Abstract
Botnet employs advanced evasion techniques to avoid detection. One of the Botnet evasion techniques is by hiding their command and control communication over an encrypted channel like SSL and TLS. This paper provides a Botnet Analysis and Detection System (BADS) framework for detecting Botnet. The BADS framework has been used as a guideline to devise the methodology, and we divided this methodology into six phases: i. data collection, customization, and conversion, ii. feature extraction and feature selection, iii. Botnet prediction and classification, iv. Botnet detection, v. attack notification, and vi. testing and evaluation. We tend to use the machine learning algorithm for Botnet prediction and classification. We also found several challenges in implementing this work. This research aims to detect Botnet over an encrypted channel with high accuracy, fast detection time, and provides autonomous management to the network manager.
Highlights
Botnet has become a significant concern in the computer industry
With users engaging in daily life surfing to the Internet, there was a high risk of becoming a victim of a Botnet attack
The botnet has developed many capabilities, but most of those capabilities are used for attack purposes, such as performing a DDoS attack, spamming, malware spreading, and large computer compromising
Summary
Botnet has become a significant concern in the computer industry. With users engaging in daily life surfing to the Internet, there was a high risk of becoming a victim of a Botnet attack. According to Nicholson (2015) [7], social media like Pinterest and Twitter, and email applications use SSL/TLS to encrypt all communications These social media have many users, making them a good target for the attacks because of the potential to compromise the massive host. Botnet itself creates a massive impact, and with the implementation of advanced evasion techniques like masquerading in the SSL channel will amplify the impacts These scenarios have shown the severity of encrypted Botnet attack and become an encouraging factor for developing solutions to detect Botnet over an encrypted channel even though there are other network attacks encrypted in SSL/TLS-enabled protocol.
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