Despite making significant investments in security technologies, organizations continue to struggle with security breaches: Their adversaries are quick to evolve tactics and stay ahead of the technology curve. Humans may soon be overwhelmed by the sheer volume, sophistication, and difficulty of detecting cyberattacks. People are already challenged to efficiently analyze the data flowing into the security operations center (SOC) from across the security tech stack. This doesn’t include the information feeds from network devices, application data, and other inputs across the broader technology stack that are often targets of advanced attackers looking for new vectors or using new malware. And as the enterprise increasingly expands beyond its firewalls, security analysts are charged with protecting a constantly growing attack surface.
source: The future of cybersecurity and AI | Deloitte Insights
Applied Sciences | Special Issue: Smart Cyberspace and IoT Systems: Challenges and Future Trends (mdpi.com)
Some of the most significant cybersecurity challenges in IoT critical infrastructures
1. There is a lack of standardization in the IoT ecosystem, which makes it difficult to implement security protocols consistently. As a result, IoT devices may have vulnerabilities that can be exploited by cybercriminals.
2. IoT devices are often distributed across large geographical areas and are vulnerable to physical tampering or theft. This can compromise the integrity of the data collected and transmitted by these devices.
3. IoT devices often collect and transmit sensitive data, and there is a risk that this data can be intercepted or stolen by cybercriminals. This can lead to significant privacy concerns for individuals and organizations.
4. Many IoT devices have limited computing resources and may not have the processing power to implement robust security protocols. This makes them vulnerable to attacks such as denial-of-service (DoS) attacks.
The most critical cybersecurity challenges in IoT critical infrastructures include device vulnerabilities, data privacy and security, and lack of standardization. Recent AI-driven cybersecurity implementations include behavior-based threat detection, anomaly detection, and AI-powered threat intelligence.
Convergence around safety and security is having a significant impact on the cybersecurity challenges in IoT cyber critical infrastructures. It involves bringing together stakeholders from different disciplines, such as engineering, IT, and cybersecurity, to work together to ensure that IoT systems are secure and safe. Please see my paper titled "The Impact of Uncertified Practitioners on the Safety and Security of Critical Infrastructure Projects and Operations in the Operational Technology Domain" See also my Ph.D. thesis on "Safety and Information Security Fusion in Operational Technology, Towards a New Convergent Holistic Risk Management Model" and my paper on "The role of Artificial Intelligence in predicting and preventing cyber-attacks in critical infrastructure" for further inputs.
Further, AI in cybersecurity is an exciting development that has the potential to significantly improve the security of IoT systems, particularly in critical infrastructure settings.
The following are some ways in which convergence around safety and security is impacting cybersecurity challenges in IoT cyber critical infrastructures:
My response give to the Question: "How would a coordinated global cyber attack impact critical infrastructure and what measures can be taken to prevent and mitigate such an event?" | here on ResearchGate may also be helpful.
The IoT (Internet of Things) has revolutionized the way we live, work, and interact with technology. However, it has also brought about significant cybersecurity challenges, particularly in cyber critical infrastructures such as power grids, transportation systems, and healthcare facilities.
Some of the key cybersecurity challenges in IoT cyber critical infrastructures include:
To address these challenges, there is a growing need for convergence around safety and security as the key response. This means that security must be integrated into the design, development, and deployment of IoT systems from the outset, rather than being an afterthought.
In recent years, AI-driven cybersecurity implementations have become increasingly popular, particularly in the area of threat detection and response. One example of AI-driven cybersecurity implementation is the application of AI to PLCs (Programmable Logic Controllers). PLCs are commonly used in industrial control systems (ICS), which are used to control and monitor critical infrastructure systems such as power plants and water treatment facilities.
*Applying AI to PLC capabilities in OT environments*
By applying AI to PLCs, it is possible to detect anomalies and potential cyber threats in real-time. For example, AI can be used to detect unusual patterns in the data being processed by the PLC, which may indicate a cyber attack. AI can also be used to identify vulnerabilities in the PLC's code and configuration, which can then be addressed to improve the system's security.
The sheer scale and complexity of IoT systems make it challenging to secure them effectively. IoT devices and systems have diverse architectures, making it difficult to implement a unified security approach. There is a lack of common standards for IoT devices and systems, which can make it difficult to ensure consistent security across different systems with many IoT devices having limited resources, such as processing power, memory, and battery life, which can make it challenging to implement strong security measures. finally many critical infrastructure systems are based on legacy technology that may not have been designed with security in mind.
I specialize on the topic of convergence in IT/OT therefore I see convergence around safety and security as a key challenge. Convergence includes facilitating better collaboration between different stakeholders, allowing them to work together to identify and address cybersecurity challenges. This collaboration ensures that security is integrated into the design, development, and deployment of IoT systems from the outset.
Convergence around safety and security promotes the use of common standards and best practices for IoT security. This approach ensures that all systems are secured in a consistent and effective manner, reducing the risk of vulnerabilities or weaknesses in IoT systems.
Convergence around safety and security is enabling stakeholders to perform risk assessments and management, helping to identify potential threats and vulnerabilities in IoT systems. This approach ensures that security measures are tailored to the specific risks associated with each IoT and finally convergence around safety and security promotes the use of continuous monitoring and improvement for IoT security. This approach ensures that security measures are updated and improved regularly, to keep up with evolving threats and vulnerabilities.
