An Empirical Assessment of Botnets and Detection Methods

A botnet is a group of devices which are controlled by the Attacker and used for fraud, scam and cyber-attacks. The attacker is also known as Bot-herder. The Bots are used to conduct many activities varies from stealing user credentials, sending spam, stealing data, ransom ware, fraudulently clicking on ads or distributed denial-of-service (DDoS) attacks. The traffic made up by bots is increasing day by day, now nearly a quarter of the whole traffic. Detecting them becomes an uphill task as the time passes. Bot developers are adopting new technologies at a rapid rate in an effort to avoid bot detection. While some bot problems are industry-specific, researchers found out that bot problems run across all industries. The top five industries which are targeted with bad bot traffic are the financial, education, IT and services industries, marketplaces, and government. This assessment paper briefs the different botnet detection methods that exist at present. The main aim of this paper is to give the insight knowledge of various bots and their implications on the present cyber world and also shedding some light on the current methods using to detect them