Kernel-level Tracing for Detecting Stegomalware and Covert Channels in Linux Environments

Modern malware is becoming hard to spot since attackers are increasingly adopting new techniques to elude signature- and rule-based detection mechanisms. Among the others, steganography and information hiding can be used to bypass security frameworks searching for suspicious communications between processes or exfiltration attempts through covert channels. Since the array of potential carriers is very large (e.g., information can be hidden in hardware resources, various multimedia files or network flows), detecting this class of threats is a scarcely generalizable process and gathering multiple behavioral information is time-consuming, lacks scalability, and could lead to performance degradation. In this paper, we leverage the extended Berkeley Packet Filter (eBPF), which is a recent code augmentation feature provided by the Linux kernel, for programmatically tracing and monitoring the behavior of software processes in a very efficient way. To prove the flexibility of the approach, we investigate two realistic use cases implementing different attack mechanisms, i.e., two processes colluding via the alteration of the file system and hidden network communication attempts nested within IPv6 traffic flows. Our results show that even simple eBPF programs can provide useful data for the detection of anomalies, with a minimal overhead. Furthermore, the flexibility to develop and run such programs allows to extract relevant features that could be used for the creation of datasets for feeding security frameworks exploiting AI.