# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 1 Introduction
### Banking Malware Communication (C&C) Architecture  
Zeus, Zeus Panda, and Ramnit are all sophisticated malware families targeting financial institutions, employing similar techniques like phishing-based propagation, Command and Control (C&C) communication, a modular architecture, and advanced evasion methods such as polymorphism and MITB attacks to steal banking credentials and personal data. While Zeus is the foundational banking Trojan known for its widespread botnet use, Zeus Panda enhances targeting with tailored regional attacks and advanced MITB capabilities. Ramnit, evolving from a worm, extends its reach with broader infection methods, including USB and executable file propagation, exhibiting a stronger persistence through polymorphic techniques and rootkit functionality. Despite their similarities, each malware family has unique traits that make it distinct in its evolution and attack strategies. Banking malware use C&C communication channels to communicate between the infected device and malicious entity, and the focus of this study is to identify these communication patterns. Once a device becomes infected, outgoing communication is hard to detect, as it can very easily obfuscate itself with the normal traffic flows of the network, thus making the malware hard to detect. Banking malware variants are made up of bots that communicate with the C&C server and, initially, these communication channels are centralized, because each bot will communicate with the C&C server directly. These bots are also controlled directly by the C&C server, and this architecture uses a push model for communication purposes. Instructions and malicious commands are all pushed from the C&C server to the bot [27]. The C&C channel always remains active in the 'connect mode', as the bot needs to be ready to receive commands [27]. This communication uses the HTTP/HTTPS protocol and can also use other protocols such as DNS tunneling, which makes the malware more difficult to detect. The bots also reach out to the C&C server at predefined intervals, and this ensures that the C&C server maintains communication with these bots. The centralized C&C architecture has limitations, and the key one is that, if the C&C server becomes inactive, all the bots fail, and the bot network becomes inactive. The centralized architecture evolved into a peer to peer (P2P) architecture in which the bots build a decentralized network of bots. This means that the bots can receive commands from other bots, and there is no longer a reliance on a centralized C&C server. This P2P network is more difficult to detect and take down [28], and makes the botnet more resilient. If a bot in a botnet loses communication, it can automatically try other bots or domains to resume communication. Also, this bot failure does not affect the botnet, as the botnet remains active and other bots are still able to communicate with each other. There are some weaknesses inherent in this type of network, and one of them is that updates can take longer to propagate across the network, as these updates have to be routed through many other bots. Also, stolen data that need to be routed back to the malicious attacker can also face similar challenges. Machine learning (ML) is critical for detecting the C&C communicating traffic used by malware due to its ability to overcome the limitations of traditional detection methods. Unlike signature-based approaches, which struggle in detecting zero-day malware, polymorphic threats, or encrypted traffic, ML focuses on patterns, behaviors, and anomalies in network communications. By analyzing network features such as packet sizes, traffic flow, and timing intervals, ML models can identify malicious communications even when the data are obfuscated. Furthermore, machine learning enables real-time detection capabilities and can efficiently scale to handle vast amounts of network traffic. It can also adapt to evolving threats, as ML models can be retrained on new data.