DCA for Bot Detection

TL;DR

This paper applies the biologically inspired Dendritic Cell Algorithm (DCA) to detect individual bots on compromised hosts, demonstrating its effectiveness in identifying malicious activities like keylogging and packet flooding.

Contribution

It introduces the use of the DCA for bot detection on host machines, showcasing its ability to identify malicious behavior without false positives.

Findings

DCA successfully detects a single bot on a compromised host.

The algorithm correlates behavioral attributes like keylogging and flooding.

DCA effectively distinguishes malicious activity from normal processes.

Abstract

Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a 'bot' - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the 'botmaster of a botnet'. In this work, we use the biologically inspired Dendritic Cell Algorithm (DCA) to detect the existence of a single bot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single bot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.