Spark-Based Network Security Honeypot System: Detailed Performance Analysis

Abstract

In the contemporary world, network security has been of the biggest importance and acute worry in both individual and institutional wisdom, concurrent with the newly emerging technologies. Firewalls, encryption techniques, intrusion detection systems, and honeypots are just a few of the systems and technologies that have been developed to ensure information security. Systems for safeguarding an organizational environment through various defensive strategies are traditionally developed. "The enemy continues on attacking" is a primarily defensive statement. By empowering an organization to take action, Honeypot demonstrates its importance. An institution can discover, gather, address, and absorb new security policy flaws with the aid of honeypots. This methodology allows an organization's security measures to continuously incorporate new threats and penetration methods. This is the main justification of creating, developing, and using a honeypot. It is a resource that is meant to be taken advantage of and compromised. To evaluate the methodology, a spark-based honeypot strategy has been designed, put into practice, and tested in this study. The suggested study strategy has undergone many days of testing on a campus-based network. The designed system's primary function is to behave as a fully resourced computer or vault to draw intruders with the requirement that they not defend against or respond to intrusions. The studies were carried out using a number of parameters that were designed.

Machine learning platform design and application based on spark

“…To validate the platform's performance in massive data scenarios, we used a product recommendation system as an application example for comparative testing [10] . A product recommendation model based on collaborative filtering was constructed in a single-machine environment using a certain Benchmark dataset for training.…”