The term “honeypot” has its origins in the world of espionage, but these days, the digital version of a honeypot has become a useful cybersecurity tool. Cyber honeypots attract hackers by mimicking legitimate targets such as servers, databases, websites or applications.
“These systems are intentionally configured to appear vulnerable to lure adversaries. Once cybercriminals interact with the honeypot, the security team can monitor their behavior, gather intelligence about their methods and tools, and use this information to strengthen defenses or divert them away from critical assets,” says Ram Chandra Sachan, a co-author of a new research paper titled “AI-Driven Adaptive Honeypots for Dynamic Cyber Threats.”
As a solution, this tactic aligns with the “not if but when” mindset that IT admins should take regarding cybersecurity amid the consistent rise in cyberattacks on schools.
Since the emergence of honeypots in the 1980s, these decoy systems have evolved and are now pivotal to enhancing cybersecurity defenses. But a new and improved version is on the rise: the AI-enhanced honeypot.
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“Using data sets of attacker-generated commands and responses, these models are trained to mimic server behaviors convincingly. Techniques such as supervised fine-tuning, prompt engineering and low-rank adaptations help tailor these models for specific tasks,” explains Hakan T. Otal, a Ph.D. student in SUNY Albany’s Department of Information Science and Technology.
AI-powered honeypots leverage advances in natural language processing and machine learning, such as fine-tuned large language models (LLMs), to create highly interactive and realistic systems.
Pros and Cons of AI-Powered Honeypots
Boosting a honeypot with artificial intelligence enables dynamic and realistic interactions with attackers, improving the quality of data collected. Models can evolve to respond to emerging attack tactics through reinforcement learning.
Sachan points out that creating AI honeypots can also result in faster deployment; drastic reductions in deployment costs; and more realistic and highly convincing honeypots that mimic real network activity, traffic patterns and logs. Leveraging AI for honeypot maintenance can lead to improved threat detection accuracy and the evolution and adaptation of honeypots based on new attack methods, making them more difficult for hackers to identify.
Using data sets of attacker-generated commands and responses, these models are trained to mimic server behaviors convincingly.”
Hakan T. Otal
Ph.D. Student, SUNY Albany
On the other hand, there are still challenges when using AI-powered honeypots, including static behaviors and predictable patterns that can make them detectable by attackers, Otal says.
Moreover, while deployment costs could be cut, the fine-tuning and maintaining of AI models still require significant investment in hardware, software, licenses and the hiring of skilled AI professionals.
How Do AI-Powered Honeypots Benefit K–12 Schools?
“AI-enhanced honeypots can act as an early warning system against the increasing number of cyberattacks being experienced by schools and divert attackers away from critical systems used to store and maintain sensitive student data, reducing the likelihood of successful breaches,” Otal explains. “This system can also detect and log malicious activity to provide actionable insights for improving school cybersecurity.”
This unique security feature also has educational value; Sachan points out that schools can use honeypots to help educate IT staff and students about cybersecurity risks and defenses.
1983
A researcher attempts to lure hackers into the first recorded cyber honeypot.
Source: metallic.io, “Honeypots: A walk down memory lane,” July 7, 2021
What Can Schools Do Now in Lieu of AI-Powered Honeypots?
Until budgets allow for the deployment of sophisticated AI-enhanced honeypots, Otal recommends, schools should focus on foundational cybersecurity measures to prevent data theft, including:
- Network security tools: Ensure firewalls, intrusion detection systems and endpoint protection platforms are running and up to date.
- Data encryption: Secure sensitive student and staff data through robust encryption methods.
- Regular updates and patching: Keep systems and software updated to mitigate vulnerabilities.
- Backup systems: Implement regular, secure backups to ensure data recovery after an incident.
It’s also important to train staff and students to recognize phishing attempts and practice good cybersecurity hygiene, Sachan says. “Any security systems are only as strong as their weakest link.”
DIVE DEEPER: States step up to help schools fight hackers.
AI-Enhanced Honeypots in Future K–12 Cybersecurity
Even if not implemented immediately, AI-enhanced honeypots could play a key role in future security strategies as schools continue to improve and upgrade their technology and as the integration of LLMs brings about a more adaptive and sophisticated security infrastructure.
However, Otal notes, it’s still “important to balance these technological advances with accessibility and ethical considerations. Collaboration across academia, industry and public sectors will be critical in making these innovations practical and beneficial for all.”
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