Protecting Cyber Data with Honeypots

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Decoys are an essential part of a comprehensive cybersecurity strategy. Their main goal is to identify vulnerabilities in the existing system and distract the hacker from legitimate purposes. Assuming that an organization can also collect valuable information from intruders inside a trap, decoys can help it prioritize and focus its cybersecurity efforts based on the methods or the most commonly used assets. The main advantages are the simplicity of analysis, cost-effectiveness, and the ability to identify internal threats.

Honeypot is an effective cybersecurity method, and intruders restrict honeypot traffic. Thus, the information security team does not need to separate attackers from legitimate web traffic  any activity in the bait can be considered malicious, simplifying the analysis (Crowdstrike, 2022). Baits can be a good investment because they interact only with negative actions and do not require high-performance resources to process large volumes of network traffic in search of attacks, which is economically practical (Lutkevich et al., 2021). Moreover, another advantage, baits can also lure internal actors, which ensures the identification of internal threats.

The main argument against this method is that the bait can report the current attack only if the appeal is attacked directly. If the attack affects other systems and the honeypot remains intact, it must rely on other mechanisms to identify the attack (James, 2023). However, it is essential to understand that decoys are one of the components of a comprehensive cybersecurity strategy. Therefore, this argument is weak because it considers Honeypots as an autonomous protection system, but this is only one of the practical tools.

Thus, decoys are an effective method for protecting cyber data. They are easy to analyze, cost-effective, and capable of identifying internal threats. Honeypots may not be the only security system, but they can be a good protection tool.

References

Crowdstrike. (2022). . Web.

James, K. (2023). . Web.

Lutkevich, B., Clark, C., & Cobb, M. (2021). . Web.

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