What challenges will the growth of connected IoT devices pose to combating carding?

[Student]

The growth of connected Internet of Things (IoT) devices significantly complicates the fight against carding—fraud involving the use of stolen credit card information. IoT devices such as smart speakers, cameras, thermostats, home appliances, and even cars are becoming increasingly common, with their number projected to exceed 30 billion by 2030. This creates new challenges for cybersecurity, particularly in the context of carding prevention. Let's take a closer look at these challenges and their educational implications.

1. IoT device vulnerabilities as an entry point for carding​

Problem: Many IoT devices have weak security, making them vulnerable to attack. Key weaknesses include:

• Weak or default passwords: Devices often come with preset passwords (e.g. "admin/admin") that users rarely change.
• Lack of firmware updates: Manufacturers of low-cost IoT devices may not provide regular updates, leaving devices vulnerable to known exploits.
• Insecure communication protocols: Some devices use outdated or insecure protocols (such as HTTP instead of HTTPS), which allows data to be intercepted.
• Limited resources: IoT devices often have minimal computing power, which limits the ability to implement complex encryption mechanisms or attack protection.

Carding Connection: Attackers can hack IoT devices and use them to:

• Forming botnets such as Mirai to conduct mass attacks, including testing stolen card data (credential stuffing or brute force).
• Interception of financial data if the device has access to payment systems (for example, smart speakers used for shopping).
• Creating proxy networks to mask fraudulent traffic, making it difficult to track carders.

Educational aspect: This emphasizes the importance of developing and implementing cybersecurity standards for the IoT. Students and professionals should understand how to design devices with "security by design" principles, including mandatory encryption, two-factor authentication, and regular updates.

2. Scaling attacks through IoT botnets​

Problem: The growing number of IoT devices is increasing the scale of distributed attacks. Any device can become part of a botnet—a network of infected devices controlled by attackers. Such networks are used to:

• DDoS attacks: Overloading the servers of online stores or payment systems, which allows carding to be carried out amid chaos.
• Mass card testing: Carders use botnets to check the validity of thousands of stolen cards simultaneously, distributing requests across multiple devices.

Carding Connection: Botnets composed of IoT devices enable automated and scalable attacks. For example, attackers can send small transactions from thousands of devices to check whether a card is active, evading detection by security systems.

Educational Aspect: This demonstrates the importance of studying network security and anomaly detection methods. Students should understand how botnets operate to develop effective defense systems, such as behavioral traffic analysis or intrusion detection systems (IDS). It is also important to understand how distributed systems work and how they can be exploited for malicious purposes.

3. Anonymity and geographical distribution of attacks​

Problem: IoT devices are connected to networks around the world, allowing attackers to:

• Use devices in different countries to create anonymization chains (for example, via VPN or proxy).
• Distribute attacks geographically to bypass regional blocks or filtering systems.

Carding Connection: Carders can route requests through IoT devices to mask their IP address and location. This complicates fraud detection systems, which often rely on geographic and behavioral patterns.

Educational Aspect: This challenge highlights the importance of learning anonymization methods and how to counter them. Students should understand technologies such as Tor, VPNs, and proxies, as well as methods for analyzing network traffic to identify suspicious nodes. This also highlights the need for international cooperation in the fight against cybercrime.

4. Collecting data for carding via IoT​

Problem: Many IoT devices collect personal data, including:

• Financial information (for example, through voice assistants used for shopping).
• Behavioral data (e.g. shopping habits tracked by smart devices).
• Location or network connection data.

If the device is compromised, this data can be stolen and used to:

• Direct access to payment data.
• Social engineering to obtain additional data (such as passwords or verification codes).

Connection to Carding: Stolen data from IoT devices can be used to create profiles of victims, facilitating targeted attacks or the direct use of cards in fraudulent transactions.

Educational aspect: This emphasizes the importance of data security at the device and network level. Students should learn the principles of data encryption (at rest and in motion), as well as leak protection methods such as tokenization and data anonymization. It is also important to understand how attackers use social engineering to exploit collected data.

5. Limited resources of IoT devices​

Problem: IoT devices often have limited processing power and memory, making it difficult to:

• Implementation of complex encryption algorithms.
• Implementation of real-time attack detection systems.
• Processing large volumes of logs for security analysis.

Carding: The lack of advanced security mechanisms makes devices vulnerable to attacks, including carding. For example, a compromised device could be used to send fraudulent requests undetected.

Educational aspect: This highlights the need to develop lightweight yet effective cybersecurity solutions. Students should explore optimizing security algorithms for resource-constrained devices, as well as alternative approaches, such as cloud-based security analytics or the use of gateways for traffic filtering.

6. Difficulty monitoring and analyzing IoT traffic​

Problem: IoT devices use a variety of protocols (Zigbee, Z-Wave, MQTT, CoAP), which complicates:

• Network traffic monitoring.
• Detecting anomalies related to carding.
• Integration of IoT into existing fraud detection systems.

Carding: Traditional security systems focused on web traffic or transactions may not be adapted to analyze IoT traffic. This allows attackers to use devices to conduct undetected attacks.

Educational aspect: This emphasizes the importance of studying IoT protocols and big data analytics methods. Students should understand network protocols as well as machine learning technologies for detecting anomalies in heterogeneous data. Understanding the architecture of IoT networks and their interaction with cloud services is also critical.

7. Regulatory and ethical challenges​

Problem:

• Lack of standards: Many countries lack strict security requirements for IoT devices, allowing manufacturers to release vulnerable products.
• Privacy concerns: Data collection by IoT devices can violate user privacy, and their compromise increases the risks.

Connection to carding: Lack of regulation allows attackers to exploit devices without serious legal consequences. Data leaks from IoT devices can directly fuel carding.

Educational aspect: This highlights the importance of studying the legal and ethical aspects of cybersecurity. Students should understand how to develop policies and standards for the IoT, as well as consider the balance between device functionality and privacy protection.

Solutions and approaches to risk minimization​

To address the challenges posed by IoT and carding, a comprehensive approach is needed:

1. Strengthening safety standards:
   • Mandatory firmware updates and complex default passwords.
   • Implementation of standards such as the NIST IoT Cybersecurity Framework or OWASP IoT Security Guidance.
2. Network segmentation:
   • Isolate IoT devices from critical systems (e.g. payment gateways) using VLANs or firewalls.
3. Monitoring and analytics:
   • Using AI and machine learning to analyze IoT traffic in real time.
   • Development of anomaly detection systems adapted to IoT protocols.
4. User education:
   • Raising awareness of the need to configure and update IoT devices.
   • Training in basic cyber hygiene (e.g. changing passwords, disabling unnecessary features).
5. Regulation:
   • Introducing mandatory security standards for IoT device manufacturers.
   • Fines for releasing vulnerable products.
6. Technological innovations:
   • Developing lightweight encryption protocols for IoT.
   • Using blockchain to ensure the integrity of data and transactions.

Conclusion​

The growth of IoT devices creates new opportunities for carders, but also drives the development of cybersecurity. These challenges highlight the importance of a multidisciplinary approach that encompasses technical, legal, and educational measures. For students and cybersecurity professionals, this means a deep understanding of IoT technologies, network security, data analysis, and ethical considerations. Studying these topics will help prepare professionals capable of effectively countering new threats in the era of the Internet of Things.