnerabilities with the assistance of AI, which enables the analysis of historical data and patterns. AI can proactively identify and address vulnerabilities before they are targeted by cyber attackers, ensuring proactive security measures.


Artificial Intelligence (AI) has numerous applications in Internet of Things (IoT) security. By leveraging machine learning algorithms and other AI approaches, organizations can enhance their ability to detect and respond to cyber threats in real-time.

Here Is Some Ways AI Can Improve IoT Security

  • Anomaly detection: Algorithms can analyze sensor data and network traffic to identify anomalous behaviors that may indicate the presence of a cyber threat. For example, if a device starts transmitting unusually enormous amounts of data, an AI-based security solution can detect this anomaly and alert the appropriate security team.
  • Real-time threat detection: AI can monitor system logs, network activity, and other data sources to detect both known and unknown cyber threats. By utilizing machine learning techniques, AI can continuously learn and adapt to new dangers, enhancing its detection capabilities over time.
  • Malware detection: By utilizing AI, examining files and source code becomes a powerful method for detecting malware, including zero-day attacks. The ability of AI to identify and prevent malware infections in IoT devices significantly enhances the overall security posture.
  • Behavioral analysis: AI can monitor the behavior of IoT devices and identify suspicious activities that may indicate a cyber-attack. For example, if an IoT device starts communicating with a new server, an AI-based security solution can detect this and alert the security team.

Despite its numerous advantages, AI powered IoT security also presents certain drawbacks. The 2016 Mirai bot attack served as a wake-up call, highlighting the potential for any internet-connected device to be utilized as a tool to target critical infrastructure, disrupting telecommunications, energy, and other vital systems we rely on daily. As a result, the focus on the proliferation of IoT devices and their impact on network and cloud infrastructures intensified, becoming a top priority. This marked a turning point for both consumers and businesses, as the consumerization of IoT attacks became a significant concern.

Some Drawbacks of AI Powered IoT Security Include

  1. The need for a substantial amount of data to build effective AI models.
  2. The potential for false-positive and false-negative outcomes, leading to incorrect threat detection or missed security risks.
  3. The risk of attacks targeting AI systems themselves, exploiting vulnerabilities and compromising their effectiveness.
  4. The cost associated with implementing AI-based security solutions, including the investment in infrastructure, training, and ongoing maintenance.

From eInfochips

For any organization, ensuring the security of IoT devices is crucial. To protect assets, data, and infrastructure from potential threats, it is important for organizations to implement robust security measures for their IoT devices. By implementing such measures, organizations can reduce the risk of successful attacks and minimize the impact in case an incident occurs.

eInfochips has played a pivotal role in assisting companies in managing security products on a global scale. We specialize in securing connected device networks across various layers, including device connectivity and applications. Our strategic, transformational, and managed operations approaches have enabled us to provide comprehensive cybersecurity expertise. This includes threat modelling and conducting Vulnerability Assessment and Penetration Testing (VAPT) across OT/IoT devices, operating systems/firmware, web/mobile applications, data, and cloud workloads. Our practices align with industry standards, regulations, and guidelines such as NIST, ENISA, OWASP, MITRE, and the IoT Security Foundation.

Here’s a case study where we used AI & IoT to for physical threat detection.

If you are looking to enhance the security of your operations in the most efficient manner, we invite you to get in touch with us.

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Hardik Gohil

Hardik Gohil works as an engineer in the Cybersecurity domain at eInfochips. He has more than 2 years of experience in Cyber Security and Web application development including application security, Vulnerability Management, Risk Management, and secure code development.

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