Using Deep Learning to Detect Digitally Encoded DNA Trigger for Trojan Malware in Bio-Cyber Attacks

TL;DR

This paper demonstrates how deep learning can effectively detect digitally encoded Trojan malware triggers in DNA sequences used in bio-cyber attack scenarios, achieving nearly 100% accuracy even with obfuscation techniques.

Contribution

It introduces a novel deep learning-based method for detecting encoded Trojan triggers in DNA sequences, enhancing bio-cyber security measures.

Findings

Deep learning achieves up to 100% detection accuracy.

Encoded DNA payloads can be synthesized and detected.

Steganography and encryption do not prevent detection.

Abstract

This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrators machine in the DNA. Genetic analysis of the samples DNA will decode the address that is used by the software trojan malware to activate and trigger a remote connection. This approach can open up to multiple perpetrators to create connections to hijack the DNA sequencing pipeline. As a way of hiding the data, the perpetrators can avoid detection by encoding the address to maximise similarity with genuine DNAs, which we showed previously. However, in this paper we show how Deep Learning can be used to successfully detect and identify the trigger encoded data, in order to protect a DNA sequencing pipeline from trojan attacks. The result shows nearly up to 100% accuracy in detection in such a novel Trojan attack scenario even after applying fragmentation encryption and steganography on the encoded trigger data. In addition, feasibility of designing and synthesizing encoded DNA for such Trojan payloads is validated by a wet lab experiment.