What challenges to combating carding will the widespread adoption of IoT devices in payment systems create by 2030?

[Student]

The widespread adoption of IoT devices (the Internet of Things) in payment systems by 2030 will create both new opportunities for convenience and automation, as well as significant challenges in combating card fraud — fraud associated with the theft and use of bank card data. For educational purposes, I will examine in detail the key risks, their causes, practical examples, and possible countermeasures to provide a comprehensive understanding of the problem. I will also consider potential technological and regulatory solutions that can mitigate these threats.

1. Increasing entry points for attacks​

Problem: IoT devices such as smart cash registers, wearables (e.g., smartwatches), smart cars, refrigerators, and even smart home systems with payment functionality are becoming part of the payment ecosystem. Each device represents a potential entry point for attackers. According to research (such as Gartner), the number of connected IoT devices is expected to exceed 25 billion by 2030, and a significant portion of them will support payment functions.

Why is this a challenge for combating carding?

• Weak device security: Many IoT devices have limited computing resources, making it difficult to implement sophisticated security mechanisms such as strong encryption or multifactor authentication. For example, smart light bulbs or low-cost sensors may use outdated protocols such as HTTP instead of HTTPS.
• Lack of updates: IoT device manufacturers often fail to provide regular firmware updates, leaving devices vulnerable to known exploits. For example, in 2016, the Mirai botnet exploited vulnerabilities in IoT devices (cameras, routers) for attacks, and similar scenarios could repeat themselves in payment systems.
• Example: In 2020, hackers exploited vulnerabilities in smart TVs to intercept data, including stored payment information. As the number of devices with payment capabilities grows, such incidents will become more frequent.

Consequences for carding:

• Attackers can hack your device to gain access to stored card data or payment system tokens (such as Apple Pay or Google Pay).
• Compromised devices can be used to spoof transactions or inject malware that redirects payments.

2. Difficulties with authentication​

Problem: IoT devices are often optimized for convenience and automation, simplifying authentication processes. For example, a smart refrigerator that orders groceries can automatically initiate payments without explicit password entry or biometric verification.

Why is this a challenge for combating carding?

• Weak authentication: Devices may rely on single-factor authentication (such as a PIN) or require no verification at all for microtransactions. This makes them vulnerable to attack if an attacker gains access to the device.
• Biometric risks: Some IoT devices use biometric data (voice, fingerprints) for authentication. If the device is hacked, biometric data can be stolen, and the compromise is irreversible, as a person cannot "change" their fingerprint.
• Example: In 2019, researchers showed that voice assistants (such as Amazon Alexa) could be tricked with synthesized voices, potentially allowing unauthorized payments to be initiated.

Consequences for carding:

• Attackers may use stolen credentials or fake biometric data to conduct transactions.
• Automated payments via IoT devices can be used to test stolen cards, making them more difficult to detect.

3. Real-time data interception​

Problem: IoT devices transmit payment data via various communication channels (Wi-Fi, Bluetooth, Zigbee, 5G), which can be vulnerable to man-in-the-middle (MITM) attacks.

Why is this a challenge for combating carding?

• Unsecured communication channels: Some devices use outdated or weak encryption protocols (for example, WEP instead of WPA3 for Wi-Fi), which allows data to be intercepted.
• Open Networks: Devices connected to public Wi-Fi networks (such as those in cafes or airports) are easy targets for attacks.
• Example: In 2021, researchers demonstrated the ability to intercept data via Bluetooth Low Energy (BLE) in wearable devices, which could be used to steal payment tokens.

Consequences for carding:

• Intercepted data (such as tokens or one-time codes) can be used to conduct unauthorized transactions.
• Attackers can introduce malware that redirects payments to controlled accounts.

4. Scaling of fraudulent schemes​

Problem: IoT devices connected to payment systems can be used to automate and scale fraudulent operations.

Why is this a challenge for combating carding?

• Botnets: Compromised IoT devices can be combined into botnets for mass testing of stolen cards. For example, each device could conduct small transactions to verify the validity of a card without raising suspicion.
• Microtransactions: The growing number of devices allows for many small transactions that are harder to track. For example, a smart coffee machine can be programmed to process thousands of $0.01 transactions.
• Example: In 2017, the IoT Reaper botnet infected millions of devices, demonstrating how easy it is to compromise the IoT ecosystem. Similar methods can be adapted for carding.

