Detection of Malicious and Low Throughput Data Exfiltration Over the DNS Protocol

@article{Nadler2019DetectionOM,
  title={Detection of Malicious and Low Throughput Data Exfiltration Over the DNS Protocol},
  author={Asaf Nadler and Avi Aminov and Asaf Shabtai},
  journal={ArXiv},
  year={2019},
  volume={abs/1709.08395}
}

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References

SHOWING 1-10 OF 57 REFERENCES

An approach towards anomaly based detection and profiling covert TCP/IP channels

TLDR
This work will explore the approach of combining anomaly based detection and covert channel profiling to be used for detecting a very precise subset of covert storage channels in network protocols, and describe a specialized tool to passively monitor networks for these types of attacks.

Detection of malicious payload distribution channels in DNS

TLDR
A system to analyze the resource record activities of domain names and build DNS zone profiles to detect payload distribution channels and reveal a few previously unreported long-running hidden domains used by the Morto worm for distributing malicious payloads is presented.

Detection of DNS Based Covert Channels

TLDR
This work shows that freely available covert DNS tools have particular traffic signatures that can be detected in order to mitigate data exfiltration and C&C traffic and created a test bed system that uses a covert DNS channel to exfiltrate data from a compromised host.

ProVeX: Detecting Botnets with Encrypted Command and Control Channels

TLDR
The proposed ProVex is a system that automatically derives probabilistic vectorized signatures for fields in the C&C protocol by evaluating byte probabilities in C &C input traces used for training, and can detect all studied malware families, most of which are not detectable with traditional means.

Entropy-based Prediction of Network Protocols in the Forensic Analysis of DNS Tunnels

TLDR
This paper analyzes the internal packet structure of DNS tunneling techniques and characterize the information entropy of different network protocols and their DNS tunneled equivalents and presents a protocol prediction method that uses entropy distribution averaging.

Detection of DNS Tunneling in Mobile Networks Using Machine Learning

TLDR
Two machine learning techniques, namely One Class Support Vector Machine (OCSVM) and K-Means are experimented and the results prove that machineLearning techniques could yield quite efficient detection solutions.

Combating Malicious DNS Tunnel

TLDR
The goal of DNS tunnel is to use DNS as a communication stack between the querier and the responder, which can be used for “command and control”, data exfiltration or tunneling of any internet protocol (IP) traffic.

Data Exfiltration Detection and Prevention: Virtually Distributed POMDPs for Practically Safer Networks

TLDR
This work provides a fast scalable POMDP formulation to address the challenge of detecting data exfiltration over Domain Name System DNS queries, and provides a decision-theoretic technique that sequentially plans to accumulate evidence under uncertainty while taking into account the cost of deploying such sensors.

Behavior-based tracking: Exploiting characteristic patterns in DNS traffic

Detection of Tunnels in PCAP Data by Random Forests

TLDR
This paper describes an approach for detecting the presence of domain name system (DNS) tunnels in network traffic using random forest classifiers to distinguish normal DNS activity from tunneling activity.
...