Guardians of AIoT: Protecting Smart Devices from Data Poisoning

What if the smart thermostat in your home decides that winter is the perfect time for you to experience tropical heat or your self-driving car interprets a stop sign as a green light? These unstable situations sound scary like science fiction, but it highlights the threats that surround artificial intelligence of things (AIoT) systems.

Numerous devices today that shape modern life, such as smart homes, industrial machines, smart gadgets, healthcare systems, etc., are powered by AIoT (AI and IoT) devices. The technological advancements undoubtedly promise unmatched convenience and efficiency, but at the same time expose us to complex vulnerabilities. In this article, I will be highlighting the threat of data poisoning where attackers manipulate the datasets used to train or deploy AI models and cause them to behave in unpredictable ways. Let’s dive deeper into why data poisoning attacks are so dangerous, how they take advantage of the unique features of AIoT systems and, most importantly, what we can do to defend against these attacks.

Understanding Data Poisoning

We first need to understand how AI models learn. Machine learning algorithms rely on datasets to identify and predict patterns. The quality and completeness of this data determines the performance of the model is determined by the quality and completeness of this data. Data poisoning attacks tamper the knowledge of the AI by introducing false or misleading information and usually following these steps:

The attacker manipulates the data by gaining access to the training dataset and injects malicious samples

• The AI is now getting trained on the poisoned data and incorporates these corrupt patterns into its decision-making process
• Once the poisoned data is deployed, the attackers now exploit it to bypass a security system or tamper critical tasks.

For example, Tay chatbot, launched by Microsoft in 2016, was designed to imitate the patterns of human conversation. On the contrary, attackers infiltrated the training content with offensive content and within hours inappropriate tweets were posted! Similarly, the spam-filter’ feature of Gmail was manipulated by malicious actors to classify illegitimate emails as novice.

The addition of AI into IoT ecosystems has intensified the potential attack surface. Traditional IoT devices were limited in functionality, but AIoT systems rely on data-driven intelligence, which makes them more vulnerable to such attacks and hence, challenge the security of the devices:

• AIoT devices collect data from different sources which increases the likelihood of data being tampered.
• The poisoned data can have catastrophic effects on the real-time decision making.
• Many IoT devices possess limited computational power to implement strong security measures which makes them easy targets for these attacks.

Detect and Mitigate Data Poisoning

The strategies range from technical mediations to policy-level safeguards:

• The first line of defense is to ensure the training data is of good quality. Data validation can be done by:
  • Removing the anomalies/outliers from the dataset prior to training.
  • Cross checking the integrity of data through hashing techniques
• Datasets can be examined for unusual patterns with the help of unsupervised learning algorithms which can indicate the poisoning of data. Dynamic environments such as smart grids or autonomous vehicles are more suitable for real-time anomaly detection.
• AI models can be taught to recognize and resist malicious inputs with the help of adversarial training which consists of simulating real time attacks. This proactive approach will build resilience against known and emerging threats.
• Every technical measure needs a strong data governance policy. This can be achieved by:
• Enforcing strict access controls for data sets.
• Maintaining audit logs of data usage and modifications.
• Regularly monitoring the AI models for vulnerabilities.

Nisos cybersecurity conducted a thorough study and revealed that from a theoretical concern, data poisoning has now advanced to a pressing reality! The analysis focuses on trends over the past several years and depicts a sharp increase in the number of recorded attacks and their economic consequences.

The graph illustrating the trends in Data Poisoning Attacks (2018-2022). Source: Nisos Cybersecurity.

An upward trend in both attack frequency and economic damage, points out the critical need for dynamic security measures fitted to AIOT systems. To prevent the concerning trends, industry-wide alliance between cybersecurity organizations, policy makers and technology partners is required.

Most attacks in 2018 involved simple methods, such as adding mislabeled data to publicly available datasets and often targeted less critical systems, like conducting minor financial fraud. or spam filtering. Eventually, by 2022, attackers started to use advanced strategies such as inserting malicious samples that bypass anomaly detection systems. Industries like healthcare and automated transportation become the primary target where the repercussions of these threats can be life threatening.

According to the graph, the quickening financial impact reflects the increased dependency on AIoT systems across industries. By 2020, industrial IoT organizations experienced huge losses due to the poisoned data that tampered the algorithms. By 2022, cybersecurity organizations noted that the cost of managing toxic datasets included new training models, improving compromised systems and legal liability for affected customers.

Attackers usually target critical industries like healthcare and tamper patient tracking systems and introduce incorrect metrics. In one such instance, a hospital’s AI powered diagnostic tool misclassified an emergency as non-essential due to data poisoning and lifesaving intervention was delayed for the patient. Similarly, in autonomous vehicle industry, tampering with the image recognition systems can cause traffic signs to be misinterpreted leading to recalls and costly security patches. These increased threats emphasize the importance of proactive measures such as adversarial training, real-time anomaly detection and a strong regulatory framework to protect the future of AIOT systems.

The Future of AIoT security

Looking ahead, avoiding data poisoning attacks requires alliance between developers, researchers, and policymakers. Evolving technologies such as blockchain and federated learning are seeming beneficial for increasing data integrity and minimizing the risk of data poisoning.

Blockchains can help with decentralized data storage by constructing tamper-proof records. This technique safeguards the accuracy of the dataset used to train the AI models. Federated learning technology trains AI models within the device and lowers the need for centralized data collection and hence, regulates exposure to data poisoning attempts.

The incorporation of AI with IoT devices has opened remarkable possibilities but at the same time has made systems more vulnerable and prone to sophisticated attacks like data poisoning. As industry leaders, it is our shared responsibility to predict these risks and reinforce our defenses. We can ensure that smart devices remain trustworthy and secure if we combine technical revolutions with robust governance practices.

Please feel free to reach out to me for more information and further discussion on this topic.

About the Author

Manav Mittal is a seasoned Project Management Expert specializing in Automation within the utility, oil, and gas industries. With over nine years of experience, Manav has honed his skills in delivering multi-million-dollar projects with exceptional precision and efficiency. His expertise is backed by PMP and CSM certifications, and he is known for his ability to seamlessly manage tasks, solve complex problems, and mitigate risks, all while fostering excellent communication and collaboration among his teams. He leads cross-functional teams on diverse projects, including construction, IT, strategy, and automation. Manav has extensive experience handling high-risk automation projects in the oil and gas industry. He has successfully implemented SCADA software, modem upgrades, smart metering, Remote Terminal Units (RTUs), Programmable Logic Controllers (PLCs), Human Machine Interfaces (HMIs), and Burner Management Systems. As a subject matter expert in automation, Manav excels at integrating these technologies with minimal disruption to day-to-day operations.

Manav Mittal can be reached online at [email protected] and at his LinkedIn https://www.linkedin.com/in/manav-mittal-project-manager.