The rising interconnection and complexity of IoT (Internet of Things) cyber critical infrastructures presents substantial cybersecurity challenges. In key infrastructure industries like energy, transportation, healthcare, and manufacturing, IoT devices increase cybersecurity risks. This domain faces several cybersecurity issues:
1. Diverse and Insecure IoT Devices:
IoT devices vary in security and may lack fundamental functions. Cyberattacks target these devices because to their variety and weaknesses.
2. Large Attack Surface: - The large number of IoT devices and their integration into vital systems provide cybercriminal chances.
3. **Data Privacy and Integrity** - IoT devices generate significant, sensitive data. Especially against sophisticated cyber attacks, protecting this data is difficult.
Traditional network security solutions may not be successful in the diverse and dynamic IoT environment, making it tough to secure network connections between IoT devices and other infrastructure.
Scalability of security solutions is crucial to support the growing number of IoT devices. Due to IoT networks' scale and complexity, traditional security methods may fail.
The lack of globally acknowledged security standards for IoT devices and systems hinders the development and deployment of appropriate security measures.
7. Resource Constraints: - Limited processing power and battery life hinder advanced security mechanisms in IoT devices.
8. Software Updates and Patch Management: - IoT device updates and patches can be difficult in large deployments. Unpatched devices can be hacked.
AI-driven cybersecurity is growing in response to these concerns. Recent AI-driven IoT cybersecurity solutions include:
In real-time, AI algorithms may detect irregularities in network traffic and device behaviour, perhaps indicating a cyber attack.
2. Predictive Security Analytics: - AI-based analysis of past data to anticipate and prevent security incidents.
3. Automated Security Incident Response: - AI systems can quickly respond to security problems, minimising damage.
4. Behavioural Biometrics: - AI-driven authentication methods for safe IoT access control by analysing user behaviour patterns.
5. AI in Threat Intelligence: - Analysing large datasets from various sources to identify emerging threats, vulnerabilities, and attack strategies.
6. Smart Grid Security: - AI assists in energy sector security by monitoring and analysing grid data to detect anomalies and assaults.
7. Healthcare Device Monitoring: - AI algorithms detect tampering or anomalous activity in linked healthcare equipment, safeguarding vital health data.
8. Edge Computing Security: - Using AI at network edges for faster data processing and security analysis near IoT devices.
These AI-driven solutions reduce IoT threats in critical infrastructures, but AI is not a panacea and must be part of a multi-layered security approach. Cybersecurity methods must change with the IoT landscape.
1. IoT Security Issues:
Robert H. Weber (2010). "Internet of Things – New security and privacy challenges." CLR 26(1):23-30.
Roman, Zhou, & Lopez (2013). "On the features and challenges of security and privacy in distributed internet of things." Computer Networks, 57(10), 2266-2279.
2. AI in Cybersecurity: - Apruzzese et al. (2018). "On the effectiveness of machine and deep learning for cybersecurity." 2018 International Conference on Cyber Security and Digital Services Protection (Cyber Security), 1-8. IEEE. (2016). Buczak, A. L., & Guven, E. "A survey of data mining and machine learning methods for cybersecurity intrusion detection." IEEE Communications Surveys & Tutorials 18(2):1153-1176.
3. Anomaly Detection and Predictive Security Analytics: - Ahmed et al. (2016). JNCA 60:19–31: "A survey of network anomaly detection techniques."
Muaaz, M., & Mayrhofer, R. (2017) discuss behavioural biometrics and IoT security. "An analysis of different approaches to gait recognition using cell phone based accelerometers." International Conference on Advances in Mobile Computing & Multimedia Proceedings (293-300).
5. Smart Grid and Healthcare Device Security:
Lu, Z., & Wang, W. (2013). "A survey on recent advances in vehicular network security, trust, and privacy." IEEE Intelligent Transportation Systems 16(2):760-776.
6. Edge Computing for IoT Security: - Shi et al. (2016). "Edge computing: Vision and challenges." IEEE IoT Journal 3,5:637-646.
These recognised journals and conferences provide a complete review of IoT cybersecurity problems and AI in cybersecurity. These are available through academic databases and libraries.
While AI shows great promise for bolstering IoT cybersecurity, real-world deployments also face hurdles that will require ongoing effort from both industry and research. Here are a few thoughts on challenges and opportunities as these technologies continue advancing:
Limited data remains a constraint. Most cybersecurity AI still depends on large, high-quality datasets -- which are scarce in many niche IoT domains like industrial control systems. Targeted data collection and sharing initiatives could help, but sensitivity around exposing vulnerabilities slows progress.
Multi-factor authentication is crucial but tricky. Behavioral biometrics may one day seamlessly ID users, but perfecting subtle human patterns into code introduces margin for error. Pairing biometrics with traditional credentials reduces risk, though managing many factors adds overhead.
Anomalies detection requires calibration. "Normal" for one network could mean something very different elsewhere. Without careful tuning to local contexts, anomaly alerts risk crying wolf. Ongoing network analysis and feedback loops help machines better grasp what's anomalous versus mundane.
Explanation is everything for trust. Even with accuracy, security AI remains a black box to most.But to gain acceptance, especially for mission-critical systems, stakeholders need visibility into how and why alerts occur. Techniques like XAI could offer needed look under the hood.
In short, while the power of AI is unquestionable, building robust, trustworthy cyber defenses demands solving sociotechnical challenges as much as computational ones. With careful human-centric design and open collaboration across perspectives, I'm hopeful these complementary technologies can team up to deliver stronger IoT security for all. But it will take dedication and patience to realize their fullest potential.
- Badges
- Science topic
- Similar topics
- Methodology
- Research Methodology
- Research Papers