Consequences for carding:

• Large-scale attacks become more effective because attackers can use thousands of devices simultaneously.
• Traditional anti-fraud systems, which focus on large transactions, may miss microtransactions.

5. Insufficient standardization and regulation​

Problem: The lack of uniform security standards for IoT devices participating in payment systems creates a chaotic ecosystem where the level of protection varies from manufacturer to manufacturer.

Why is this a challenge for combating card fraud?

• Diversity of devices: IoT devices are manufactured by many companies, and not all of them comply with standards such as PCI DSS (Payment Card Industry Data Security Standard).
• Differences in Regulation: Different countries have different requirements for IoT device security. For example, the EU has strict GDPR regulations, while other regions may have minimal regulation.
• Example: In 2022, a study found that 80% of IoT devices on the market did not meet basic security standards, including a lack of data encryption.

Consequences for carding:

• Devices with poor security become the weak link in the payment ecosystem.
• Attackers can exploit jurisdictions with minimal regulation to carry out attacks.

6. Difficulties in monitoring and detection​

Problem: With the growth of IoT devices, transaction volumes will increase dramatically, putting strain on fraud monitoring systems.

Why is this a challenge for anti-carding?

• Data volume: Payment systems will process billions of transactions from IoT devices, making real-time analysis challenging.
• Complex patterns: IoT devices behave differently from traditional transactions (such as automatic payments for electricity or subscriptions), requiring adaptation of anti-fraud systems.
• Example: In 2023, banks reported difficulties detecting fraud in mobile payment systems due to the rise in low-value transactions.

Consequences for carding:

• Fraudulent transactions can get lost in the general data flow.
• Delays in fraud detection increase losses for users and banks.

Countermeasures​

To minimize the risks of carding in the era of mass adoption of IoT devices in payment systems, a comprehensive approach is needed, including technical, organizational, and regulatory measures:

1. Strengthening device security:
   • Encryption: All IoT devices should use modern encryption protocols (e.g. TLS 1.3) to protect data.
   • Multi-factor authentication (MFA): Even for automated transactions, additional checks such as geolocation or behavioral analysis can be implemented.
   • Firmware Updates: Manufacturers must commit to providing regular security updates throughout the lifecycle of a device.
2. Monitoring and analytics:
   • AI and machine learning: Anti-fraud systems must adapt to the analysis of IoT transactions, taking into account their unique patterns. For example, algorithms can detect anomalies, such as unusual transaction times or locations.
   • Real Time: Investments in big data processing systems will enable transactions to be analyzed in real time, minimizing delays.
3. Standardization:
   • Global Standards: Developing and implementing unified security standards for IoT devices involved in payments. For example, the PCI DSS extension for IoT.
   • Certification: Mandatory certification of IoT devices before their use in payment systems.
4. User education:
   • Users should be aware of the need to set secure passwords, disable unnecessary features (such as Bluetooth when not in use), and keep their devices updated regularly.
   • Awareness campaigns can reduce the likelihood of device compromise.
5. Blockchain and decentralization:
   • Using blockchain technology to create transparent and immutable records of transactions could make fraud more difficult.
   • Payment tokenization (replacing card data with unique tokens) is already widely used, but it can be improved for IoT.
6. Regulatory measures:
   • Introducing strict requirements for IoT device manufacturers, including mandatory vulnerability testing.
   • International cooperation to harmonize security standards and combat cybercrime.

Forecast to 2030​

Hello! By 2030, IoT devices will become an integral part of payment systems, leading to exponential growth in the number of transactions. This will create new opportunities for carders, especially in regions with low levels of regulation. However, the development of technologies such as AI, blockchain, and quantum encryption can significantly improve security. For example:

• AI systems will be able to predict and prevent fraudulent transactions with over 99% accuracy.
• Blockchain can provide decentralized verification of transactions, reducing reliance on vulnerable central servers.
• Regulators are likely to introduce stricter standards, such as mandatory certification of IoT devices for payment transactions.

However, without coordinated efforts from manufacturers, banks, regulators, and users, the risks of card fraud will grow proportionally with the spread of the IoT. A key factor for success will be balancing convenience and security.

Illustration of challenges​

Although you didn't request a graph, I can offer a visualization for educational purposes, such as a graph showing the projected growth of IoT devices and the associated increase in cyberthreats. If you'd like, I can create such a graph (for example, using data on IoT device growth and carding incidents). Please let me know if you're interested.