Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio‑Cyber attacks

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 perpetrator’s machine in the DNA. Genetic analysis of the sample’s 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

A Novel Web Attack Detection System for Internet of Things via Ensemble Classification

Internet of things (IoT) has become one of the fastestgrowing technologies and has been broadly applied in various fields. IoT networks contain millions of devices with the capability of interacting with each other and providing functionalities that were never available to us before. These IoT networks are designed to provide friendly and intelligent operations through big data analysis of information generated or collected from an abundance of devices in real time. However, the diversity of IoT devices makes the IoT networks environments more complex and more vulnerable to various web attacks compared to traditional computer networks. In this paper, we propose a novel Ensemble Deep Learning based Web Attack Detection System (EDL-WADS) to alleviate the serious issues that IoT networks faces. Specifically, we have designed three deep learning models to first detect web attacks separately. We then use an ensemble classifier to make the final decision according to the results obtained from the three deep learning models. In order to evaluate the proposed WADS, we have performed experiments on a public dataset as well as a realword dataset running in a distributed environment. Experimental results show that the proposed system can detect web attacks accurately with low false positive and negative rates